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Fix imports
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@hanouticelina
hanouticelina committed Oct 25, 2024
1 parent 9365bd6 commit 5e961fe
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25 changes: 0 additions & 25 deletions src/huggingface_hub/inference/_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1083,9 +1083,6 @@ def fill_mask(
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocabulary. If the provided targets are not in the model vocab, they will be tokenized and the first
resulting token will be used (with a warning, and that might be slower).
in the whole vocabulary. If the provided targets are not in the model vocab, they will be
tokenized and the first resulting token will be used (with a warning, and that might be
slower).
top_k (`int`, *optional*):
When passed, overrides the number of predictions to return.
Returns:
Expand Down Expand Up @@ -2381,11 +2378,9 @@ def text_to_image(
num_inference_steps (`int`, *optional*):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
expense of slower inference.
guidance_scale (`float`, *optional*):
A higher guidance scale value encourages the model to generate images closely linked to the text
prompt, but values too high may cause saturation and other artifacts.
usually at the expense of lower image quality.
model (`str`, *optional*):
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. If not provided, the default recommended text-to-image model will be used.
Expand Down Expand Up @@ -2479,9 +2474,6 @@ def text_to_speech(
epsilon_cutoff will be sampled. In the paper, suggested values range from 3e-4 to 9e-4, depending on
the size of the model. See [Truncation Sampling as Language Model
Desmoothing](https://hf.co/papers/2210.15191) for more details.
greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
eta_cutoff (`float`, *optional*):
Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to float strictly
between 0 and 1, a token is only considered if it is greater than either eta_cutoff or sqrt(eta_cutoff)
Expand All @@ -2497,9 +2489,6 @@ def text_to_speech(
probability, scaled by sqrt(eta_cutoff). In the paper, suggested values range from 3e-4 to 2e-3,
depending on the size of the model. See [Truncation Sampling as Language Model
Desmoothing](https://hf.co/papers/2210.15191) for more details.
the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
for more details.
max_length (`int`, *optional*):
The maximum length (in tokens) of the generated text, including the input.
max_new_tokens (`int`, *optional*):
Expand All @@ -2511,30 +2500,23 @@ def text_to_speech(
num_beam_groups (`int`, *optional*):
Number of groups to divide num_beams into in order to ensure diversity among different groups of beams.
See [this paper](https://hf.co/papers/1610.02424) for more details.
groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
num_beams (`int`, *optional*):
Number of beams to use for beam search.
penalty_alpha (`float`, *optional*):
The value balances the model confidence and the degeneration penalty in contrastive search decoding.
search decoding.
temperature (`float`, *optional*):
The value used to modulate the next token probabilities.
top_k (`int`, *optional*):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`, *optional*):
If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to
top_p or higher are kept for generation.
that add up to top_p or higher are kept for generation.
typical_p (`float`, *optional*):
Local typicality measures how similar the conditional probability of predicting a target token next is
to the expected conditional probability of predicting a random token next, given the partial text
already generated. If set to float < 1, the smallest set of the most locally typical tokens with
probabilities that add up to typical_p or higher are kept for generation. See [this
paper](https://hf.co/papers/2202.00666) for more details.
to the expected conditional probability of predicting a random token next, given the partial text
already generated. If set to float < 1, the smallest set of the most locally typical tokens with
probabilities that add up to typical_p or higher are kept for generation. See [this
paper](https://hf.co/papers/2202.00666) for more details.
use_cache (`bool`, *optional*):
Whether the model should use the past last key/values attentions to speed up decoding
Expand Down Expand Up @@ -2756,8 +2738,6 @@ def visual_question_answering(
top_k (`int`, *optional*):
The number of answers to return (will be chosen by order of likelihood). Note that we return less than
topk answers if there are not enough options available within the context.
return less than topk answers if there are not enough options available within the
context.
Returns:
`List[VisualQuestionAnsweringOutputElement]`: a list of [`VisualQuestionAnsweringOutputElement`] items containing the predicted label and associated probability.
Expand Down Expand Up @@ -2808,13 +2788,9 @@ def zero_shot_classification(
Whether multiple candidate labels can be true. If false, the scores are normalized such that the sum of
the label likelihoods for each sequence is 1. If true, the labels are considered independent and
probabilities are normalized for each candidate.
If False, the labels are considered mutually exclusive and the probability over all labels always sums to 1. Defaults to False.
hypothesis_template (`str`, *optional*):
The sentence used in conjunction with candidateLabels to attempt the text classification by replacing
the placeholder with the candidate labels.
Zero-shot classifiers are based on NLI models, which evaluate if a hypothesis is entailed in another text or not.
For example, with hypothesis_template="This text is about {}." and labels=["economics", "politics"], the system internally creates the two hypotheses "This text is about economics." and "This text is about politics.".
The model then evaluates for both hypotheses if they are entailed in the provided `text` or not.
model (`str`, *optional*):
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. If not provided, the default recommended zero-shot classification model will be used.
Expand Down Expand Up @@ -2914,7 +2890,6 @@ def zero_shot_image_classification(
hypothesis_template (`str`, *optional*):
The sentence used in conjunction with candidateLabels to attempt the text classification by replacing
the placeholder with the candidate labels.
placeholder with the candidate labels.
Returns:
`List[ZeroShotImageClassificationOutputElement]`: List of [`ZeroShotImageClassificationOutputElement`] items containing the predicted labels and their confidence.
Expand Down
33 changes: 1 addition & 32 deletions src/huggingface_hub/inference/_generated/_async_client.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1127,9 +1127,6 @@ async def fill_mask(
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocabulary. If the provided targets are not in the model vocab, they will be tokenized and the first
resulting token will be used (with a warning, and that might be slower).
in the whole vocabulary. If the provided targets are not in the model vocab, they will be
tokenized and the first resulting token will be used (with a warning, and that might be
slower).
top_k (`int`, *optional*):
When passed, overrides the number of predictions to return.
Returns:
Expand Down Expand Up @@ -1543,28 +1540,22 @@ async def question_answering(
align_to_words (`bool`, *optional*):
Attempts to align the answer to real words. Improves quality on space separated languages. Might hurt
on non-space-separated languages (like Japanese or Chinese)
languages. Might hurt on non-space-separated languages (like Japanese or Chinese).
doc_stride (`int`, *optional*):
If the context is too long to fit with the question for the model, it will be split in several chunks
with some overlap. This argument controls the size of that overlap.
several chunks with some overlap. This argument controls the size of that overlap.
handle_impossible_answer (`bool`, *optional*):
Whether to accept impossible as an answer.
max_answer_len (`int`, *optional*):
The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
considered).
max_question_len (`int`, *optional*):
The maximum length of the question after tokenization. It will be truncated if needed.
max_seq_len (`int`, *optional*):
The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
model. The context will be split in several chunks (using docStride as overlap) if needed.
passed to the model. The context will be split in several chunks (using docStride as
overlap) if needed.
top_k (`int`, *optional*):
The number of answers to return (will be chosen by order of likelihood). Note that we return less than
topk answers if there are not enough options available within the context.
return less than topk answers if there are not enough options available within the
context.
Returns:
Union[`QuestionAnsweringOutputElement`, List[`QuestionAnsweringOutputElement`]]:
When top_k is 1 or not provided, it returns a single `QuestionAnsweringOutputElement`.
Expand Down Expand Up @@ -2451,11 +2442,9 @@ async def text_to_image(
num_inference_steps (`int`, *optional*):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
expense of slower inference.
guidance_scale (`float`, *optional*):
A higher guidance scale value encourages the model to generate images closely linked to the text
prompt, but values too high may cause saturation and other artifacts.
usually at the expense of lower image quality.
model (`str`, *optional*):
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. If not provided, the default recommended text-to-image model will be used.
Expand Down Expand Up @@ -2550,9 +2539,6 @@ async def text_to_speech(
epsilon_cutoff will be sampled. In the paper, suggested values range from 3e-4 to 9e-4, depending on
the size of the model. See [Truncation Sampling as Language Model
Desmoothing](https://hf.co/papers/2210.15191) for more details.
greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
eta_cutoff (`float`, *optional*):
Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to float strictly
between 0 and 1, a token is only considered if it is greater than either eta_cutoff or sqrt(eta_cutoff)
Expand All @@ -2568,9 +2554,6 @@ async def text_to_speech(
probability, scaled by sqrt(eta_cutoff). In the paper, suggested values range from 3e-4 to 2e-3,
depending on the size of the model. See [Truncation Sampling as Language Model
Desmoothing](https://hf.co/papers/2210.15191) for more details.
the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
for more details.
max_length (`int`, *optional*):
The maximum length (in tokens) of the generated text, including the input.
max_new_tokens (`int`, *optional*):
Expand All @@ -2582,30 +2565,23 @@ async def text_to_speech(
num_beam_groups (`int`, *optional*):
Number of groups to divide num_beams into in order to ensure diversity among different groups of beams.
See [this paper](https://hf.co/papers/1610.02424) for more details.
groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
num_beams (`int`, *optional*):
Number of beams to use for beam search.
penalty_alpha (`float`, *optional*):
The value balances the model confidence and the degeneration penalty in contrastive search decoding.
search decoding.
temperature (`float`, *optional*):
The value used to modulate the next token probabilities.
top_k (`int`, *optional*):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`, *optional*):
If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to
top_p or higher are kept for generation.
that add up to top_p or higher are kept for generation.
typical_p (`float`, *optional*):
Local typicality measures how similar the conditional probability of predicting a target token next is
to the expected conditional probability of predicting a random token next, given the partial text
already generated. If set to float < 1, the smallest set of the most locally typical tokens with
probabilities that add up to typical_p or higher are kept for generation. See [this
paper](https://hf.co/papers/2202.00666) for more details.
to the expected conditional probability of predicting a random token next, given the partial text
already generated. If set to float < 1, the smallest set of the most locally typical tokens with
probabilities that add up to typical_p or higher are kept for generation. See [this
paper](https://hf.co/papers/2202.00666) for more details.
use_cache (`bool`, *optional*):
Whether the model should use the past last key/values attentions to speed up decoding
Expand Down Expand Up @@ -2830,8 +2806,6 @@ async def visual_question_answering(
top_k (`int`, *optional*):
The number of answers to return (will be chosen by order of likelihood). Note that we return less than
topk answers if there are not enough options available within the context.
return less than topk answers if there are not enough options available within the
context.
Returns:
`List[VisualQuestionAnsweringOutputElement]`: a list of [`VisualQuestionAnsweringOutputElement`] items containing the predicted label and associated probability.
Expand Down Expand Up @@ -2883,13 +2857,9 @@ async def zero_shot_classification(
Whether multiple candidate labels can be true. If false, the scores are normalized such that the sum of
the label likelihoods for each sequence is 1. If true, the labels are considered independent and
probabilities are normalized for each candidate.
If False, the labels are considered mutually exclusive and the probability over all labels always sums to 1. Defaults to False.
hypothesis_template (`str`, *optional*):
The sentence used in conjunction with candidateLabels to attempt the text classification by replacing
the placeholder with the candidate labels.
Zero-shot classifiers are based on NLI models, which evaluate if a hypothesis is entailed in another text or not.
For example, with hypothesis_template="This text is about {}." and labels=["economics", "politics"], the system internally creates the two hypotheses "This text is about economics." and "This text is about politics.".
The model then evaluates for both hypotheses if they are entailed in the provided `text` or not.
model (`str`, *optional*):
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. If not provided, the default recommended zero-shot classification model will be used.
Expand Down Expand Up @@ -2991,7 +2961,6 @@ async def zero_shot_image_classification(
hypothesis_template (`str`, *optional*):
The sentence used in conjunction with candidateLabels to attempt the text classification by replacing
the placeholder with the candidate labels.
placeholder with the candidate labels.
Returns:
`List[ZeroShotImageClassificationOutputElement]`: List of [`ZeroShotImageClassificationOutputElement`] items containing the predicted labels and their confidence.
Expand Down
4 changes: 1 addition & 3 deletions utils/check_task_parameters.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -813,9 +813,7 @@ def update_inference_client(update: bool):

# Construct a mapping between method names and their parameters dataclass names
method_params = {}
for method_name, _ in [
("question_answering", None)
]: # inspect.getmembers(InferenceClient, predicate=inspect.isfunction)
for method_name, _ in inspect.getmembers(InferenceClient, predicate=inspect.isfunction):
if method_name.startswith("_") or method_name not in tasks:
continue
parameter_type_name = _get_parameter_type_name(method_name)
Expand Down

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