

"""simple docstring"""





import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_a       =							'\\n\n'

_a       =							'\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

_a       =							'\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION   )
class 					_lowerCAmelCase  (	datasets.Metric   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Optional[Any]	):
    return datasets.MetricInfo(
        description=_DESCRIPTION,					citation=_CITATION,					inputs_description=_KWARGS_DESCRIPTION,					features=datasets.Features(
            {
                "input_texts": datasets.Value("string"	),
            }	),					reference_urls=["https://huggingface.co/docs/transformers/perplexity"],					)



 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			int = 1_6,					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Dict=None	):
    if device is not None:
       assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
       if device == "gpu":
          __lowercase								=						"cuda"
    else:
       __lowercase								=						"cuda" if torch.cuda.is_available() else "cpu"

    __lowercase								=						AutoModelForCausalLM.from_pretrained(UpperCAmelCase__	)
    __lowercase								=						model.to(UpperCAmelCase__	)

    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__	)

    # if batch_size > 1 (which generally leads to padding being required), and
    # if there is not an already assigned pad_token, assign an existing
    # special token to also be the padding token
    if tokenizer.pad_token is None and batch_size > 1:
       __lowercase								=						list(tokenizer.special_tokens_map_extended.values()	)
       # check that the model already has at least one special token defined
       assert (
           len(UpperCAmelCase__	) > 0
       ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
       # assign one of the special tokens to also be the pad token
       tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]}	)

    if add_start_token:
       # leave room for <BOS> token to be added:
       assert (
           tokenizer.bos_token is not None
       ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
       __lowercase								=						model.config.max_length - 1
    else:
       __lowercase								=						model.config.max_length

    __lowercase								=						tokenizer(
        UpperCAmelCase__,					add_special_tokens=UpperCAmelCase__,					padding=UpperCAmelCase__,					truncation=UpperCAmelCase__,					max_length=UpperCAmelCase__,					return_tensors="pt",					return_attention_mask=UpperCAmelCase__,					).to(UpperCAmelCase__	)

    __lowercase								=						encodings["input_ids"]
    __lowercase								=						encodings["attention_mask"]

    # check that each input is long enough:
    if add_start_token:
       assert torch.all(torch.ge(attn_masks.sum(1	),					1	)	), "Each input text must be at least one token long."
    else:
       assert torch.all(
           torch.ge(attn_masks.sum(1	),					2	)	), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

    __lowercase								=						[]
    __lowercase								=						CrossEntropyLoss(reduction="none"	)

    for start_index in logging.tqdm(range(0,					len(UpperCAmelCase__	),					UpperCAmelCase__	)	):
       __lowercase								=						min(start_index + batch_size,					len(UpperCAmelCase__	)	)
       __lowercase								=						encoded_texts[start_index:end_index]
       __lowercase								=						attn_masks[start_index:end_index]

       if add_start_token:
          __lowercase								=						torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0	)	).to(UpperCAmelCase__	)
          __lowercase								=						torch.cat([bos_tokens_tensor, encoded_batch],					dim=1	)
          __lowercase								=						torch.cat(
              [torch.ones(bos_tokens_tensor.size(),					dtype=torch.intaa	).to(UpperCAmelCase__	), attn_mask],					dim=1	)

       __lowercase								=						encoded_batch

       with torch.no_grad():
          __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__	).logits

       __lowercase								=						out_logits[..., :-1, :].contiguous()
       __lowercase								=						labels[..., 1:].contiguous()
       __lowercase								=						attn_mask[..., 1:].contiguous()

       __lowercase								=						torch.expa(
           (loss_fct(shift_logits.transpose(1,					2	),					UpperCAmelCase__	) * shift_attention_mask_batch).sum(1	)
           / shift_attention_mask_batch.sum(1	)	)

       ppls += perplexity_batch.tolist()

    return {"perplexities": ppls, "mean_perplexity": np.mean(UpperCAmelCase__	)}







"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	"openai/whisper-base"
 __UpperCAmelCase	:					Union[str, Any]   		=	(
     "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
     "transcribed text."
 )
 __UpperCAmelCase	:					List[str]   		=	"transcriber"
 __UpperCAmelCase	:					Optional[Any]   		=	WhisperProcessor
 __UpperCAmelCase	:					str   		=	WhisperForConditionalGeneration

 __UpperCAmelCase	:					List[str]   		=	["audio"]
 __UpperCAmelCase	:					Tuple   		=	["text"]


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			int	):
    return self.pre_processor(UpperCAmelCase__,					return_tensors="pt"	).input_features


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any]	):
    return self.model.generate(inputs=UpperCAmelCase__	)



 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[int]	):
    return self.pre_processor.batch_decode(UpperCAmelCase__,					skip_special_tokens=UpperCAmelCase__	)[0]




