


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

import datasets
from datasets import logging


snake_case_						:			Dict													=							'''\

'''

snake_case_						:			Union[str, Any]													=							'''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
'''

snake_case_						:			Tuple													=							'''
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to \'cuda\' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        78.22
        >>> print(round(results["perplexities"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = datasets.load_dataset("wikitext",
        ...                                     "wikitext-2-raw-v1",
        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!=\'\']
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        60.35
        >>> print(round(results["perplexities"][0], 2))
        81.12
'''

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




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     int:


										"""simple docstring"""





										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 							__UpperCamelCase				(       self					:      Dict				,    _a					:      Dict				,    _a					:      Optional[Any]				,    _a					:      int = 16				,    _a					:      bool = True				,    _a					:      List[Any]=None  )							->     Dict:


										"""simple docstring"""





										if device is not None:
														assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
														if device == "gpu":
																		_SCREAMING_SNAKE_CASE			  ='''cuda'''
										else:
														_SCREAMING_SNAKE_CASE			  ='''cuda''' if torch.cuda.is_available() else '''cpu'''

										_SCREAMING_SNAKE_CASE			  =AutoModelForCausalLM.from_pretrained(_a  )
										_SCREAMING_SNAKE_CASE			  =model.to(_a  )

										_SCREAMING_SNAKE_CASE			  =AutoTokenizer.from_pretrained(_a  )

										# 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:
														_SCREAMING_SNAKE_CASE			  =list(tokenizer.special_tokens_map_extended.values()  )
														# check that the model already has at least one special token defined
														assert (
														    len(_a  ) > 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"
														_SCREAMING_SNAKE_CASE			  =model.config.max_length - 1
										else:
														_SCREAMING_SNAKE_CASE			  =model.config.max_length

										_SCREAMING_SNAKE_CASE			  =tokenizer(
										    _a				,    add_special_tokens=_a				,    padding=_a				,    truncation=_a				,    max_length=_a				,    return_tensors='''pt'''				,    return_attention_mask=_a				,    ).to(_a  )

										_SCREAMING_SNAKE_CASE			  =encodings['''input_ids''']
										_SCREAMING_SNAKE_CASE			  =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."

										_SCREAMING_SNAKE_CASE			  =[]
										_SCREAMING_SNAKE_CASE			  =CrossEntropyLoss(reduction='''none'''  )

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

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

														_SCREAMING_SNAKE_CASE			  =encoded_batch

														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE			  =model(_a				,    attention_mask=_a  ).logits

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

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

														ppls += perplexity_batch.tolist()

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



import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
							from cookiecutter.main import cookiecutter

							snake_case_						:			str													=							True
except ImportError:
							snake_case_						:			Optional[Any]													=							False

snake_case_						:			int													=							logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						lowerCamelCase(   a__):
				return AddNewModelCommand(args.testing	,args.testing_file	,path=args.path)


class 						A__							(		UpperCamelCase__  ):




						@staticmethod
						def 							__UpperCamelCase				(       _a					:      ArgumentParser  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =parser.add_parser('''add-new-model'''  )
										add_new_model_parser.add_argument('''--testing'''				,    action='''store_true'''				,    help='''If in testing mode.'''  )
										add_new_model_parser.add_argument('''--testing_file'''				,    type=_a				,    help='''Configuration file on which to run.'''  )
										add_new_model_parser.add_argument(
										    '''--path'''				,    type=_a				,    help='''Path to cookiecutter. Should only be used for testing purposes.'''  )
										add_new_model_parser.set_defaults(func=_a  )




						def __init__(       self					:      List[str]				,    _a					:      bool				,    _a					:      str				,    _a					:      Any=None				,    *_a					:      Union[str, Any]  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =testing
										_SCREAMING_SNAKE_CASE			  =testing_file
										_SCREAMING_SNAKE_CASE			  =path




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     int:


										"""simple docstring"""





										warnings.warn(
										    '''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
										    '''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
										    '''checks, you should use `transformers-cli add-new-model-like` instead.'''  )
										if not _has_cookiecutter:
														raise ImportError(
														    '''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
														    '''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'''  )
										# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
										_SCREAMING_SNAKE_CASE			  =[directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:22]]
										if len(_a  ) > 0:
														raise ValueError(
														    '''Several directories starting with `cookiecutter-template-` in current working directory. '''
														    '''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
														    '''change your working directory.'''  )

										_SCREAMING_SNAKE_CASE			  =(
										    Path(_a  ).parent.parent.parent.parent if self._path is None else Path(self._path  ).parent.parent
										)
										_SCREAMING_SNAKE_CASE			  =path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

										# Execute cookiecutter
										if not self._testing:
														cookiecutter(str(_a  )  )
										else:
														with open(self._testing_file				,    '''r'''  ) as configuration_file:
																		_SCREAMING_SNAKE_CASE			  =json.load(_a  )

														cookiecutter(
														    str(path_to_cookiecutter if self._path is None else self._path  )				,    no_input=_a				,    extra_context=_a				,    )

										_SCREAMING_SNAKE_CASE			  =[directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0]

										# Retrieve configuration
										with open(directory + '''/configuration.json'''				,    '''r'''  ) as configuration_file:
														_SCREAMING_SNAKE_CASE			  =json.load(_a  )

										_SCREAMING_SNAKE_CASE			  =configuration['''lowercase_modelname''']
										_SCREAMING_SNAKE_CASE			  =configuration['''generate_tensorflow_pytorch_and_flax''']
										os.remove(f"{directory}/configuration.json"  )

										_SCREAMING_SNAKE_CASE			  ='''PyTorch''' in generate_tensorflow_pytorch_and_flax
										_SCREAMING_SNAKE_CASE			  ='''TensorFlow''' in generate_tensorflow_pytorch_and_flax
										_SCREAMING_SNAKE_CASE			  ='''Flax''' in generate_tensorflow_pytorch_and_flax

										_SCREAMING_SNAKE_CASE			  =f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
										os.makedirs(_a				,    exist_ok=_a  )
										os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}"				,    exist_ok=_a  )

										# Tests require submodules as they have parent imports
										with open(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py"				,    '''w'''  ):
														pass

										shutil.move(
										    f"{directory}/__init__.py"				,    f"{model_dir}/__init__.py"				,    )
										shutil.move(
										    f"{directory}/configuration_{lowercase_model_name}.py"				,    f"{model_dir}/configuration_{lowercase_model_name}.py"				,    )

										def remove_copy_lines(_a					:      int  ):
														with open(_a				,    '''r'''  ) as f:
																		_SCREAMING_SNAKE_CASE			  =f.readlines()
														with open(_a				,    '''w'''  ) as f:
																		for line in lines:
																						if "# Copied from transformers." not in line:
																										f.write(_a  )

										if output_pytorch:
														if not self._testing:
																		remove_copy_lines(f"{directory}/modeling_{lowercase_model_name}.py"  )

														shutil.move(
														    f"{directory}/modeling_{lowercase_model_name}.py"				,    f"{model_dir}/modeling_{lowercase_model_name}.py"				,    )

														shutil.move(
														    f"{directory}/test_modeling_{lowercase_model_name}.py"				,    f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py"				,    )
										else:
														os.remove(f"{directory}/modeling_{lowercase_model_name}.py"  )
														os.remove(f"{directory}/test_modeling_{lowercase_model_name}.py"  )

										if output_tensorflow:
														if not self._testing:
																		remove_copy_lines(f"{directory}/modeling_tf_{lowercase_model_name}.py"  )

														shutil.move(
														    f"{directory}/modeling_tf_{lowercase_model_name}.py"				,    f"{model_dir}/modeling_tf_{lowercase_model_name}.py"				,    )

														shutil.move(
														    f"{directory}/test_modeling_tf_{lowercase_model_name}.py"				,    f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py"				,    )
										else:
														os.remove(f"{directory}/modeling_tf_{lowercase_model_name}.py"  )
														os.remove(f"{directory}/test_modeling_tf_{lowercase_model_name}.py"  )

										if output_flax:
														if not self._testing:
																		remove_copy_lines(f"{directory}/modeling_flax_{lowercase_model_name}.py"  )

														shutil.move(
														    f"{directory}/modeling_flax_{lowercase_model_name}.py"				,    f"{model_dir}/modeling_flax_{lowercase_model_name}.py"				,    )

														shutil.move(
														    f"{directory}/test_modeling_flax_{lowercase_model_name}.py"				,    f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py"				,    )
										else:
														os.remove(f"{directory}/modeling_flax_{lowercase_model_name}.py"  )
														os.remove(f"{directory}/test_modeling_flax_{lowercase_model_name}.py"  )

										shutil.move(
										    f"{directory}/{lowercase_model_name}.md"				,    f"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md"				,    )

										shutil.move(
										    f"{directory}/tokenization_{lowercase_model_name}.py"				,    f"{model_dir}/tokenization_{lowercase_model_name}.py"				,    )

										shutil.move(
										    f"{directory}/tokenization_fast_{lowercase_model_name}.py"				,    f"{model_dir}/tokenization_{lowercase_model_name}_fast.py"				,    )

										from os import fdopen, remove
										from shutil import copymode, move
										from tempfile import mkstemp

										def replace(_a					:      str				,    _a					:      str				,    _a					:      List[str]  ):
														# Create temp file
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =mkstemp()
														_SCREAMING_SNAKE_CASE			  =False
														with fdopen(_a				,    '''w'''  ) as new_file:
																		with open(_a  ) as old_file:
																						for line in old_file:
																										new_file.write(_a  )
																										if line_to_copy_below in line:
																														_SCREAMING_SNAKE_CASE			  =True
																														for line_to_copy in lines_to_copy:
																																		new_file.write(_a  )

														if not line_found:
																		raise ValueError(f"Line {line_to_copy_below} was not found in file."  )

														# Copy the file permissions from the old file to the new file
														copymode(_a				,    _a  )
														# Remove original file
														remove(_a  )
														# Move new file
														move(_a				,    _a  )

										def skip_units(_a					:      str  ):
														return (
														    ("generating PyTorch" in line and not output_pytorch)
														    or ("generating TensorFlow" in line and not output_tensorflow)
														    or ("generating Flax" in line and not output_flax)
														)

										def replace_in_files(_a					:      Any  ):
														with open(_a  ) as datafile:
																		_SCREAMING_SNAKE_CASE			  =[]
																		_SCREAMING_SNAKE_CASE			  =False
																		_SCREAMING_SNAKE_CASE			  =False
																		for line in datafile:
																						if "# To replace in: " in line and "##" not in line:
																										_SCREAMING_SNAKE_CASE			  =line.split('''"'''  )[1]
																										_SCREAMING_SNAKE_CASE			  =skip_units(_a  )
																						elif "# Below: " in line and "##" not in line:
																										_SCREAMING_SNAKE_CASE			  =line.split('''"'''  )[1]
																										_SCREAMING_SNAKE_CASE			  =skip_units(_a  )
																						elif "# End." in line and "##" not in line:
																										if not skip_file and not skip_snippet:
																														replace(_a				,    _a				,    _a  )

																										_SCREAMING_SNAKE_CASE			  =[]
																						elif "# Replace with" in line and "##" not in line:
																										_SCREAMING_SNAKE_CASE			  =[]
																						elif "##" not in line:
																										lines_to_copy.append(_a  )

														remove(_a  )

										replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py"  )
										os.rmdir(_a  )