

from jiwer import compute_measures

import datasets


A									=  '\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

A									=  '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A									=  '\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'


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






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return datasets.MetricInfo(
				    description=_DESCRIPTION			,					citation=_CITATION			,					inputs_description=_KWARGS_DESCRIPTION			,					features=datasets.Features(
				        {
				            'predictions': datasets.Value('string'			,					id='sequence'				),
				            'references': datasets.Value('string'			,					id='sequence'				),
				        }				)			,					codebase_urls=['https://github.com/jitsi/jiwer/']			,					reference_urls=[
				        'https://en.wikipedia.org/wiki/Word_error_rate',
				    ]			,					)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase=False				):

				"""simple docstring"""


				if concatenate_texts:
					return compute_measures(__UpperCamelCase			,					__UpperCamelCase				)["wer"]
				else:
					snake_case_   		=			0
					snake_case_   		=			0
					for prediction, reference in zip(__UpperCamelCase			,					__UpperCamelCase				):
						snake_case_   		=			compute_measures(__UpperCamelCase			,					__UpperCamelCase				)
						incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
						total += measures["substitutions"] + measures["deletions"] + measures["hits"]
					return incorrect / total



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


A									=  logging.get_logger(__name__)

A									=  {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""gpt_neo"""
			__A    	=	["""past_key_values"""]
			__A    	=	{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


			def __init__(     self			,					__UpperCamelCase=5_02_57			,					__UpperCamelCase=20_48			,					__UpperCamelCase=20_48			,					__UpperCamelCase=24			,					__UpperCamelCase=[[["global", "local"], 12]]			,					__UpperCamelCase=16			,					__UpperCamelCase=None			,					__UpperCamelCase=2_56			,					__UpperCamelCase="gelu_new"			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.1			,					__UpperCamelCase=1E-5			,					__UpperCamelCase=0.02			,					__UpperCamelCase=True			,					__UpperCamelCase=5_02_56			,					__UpperCamelCase=5_02_56			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				snake_case_   		=			vocab_size
				snake_case_   		=			max_position_embeddings
				snake_case_   		=			hidden_size
				snake_case_   		=			num_layers
				snake_case_   		=			num_heads
				snake_case_   		=			intermediate_size
				snake_case_   		=			window_size
				snake_case_   		=			activation_function
				snake_case_   		=			resid_dropout
				snake_case_   		=			embed_dropout
				snake_case_   		=			attention_dropout
				snake_case_   		=			classifier_dropout
				snake_case_   		=			layer_norm_epsilon
				snake_case_   		=			initializer_range
				snake_case_   		=			use_cache

				snake_case_   		=			bos_token_id
				snake_case_   		=			eos_token_id

				snake_case_   		=			attention_types
				snake_case_   		=			self.expand_attention_types_params(__UpperCamelCase				)

				if len(self.attention_layers				) != self.num_layers:
					raise ValueError(
					    'Configuration for convolutional module is incorrect. '
					    'It is required that `len(config.attention_layers)` == `config.num_layers` '
					    f"""but is `len(config.attention_layers) = {len(self.attention_layers				)}`, """
					    f"""`config.num_layers = {self.num_layers}`. """
					    '`config.attention_layers` is prepared using `config.attention_types`. '
					    'Please verify the value of `config.attention_types` argument.'				)

				super().__init__(bos_token_id=__UpperCamelCase			,					eos_token_id=__UpperCamelCase			,					**__UpperCamelCase				)







			@staticmethod
			def       __lowerCAmelCase			(     __UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			[]
				for item in attention_types:
					for _ in range(item[1]				):
						attentions.extend(item[0]				)
				return attentions







def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	import torch

	snake_case_   		=			input.size()
	snake_case_   		=			len(lowercase__						)
	snake_case_   		=			shape[dimension]

	snake_case_   		=			torch.arange(0      ,			lowercase__      ,			lowercase__						)
	snake_case_   		=			torch.div(sizedim - size      ,			lowercase__      ,			rounding_mode='floor'						) + 1
	snake_case_   		=			torch.arange(lowercase__						) + low_indices[:min_length][:, None]

	snake_case_   		=			[slice(lowercase__						)] * rank
	snake_case_   		=			indices
	snake_case_   		=			input[s]

	snake_case_   		=			list(range(0      ,			rank + 1						)						)
	perm.append(perm.pop(dimension + 1						)						)

	return sliced.permute(lowercase__						)





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	import torch

	snake_case_   		=			torch.arange(1      ,			lowercase__						)
	snake_case_   		=			torch.remainder(lowercase__      ,			lowercase__						)
	snake_case_   		=			remainders == 0
	snake_case_   		=			candidates[divisor_indices]
	snake_case_   		=			torch.max(lowercase__						)
	return largest_divisor, torch.div(lowercase__      ,			lowercase__      ,			rounding_mode='floor'						)






class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}				)
				if self.use_past:
					self.fill_with_past_key_values_(__UpperCamelCase			,					direction='inputs'				)
					snake_case_   		=			{0: 'batch', 1: 'past_sequence + sequence'}
				else:
					snake_case_   		=			{0: 'batch', 1: 'sequence'}

				return common_inputs


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self._config.num_heads


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = -1			,					__UpperCamelCase = -1			,					__UpperCamelCase = False			,					__UpperCamelCase = None			,					):

				"""simple docstring"""


				snake_case_   		=			super(__UpperCamelCase			,					self				).generate_dummy_inputs(
				    __UpperCamelCase			,					batch_size=__UpperCamelCase			,					seq_length=__UpperCamelCase			,					is_pair=__UpperCamelCase			,					framework=__UpperCamelCase				)

				# We need to order the input in the way they appears in the forward()
				snake_case_   		=			OrderedDict({'input_ids': common_inputs['input_ids']}				)

				# Need to add the past_keys
				if self.use_past:
					if not is_torch_available():
						raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'				)
					else:
						import torch

						snake_case_		,							snake_case_   		=			common_inputs['input_ids'].shape
						# Not using the same length for past_key_values
						snake_case_   		=			seqlen + 2
						snake_case_   		=			(
						    batch,
						    self.num_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // self.num_attention_heads,
						)
						snake_case_   		=			[
						    (torch.zeros(__UpperCamelCase				), torch.zeros(__UpperCamelCase				)) for _ in range(self.num_layers				)
						]

				snake_case_   		=			common_inputs['attention_mask']
				if self.use_past:
					snake_case_   		=			ordered_inputs['attention_mask'].dtype
					snake_case_   		=			torch.cat(
					    [ordered_inputs['attention_mask'], torch.ones(__UpperCamelCase			,					__UpperCamelCase			,					dtype=__UpperCamelCase				)]			,					dim=1				)

				return ordered_inputs







			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 13
