



"""simple docstring"""
from jiwer import compute_measures

import datasets


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

__SCREAMING_SNAKE_CASE  :						List[str]    							=      '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The 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.

This 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.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''

__SCREAMING_SNAKE_CASE  :						Tuple    							=      '''
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = datasets.load_metric("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
'''


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





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									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 			snake_case__      (    self				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=False   ):
									if concatenate_texts:
													return compute_measures(lowerCamelCase__				,       lowerCamelCase__   )["wer"]
									else:
													_lowerCamelCase        =				0
													_lowerCamelCase        =				0
													for prediction, reference in zip(lowerCamelCase__				,       lowerCamelCase__   ):
																	_lowerCamelCase        =				compute_measures(lowerCamelCase__				,       lowerCamelCase__   )
																	incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																	total += measures["substitutions"] + measures["deletions"] + measures["hits"]
													return incorrect / total





"""simple docstring"""
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							List[Any]						,   lowercase_     :							List[Any]						,   lowercase_     :							Tuple						,   lowercase_     :							List[str]							)	->       Dict:
				# Load configuration defined in the metadata file
				with open(lowercase_							) as metadata_file:
								_lowerCamelCase        =				json.load(lowercase_							)
				_lowerCamelCase        =				LukeConfig(use_entity_aware_attention=lowercase_						,   **metadata['''model_config''']							)

				# Load in the weights from the checkpoint_path
				_lowerCamelCase        =				torch.load(lowercase_						,   map_location='''cpu'''							)

				# Load the entity vocab file
				_lowerCamelCase        =				load_entity_vocab(lowercase_							)

				_lowerCamelCase        =				RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

				# Add special tokens to the token vocabulary for downstream tasks
				_lowerCamelCase        =				AddedToken('''<ent>'''						,   lstrip=lowercase_						,   rstrip=lowercase_							)
				_lowerCamelCase        =				AddedToken('''<ent2>'''						,   lstrip=lowercase_						,   rstrip=lowercase_							)
				tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}							)
				config.vocab_size += 2

				print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""							)
				tokenizer.save_pretrained(lowercase_							)
				with open(os.path.join(lowercase_						,   LukeTokenizer.vocab_files_names['''entity_vocab_file''']							)						,   '''w'''							) as f:
								json.dump(lowercase_						,   lowercase_							)

				_lowerCamelCase        =				LukeTokenizer.from_pretrained(lowercase_							)

				# Initialize the embeddings of the special tokens
				_lowerCamelCase        =				state_dict['''embeddings.word_embeddings.weight''']
				_lowerCamelCase        =				word_emb[tokenizer.convert_tokens_to_ids(['''@''']							)[0]].unsqueeze(0							)
				_lowerCamelCase        =				word_emb[tokenizer.convert_tokens_to_ids(['''#''']							)[0]].unsqueeze(0							)
				_lowerCamelCase        =				torch.cat([word_emb, ent_emb, enta_emb]							)

				# Initialize the query layers of the entity-aware self-attention mechanism
				for layer_index in range(config.num_hidden_layers							):
								for matrix_name in ["query.weight", "query.bias"]:
												_lowerCamelCase        =				F"""encoder.layer.{layer_index}.attention.self."""
												_lowerCamelCase        =				state_dict[prefix + matrix_name]
												_lowerCamelCase        =				state_dict[prefix + matrix_name]
												_lowerCamelCase        =				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				_lowerCamelCase        =				state_dict['''entity_embeddings.entity_embeddings.weight''']
				_lowerCamelCase        =				entity_emb[entity_vocab['''[MASK]''']]

				_lowerCamelCase        =				LukeModel(config=lowercase_							).eval()

				_lowerCamelCase  ,     _lowerCamelCase        =				model.load_state_dict(lowercase_						,   strict=lowercase_							)
				if not (len(lowercase_							) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(F"""Missing keys {", ".join(lowercase_							)}. Expected only missing embeddings.position_ids"""							)
				if not (all(key.startswith('''entity_predictions'''							) or key.startswith('''lm_head'''							) for key in unexpected_keys							)):
								raise ValueError(
								    '''Unexpected keys'''
								    F""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"							) or key.startswith("lm_head"							))]							)}"""							)

				# Check outputs
				_lowerCamelCase        =				LukeTokenizer.from_pretrained(lowercase_						,   task='''entity_classification'''							)

				_lowerCamelCase        =				(
				    '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
				    ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
				)
				_lowerCamelCase        =				(39, 42)
				_lowerCamelCase        =				tokenizer(lowercase_						,   entity_spans=[span]						,   add_prefix_space=lowercase_						,   return_tensors='''pt'''							)

				_lowerCamelCase        =				model(**lowercase_							)

				# Verify word hidden states
				if model_size == "large":
								_lowerCamelCase        =				torch.Size((1, 42, 10_24)							)
								_lowerCamelCase        =				torch.tensor(
								    [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]							)
				else:  # base
								_lowerCamelCase        =				torch.Size((1, 42, 7_68)							)
								_lowerCamelCase        =				torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]							)

				if not (outputs.last_hidden_state.shape == expected_shape):
								raise ValueError(
								    F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""							)
				if not torch.allclose(outputs.last_hidden_state[0, :3, :3]						,   lowercase_						,   atol=1e-4							):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								_lowerCamelCase        =				torch.Size((1, 1, 10_24)							)
								_lowerCamelCase        =				torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]							)
				else:  # base
								_lowerCamelCase        =				torch.Size((1, 1, 7_68)							)
								_lowerCamelCase        =				torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]							)

				if not (outputs.entity_last_hidden_state.shape != expected_shape):
								raise ValueError(
								    F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
								    F""" {expected_shape}"""							)
				if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]						,   lowercase_						,   atol=1e-4							):
								raise ValueError

				# Finally, save our PyTorch model and tokenizer
				print('''Saving PyTorch model to {}'''.format(lowercase_							)							)
				model.save_pretrained(lowercase_							)



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]							)	->       Any:
				_lowerCamelCase        =				{}
				with open(lowercase_						,   '''r'''						,   encoding='''utf-8'''							) as f:
								for index, line in enumerate(lowercase_							):
												_lowerCamelCase  ,     _lowerCamelCase        =				line.rstrip().split('''\t'''							)
												_lowerCamelCase        =				index

				return entity_vocab


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[str]    							=      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
			parser.add_argument(
			    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
			)
			parser.add_argument(
			    '''--entity_vocab_path''',
			    default=None,
			    type=str,
			    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
			)
			__SCREAMING_SNAKE_CASE  :						int    							=      parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)
