'''simple docstring'''





import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import BertGenerationDecoder, BertGenerationEncoder






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=99    ,							lowercase__=32    ,							lowercase__=5    ,							lowercase__=4    ,							lowercase__=37    ,							lowercase__="gelu"    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=50    ,							lowercase__=0.0_2    ,							lowercase__=True    ,							lowercase__=None    ,							)							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       parent
         SCREAMING_SNAKE_CASE					:			Tuple    	=       batch_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       seq_length
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       is_training
         SCREAMING_SNAKE_CASE					:			Dict    	=       use_input_mask
         SCREAMING_SNAKE_CASE					:			List[str]    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       intermediate_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			List[str]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			List[str]    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			str    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			str    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			str    	=       use_labels
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scope






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Any    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       None
         if self.use_input_mask:
              SCREAMING_SNAKE_CASE					:			Dict    	=       random_attention_mask([self.batch_size, self.seq_length] )

         if self.use_labels:
              SCREAMING_SNAKE_CASE					:			Dict    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.get_config()

         return config, input_ids, input_mask, token_labels






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         return BertGenerationConfig(
             vocab_size=self.vocab_size    ,							hidden_size=self.hidden_size    ,							num_hidden_layers=self.num_hidden_layers    ,							num_attention_heads=self.num_attention_heads    ,							intermediate_size=self.intermediate_size    ,							hidden_act=self.hidden_act    ,							hidden_dropout_prob=self.hidden_dropout_prob    ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							is_decoder=lowercase__    ,							initializer_range=self.initializer_range    ,							)






    def       _UpperCamelCase    (       self )							-> str:
         (
             SCREAMING_SNAKE_CASE   
         )					:			List[Any]    	=       self.prepare_config_and_inputs()

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       True
         SCREAMING_SNAKE_CASE					:			List[str]    	=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ids_tensor([self.batch_size, self.seq_length]    ,							vocab_size=2 )

         return (
             config,
             input_ids,
             input_mask,
             token_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							**lowercase__    ,							)							-> str:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       BertGenerationEncoder(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Dict    	=       model(lowercase__    ,							attention_mask=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__ )
         self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							**lowercase__    ,							)							-> Tuple:
         SCREAMING_SNAKE_CASE					:			int    	=       True
         SCREAMING_SNAKE_CASE					:			List[str]    	=       BertGenerationEncoder(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Dict    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							encoder_attention_mask=lowercase__    ,							)
         SCREAMING_SNAKE_CASE					:			int    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							)
         self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							**lowercase__    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			Any    	=       True
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       True
         SCREAMING_SNAKE_CASE					:			Tuple    	=       BertGenerationDecoder(config=lowercase__ ).to(lowercase__ ).eval()

         # first forward pass
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							encoder_attention_mask=lowercase__    ,							use_cache=lowercase__    ,							)
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ids_tensor((self.batch_size, 3)    ,							config.vocab_size )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       ids_tensor((self.batch_size, 3)    ,							vocab_size=2 )

         # append to next input_ids and
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.cat([input_ids, next_tokens]    ,							dim=-1 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.cat([input_mask, next_mask]    ,							dim=-1 )

         SCREAMING_SNAKE_CASE					:			Any    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							encoder_attention_mask=lowercase__    ,							output_hidden_states=lowercase__    ,							)['hidden_states'][0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							encoder_hidden_states=lowercase__    ,							encoder_attention_mask=lowercase__    ,							past_key_values=lowercase__    ,							output_hidden_states=lowercase__    ,							)['hidden_states'][0]

         # select random slice
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ids_tensor((1,)    ,							output_from_past.shape[-1] ).item()
         SCREAMING_SNAKE_CASE					:			int    	=       output_from_no_past[:, -3:, random_slice_idx].detach()
         SCREAMING_SNAKE_CASE					:			str    	=       output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(lowercase__    ,							lowercase__    ,							atol=1E-3 ) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							*lowercase__    ,							)							-> str:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       BertGenerationDecoder(lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Dict    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.vocab_size) )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			int    	=       self.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_torch
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   Dict							=						(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
    snake_case__							:   Tuple							=						(BertGenerationDecoder,) if is_torch_available() else ()
    snake_case__							:   str							=						(
        {"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
        if is_torch_available()
        else {}
    )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       BertGenerationEncoderTester(self )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ConfigTester(self    ,							config_class=lowercase__    ,							hidden_size=37 )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         self.config_tester.run_common_tests()






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       'bert'
         self.model_tester.create_and_check_model(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         # This regression test was failing with PyTorch < 1.3
         (
             SCREAMING_SNAKE_CASE   
         )					:			Optional[Any]    	=       self.model_tester.prepare_config_and_inputs_for_decoder()

         SCREAMING_SNAKE_CASE					:			str    	=       None

         self.model_tester.create_and_check_model_as_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							)






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_for_causal_lm(*lowercase__ )






    @slow
    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Any    	=       BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
         self.assertIsNotNone(lowercase__ )






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @slow
    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			int    	=       BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
         SCREAMING_SNAKE_CASE					:			int    	=       torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]] )
         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			List[str]    	=       model(lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.Size([1, 8, 1_024] )
         self.assertEqual(output.shape    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.tensor(
             [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]] )
         self.assertTrue(torch.allclose(output[:, :3, :3]    ,							lowercase__    ,							atol=1E-4 ) )






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @slow
    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]] )
         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.Size([1, 8, 50_358] )
         self.assertEqual(output.shape    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor(
             [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]] )
         self.assertTrue(torch.allclose(output[:, :3, :3]    ,							lowercase__    ,							atol=1E-4 ) )




'''simple docstring'''





import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       args.log_outputs
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       '_'.join(args.dataset.split('/'		) + [args.config, args.split]		)

     # load metric
     SCREAMING_SNAKE_CASE					:			int    	=       load_metric('wer'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       load_metric('cer'		)

     # compute metrics
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       wer.compute(references=result['target']       ,      predictions=result['prediction']		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       cer.compute(references=result['target']       ,      predictions=result['prediction']		)

     # print & log results
     SCREAMING_SNAKE_CASE					:			int    	=       f"""WER: {wer_result}\nCER: {cer_result}"""
     print(a_		)

     with open(f"""{dataset_id}_eval_results.txt"""       ,      'w'		) as f:
          f.write(a_		)

     # log all results in text file. Possibly interesting for analysis
     if log_outputs is not None:
          SCREAMING_SNAKE_CASE					:			Any    	=       f"""log_{dataset_id}_predictions.txt"""
          SCREAMING_SNAKE_CASE					:			int    	=       f"""log_{dataset_id}_targets.txt"""

          with open(a_       ,      'w'		) as p, open(a_       ,      'w'		) as t:
               # mapping function to write output
               def write_to_file(a_       ,      a_		):
                    p.write(f"""{i}""" + '\n'		)
                    p.write(batch['prediction'] + '\n'		)
                    t.write(f"""{i}""" + '\n'		)
                    t.write(batch['target'] + '\n'		)

               result.map(a_       ,      with_indices=a_		)


def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       re.sub(a_       ,      ''       ,      text.lower()		)

     # In addition, we can normalize the target text, e.g. removing new lines characters etc...
     # note that order is important here!
     SCREAMING_SNAKE_CASE					:			str    	=       ['\n\n', '\n', '   ', '  ']

     for t in token_sequences_to_ignore:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       ' '.join(text.split(a_		)		)

     return text


def     __lowerCAmelCase						(       a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       load_dataset(args.dataset       ,      args.config       ,      split=args.split       ,      use_auth_token=a_		)

     # for testing: only process the first two examples as a test
     # dataset = dataset.select(range(10))

     # load processor
     SCREAMING_SNAKE_CASE					:			str    	=       AutoFeatureExtractor.from_pretrained(args.model_id		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       feature_extractor.sampling_rate

     # resample audio
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       dataset.cast_column('audio'       ,      Audio(sampling_rate=a_		)		)

     # load eval pipeline
     if args.device is None:
          SCREAMING_SNAKE_CASE					:			Tuple    	=       0 if torch.cuda.is_available() else -1
     SCREAMING_SNAKE_CASE					:			int    	=       pipeline('automatic-speech-recognition'       ,      model=args.model_id       ,      device=args.device		)

     # map function to decode audio
     def map_to_pred(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       asr(
              batch['audio']['array']       ,      chunk_length_s=args.chunk_length_s       ,      stride_length_s=args.stride_length_s		)

          SCREAMING_SNAKE_CASE					:			str    	=       prediction['text']
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       normalize_text(batch['sentence']		)
          return batch

     # run inference on all examples
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dataset.map(a_       ,      remove_columns=dataset.column_names		)

     # compute and log_results
     # do not change function below
     log_results(a_       ,      a_		)


if __name__ == "__main__":
 _lowerCAmelCase  :Optional[Any]  				=    argparse.ArgumentParser()

 parser.add_argument(
     """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
 )
 parser.add_argument(
     """--dataset""",
     type=str,
     required=True,
     help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
 )
 parser.add_argument(
     """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
 )
 parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
 parser.add_argument(
     """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
 )
 parser.add_argument(
     """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
 )
 parser.add_argument(
     """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
 )
 parser.add_argument(
     """--device""",
     type=int,
     default=None,
     help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
 )
 _lowerCAmelCase  :List[str]  				=    parser.parse_args()

 main(args)



