
import unittest

from transformers import XLMConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       XLMForMultipleChoice,
       XLMForQuestionAnswering,
       XLMForQuestionAnsweringSimple,
       XLMForSequenceClassification,
       XLMForTokenClassification,
       XLMModel,
       XLMWithLMHeadModel,
   )
   from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST






class   UpperCAmelCase		:



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					str    ,						snake_case__		:					Optional[Any]=13    ,						snake_case__		:					List[str]=7    ,						snake_case__		:					Dict=True    ,						snake_case__		:					str=True    ,						snake_case__		:					Optional[Any]=True    ,						snake_case__		:					Tuple=True    ,						snake_case__		:					Union[str, Any]=True    ,						snake_case__		:					Optional[int]=False    ,						snake_case__		:					Tuple=False    ,						snake_case__		:					Tuple=False    ,						snake_case__		:					List[str]=2    ,						snake_case__		:					List[Any]=99    ,						snake_case__		:					List[Any]=0    ,						snake_case__		:					List[Any]=32    ,						snake_case__		:					Tuple=5    ,						snake_case__		:					int=4    ,						snake_case__		:					str=0.1    ,						snake_case__		:					Dict=0.1    ,						snake_case__		:					Optional[Any]=5_12    ,						snake_case__		:					Optional[Any]=2    ,						snake_case__		:					Tuple=0.02    ,						snake_case__		:					Optional[int]=2    ,						snake_case__		:					Union[str, Any]=4    ,						snake_case__		:					int="last"    ,						snake_case__		:					str=True    ,						snake_case__		:					Dict=None    ,						snake_case__		:					int=0    ,						)		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Tuple							=      parent
          snake_case       :   List[str]							=      batch_size
          snake_case       :   Dict							=      seq_length
          snake_case       :   List[str]							=      is_training
          snake_case       :   Dict							=      use_input_lengths
          snake_case       :   List[str]							=      use_token_type_ids
          snake_case       :   Dict							=      use_labels
          snake_case       :   List[Any]							=      gelu_activation
          snake_case       :   Any							=      sinusoidal_embeddings
          snake_case       :   Tuple							=      causal
          snake_case       :   str							=      asm
          snake_case       :   Tuple							=      n_langs
          snake_case       :   int							=      vocab_size
          snake_case       :   Any							=      n_special
          snake_case       :   List[Any]							=      hidden_size
          snake_case       :   Optional[int]							=      num_hidden_layers
          snake_case       :   List[Any]							=      num_attention_heads
          snake_case       :   Any							=      hidden_dropout_prob
          snake_case       :   Tuple							=      attention_probs_dropout_prob
          snake_case       :   Dict							=      max_position_embeddings
          snake_case       :   int							=      type_sequence_label_size
          snake_case       :   List[str]							=      initializer_range
          snake_case       :   str							=      num_labels
          snake_case       :   Optional[Any]							=      num_choices
          snake_case       :   Union[str, Any]							=      summary_type
          snake_case       :   Any							=      use_proj
          snake_case       :   int							=      scope
          snake_case       :   Optional[Any]							=      bos_token_id



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size )
          snake_case       :   Optional[int]							=      random_attention_mask([self.batch_size, self.seq_length] )

          snake_case       :   Tuple							=      None
          if self.use_input_lengths:
                 snake_case       :   List[str]							=      (
                     ids_tensor([self.batch_size]    ,						vocab_size=2 ) + self.seq_length - 2
                 )  # small variation of seq_length

          snake_case       :   Any							=      None
          if self.use_token_type_ids:
                 snake_case       :   str							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.n_langs )

          snake_case       :   Optional[int]							=      None
          snake_case       :   Optional[int]							=      None
          snake_case       :   Union[str, Any]							=      None
          if self.use_labels:
                 snake_case       :   Optional[Any]							=      ids_tensor([self.batch_size]    ,						self.type_sequence_label_size )
                 snake_case       :   int							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.num_labels )
                 snake_case       :   Union[str, Any]							=      ids_tensor([self.batch_size]    ,						2 ).float()
                 snake_case       :   int							=      ids_tensor([self.batch_size]    ,						self.num_choices )

          snake_case       :   List[Any]							=      self.get_config()

          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	int:


          '''simple docstring'''


          return XLMConfig(
              vocab_size=self.vocab_size    ,						n_special=self.n_special    ,						emb_dim=self.hidden_size    ,						n_layers=self.num_hidden_layers    ,						n_heads=self.num_attention_heads    ,						dropout=self.hidden_dropout_prob    ,						attention_dropout=self.attention_probs_dropout_prob    ,						gelu_activation=self.gelu_activation    ,						sinusoidal_embeddings=self.sinusoidal_embeddings    ,						asm=self.asm    ,						causal=self.causal    ,						n_langs=self.n_langs    ,						max_position_embeddings=self.max_position_embeddings    ,						initializer_range=self.initializer_range    ,						summary_type=self.summary_type    ,						use_proj=self.use_proj    ,						num_labels=self.num_labels    ,						bos_token_id=self.bos_token_id    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					Any    ,						snake_case__		:					List[str]    ,						snake_case__		:					str    ,						snake_case__		:					List[Any]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					str    ,						)		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   str							=      XLMModel(config=snake_case__ )
          model.to(snake_case__ )
          model.eval()
          snake_case       :   Dict							=      model(snake_case__    ,						lengths=snake_case__    ,						langs=snake_case__ )
          snake_case       :   Optional[Any]							=      model(snake_case__    ,						langs=snake_case__ )
          snake_case       :   Union[str, Any]							=      model(snake_case__ )
          self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Tuple    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					List[str]    ,						snake_case__		:					Any    ,						snake_case__		:					Any    ,						snake_case__		:					List[Any]    ,						)		->	str:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      XLMWithLMHeadModel(snake_case__ )
          model.to(snake_case__ )
          model.eval()

          snake_case       :   str							=      model(snake_case__    ,						token_type_ids=snake_case__    ,						labels=snake_case__ )
          self.parent.assertEqual(result.loss.shape    ,						() )
          self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Any    ,						snake_case__		:					List[str]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Tuple    ,						snake_case__		:					str    ,						snake_case__		:					int    ,						snake_case__		:					int    ,						)		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   int							=      XLMForQuestionAnsweringSimple(snake_case__ )
          model.to(snake_case__ )
          model.eval()

          snake_case       :   int							=      model(snake_case__ )

          snake_case       :   Optional[int]							=      model(snake_case__    ,						start_positions=snake_case__    ,						end_positions=snake_case__ )
          snake_case       :   Tuple							=      outputs
          self.parent.assertEqual(result.start_logits.shape    ,						(self.batch_size, self.seq_length) )
          self.parent.assertEqual(result.end_logits.shape    ,						(self.batch_size, self.seq_length) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					int    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					Any    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					Tuple    ,						)		->	str:


          '''simple docstring'''


          snake_case       :   int							=      XLMForQuestionAnswering(snake_case__ )
          model.to(snake_case__ )
          model.eval()

          snake_case       :   int							=      model(snake_case__ )

          snake_case       :   Optional[int]							=      model(
              snake_case__    ,						start_positions=snake_case__    ,						end_positions=snake_case__    ,						cls_index=snake_case__    ,						is_impossible=snake_case__    ,						p_mask=snake_case__    ,						)

          snake_case       :   int							=      model(
              snake_case__    ,						start_positions=snake_case__    ,						end_positions=snake_case__    ,						cls_index=snake_case__    ,						is_impossible=snake_case__    ,						)

          ((snake_case)				,					)       :   List[Any]							=      result_with_labels.to_tuple()

          snake_case       :   Optional[Any]							=      model(snake_case__    ,						start_positions=snake_case__    ,						end_positions=snake_case__ )

          ((snake_case)				,					)       :   int							=      result_with_labels.to_tuple()

          self.parent.assertEqual(result_with_labels.loss.shape    ,						() )
          self.parent.assertEqual(result.start_top_log_probs.shape    ,						(self.batch_size, model.config.start_n_top) )
          self.parent.assertEqual(result.start_top_index.shape    ,						(self.batch_size, model.config.start_n_top) )
          self.parent.assertEqual(
              result.end_top_log_probs.shape    ,						(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
          self.parent.assertEqual(
              result.end_top_index.shape    ,						(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
          self.parent.assertEqual(result.cls_logits.shape    ,						(self.batch_size,) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					Dict    ,						snake_case__		:					Any    ,						snake_case__		:					Any    ,						snake_case__		:					int    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					int    ,						snake_case__		:					str    ,						snake_case__		:					Dict    ,						)		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      XLMForSequenceClassification(snake_case__ )
          model.to(snake_case__ )
          model.eval()

          snake_case       :   Dict							=      model(snake_case__ )
          snake_case       :   List[str]							=      model(snake_case__    ,						labels=snake_case__ )
          self.parent.assertEqual(result.loss.shape    ,						() )
          self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					Any    ,						snake_case__		:					Any    ,						snake_case__		:					int    ,						snake_case__		:					str    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					str    ,						snake_case__		:					List[str]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Optional[int]    ,						)		->	Tuple:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      self.num_labels
          snake_case       :   Tuple							=      XLMForTokenClassification(snake_case__ )
          model.to(snake_case__ )
          model.eval()

          snake_case       :   Dict							=      model(snake_case__    ,						attention_mask=snake_case__    ,						labels=snake_case__ )
          self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.num_labels) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Any    ,						snake_case__		:					str    ,						snake_case__		:					Tuple    ,						snake_case__		:					Tuple    ,						snake_case__		:					Tuple    ,						snake_case__		:					Tuple    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Union[str, Any]    ,						)		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      self.num_choices
          snake_case       :   str							=      XLMForMultipleChoice(config=snake_case__ )
          model.to(snake_case__ )
          model.eval()
          snake_case       :   Dict							=      input_ids.unsqueeze(1 ).expand(-1    ,						self.num_choices    ,						-1 ).contiguous()
          snake_case       :   int							=      token_type_ids.unsqueeze(1 ).expand(-1    ,						self.num_choices    ,						-1 ).contiguous()
          snake_case       :   Tuple							=      input_mask.unsqueeze(1 ).expand(-1    ,						self.num_choices    ,						-1 ).contiguous()
          snake_case       :   Union[str, Any]							=      model(
              snake_case__    ,						attention_mask=snake_case__    ,						token_type_ids=snake_case__    ,						labels=snake_case__    ,						)
          self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_choices) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Any:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      self.prepare_config_and_inputs()
          (
              (
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					
          )       :   Dict							=      config_and_inputs
          snake_case       :   Dict							=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
          return config, inputs_dict






@require_torch
class   UpperCAmelCase		(						A_       ,A_       ,A_       ,unittest.TestCase ):
   A__					:		Dict						     =     (
       (
           XLMModel,
           XLMWithLMHeadModel,
           XLMForQuestionAnswering,
           XLMForSequenceClassification,
           XLMForQuestionAnsweringSimple,
           XLMForTokenClassification,
           XLMForMultipleChoice,
       )
       if is_torch_available()
       else ()
   )
   A__					:		Any						     =     (
       (XLMWithLMHeadModel,) if is_torch_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   A__					:		str						     =     (
       {
           "feature-extraction": XLMModel,
           "fill-mask": XLMWithLMHeadModel,
           "question-answering": XLMForQuestionAnsweringSimple,
           "text-classification": XLMForSequenceClassification,
           "text-generation": XLMWithLMHeadModel,
           "token-classification": XLMForTokenClassification,
           "zero-shot": XLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Tuple    ,						snake_case__		:					int    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					List[str] )		->	int:


          '''simple docstring'''


          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith("Fast" )
          ):
                 # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                 # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                 # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                 return True

          return False



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					str=False )		->	int:


          '''simple docstring'''


          snake_case       :   Tuple							=      super()._prepare_for_class(snake_case__    ,						snake_case__    ,						return_labels=snake_case__ )

          if return_labels:
                 if model_class.__name__ == "XLMForQuestionAnswering":
                        snake_case       :   Tuple							=      torch.zeros(
                            self.model_tester.batch_size    ,						dtype=torch.long    ,						device=snake_case__ )
                        snake_case       :   Optional[int]							=      torch.zeros(
                            self.model_tester.batch_size    ,						dtype=torch.long    ,						device=snake_case__ )

          return inputs_dict



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	int:


          '''simple docstring'''


          snake_case       :   Any							=      XLMModelTester(self )
          snake_case       :   List[str]							=      ConfigTester(self    ,						config_class=snake_case__    ,						emb_dim=37 )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[Any]:


          '''simple docstring'''


          self.config_tester.run_common_tests()



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Any:


          '''simple docstring'''


          snake_case       :   int							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   str							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


          snake_case       :   List[str]							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_simple_qa(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	Any:


          '''simple docstring'''


          snake_case       :   str							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_token_classif(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Any:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					str    ,						snake_case__		:					List[Any]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Optional[Any]=False    ,						snake_case__		:					int=1 )		->	Optional[int]:


          '''simple docstring'''


          self.assertIsInstance(snake_case__    ,						snake_case__ )
          self.assertListEqual(
              [isinstance(snake_case__    ,						snake_case__ ) for iter_attentions in attentions]    ,						[True] * len(snake_case__ ) )
          self.assertEqual(len(snake_case__ )    ,						(max_length - min_length) * num_beam_groups )

          for idx, iter_attentions in enumerate(snake_case__ ):
                 # adds PAD dummy token
                 snake_case       :   int							=      min_length + idx + 1
                 snake_case       :   Any							=      min_length + idx + 1

                 snake_case       :   str							=      (
                     batch_size * num_beam_groups,
                     config.num_attention_heads,
                     tgt_len,
                     src_len,
                 )
                 # check attn size
                 self.assertListEqual(
                     [layer_attention.shape for layer_attention in iter_attentions]    ,						[expected_shape] * len(snake_case__ ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					List[str]    ,						snake_case__		:					Dict    ,						snake_case__		:					Tuple    ,						snake_case__		:					Dict    ,						snake_case__		:					Optional[int]=False    ,						snake_case__		:					str=1 )		->	Dict:


          '''simple docstring'''


          self.assertIsInstance(snake_case__    ,						snake_case__ )
          self.assertListEqual(
              [isinstance(snake_case__    ,						snake_case__ ) for iter_hidden_states in hidden_states]    ,						[True] * len(snake_case__ )    ,						)
          self.assertEqual(len(snake_case__ )    ,						(max_length - min_length) * num_beam_groups )

          for idx, iter_hidden_states in enumerate(snake_case__ ):
                 # adds PAD dummy token
                 snake_case       :   str							=      min_length + idx + 1
                 snake_case       :   Tuple							=      (batch_size * num_beam_groups, seq_len, config.hidden_size)
                 # check hidden size
                 self.assertListEqual(
                     [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]    ,						[expected_shape] * len(snake_case__ )    ,						)
          pass



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


          for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case       :   Dict							=      XLMModel.from_pretrained(snake_case__ )
                 self.assertIsNotNone(snake_case__ )






@require_torch
class   UpperCAmelCase		(						unittest.TestCase ):



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   int							=      XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048" )
          model.to(snake_case__ )
          snake_case       :   Any							=      torch.tensor([[14, 4_47]]    ,						dtype=torch.long    ,						device=snake_case__ )  # the president
          snake_case       :   Dict							=      [
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
              14,
              4_47,
          ]  # the president the president the president the president the president the president the president the president the president the president
          # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
          snake_case       :   Optional[int]							=      model.generate(snake_case__    ,						do_sample=snake_case__ )
          self.assertListEqual(output_ids[0].cpu().numpy().tolist()    ,						snake_case__ )







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 			UpperCamelCase					(    __lowerCamelCase     : Dataset				,					__lowerCamelCase     : Dict[str, str]    ):
       snake_case       :   int							=      args.log_outputs
       snake_case       :   Dict							=      "_".join(args.dataset.split("/"    ) + [args.config, args.split]    )

       # load metric
       snake_case       :   List[str]							=      load_metric("wer"    )
       snake_case       :   Tuple							=      load_metric("cer"    )

       # compute metrics
       snake_case       :   List[Any]							=      wer.compute(references=result["target"]				,					predictions=result["prediction"]    )
       snake_case       :   int							=      cer.compute(references=result["target"]				,					predictions=result["prediction"]    )

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

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

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

              with open(__lowerCamelCase				,					"w"    ) as p, open(__lowerCamelCase				,					"w"    ) as t:
                     # mapping function to write output
                     def write_to_file(__lowerCamelCase     : str				,					__lowerCamelCase     : Optional[int]    ):
                            p.write(f"""{i}""" + "\n"    )
                            p.write(batch["prediction"] + "\n"    )
                            t.write(f"""{i}""" + "\n"    )
                            t.write(batch["target"] + "\n"    )

                     result.map(__lowerCamelCase				,					with_indices=__lowerCamelCase    )


def 			UpperCamelCase					(    __lowerCamelCase     : str    ):
       snake_case       :   List[Any]							=      "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

       snake_case       :   List[Any]							=      re.sub(__lowerCamelCase				,					""				,					text.lower()    )

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

       for t in token_sequences_to_ignore:
              snake_case       :   Dict							=      " ".join(text.split(__lowerCamelCase    )    )

       return text


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       # load dataset
       snake_case       :   str							=      load_dataset(args.dataset				,					args.config				,					split=args.split				,					use_auth_token=__lowerCamelCase    )

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

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

       # resample audio
       snake_case       :   Union[str, Any]							=      dataset.cast_column("audio"				,					Audio(sampling_rate=__lowerCamelCase    )    )

       # load eval pipeline
       if args.device is None:
              snake_case       :   List[str]							=      0 if torch.cuda.is_available() else -1
       snake_case       :   str							=      pipeline("automatic-speech-recognition"				,					model=args.model_id				,					device=args.device    )

       # map function to decode audio
       def map_to_pred(__lowerCamelCase     : int    ):
              snake_case       :   Dict							=      asr(
                  batch["audio"]["array"]				,					chunk_length_s=args.chunk_length_s				,					stride_length_s=args.stride_length_s    )

              snake_case       :   str							=      prediction["text"]
              snake_case       :   Tuple							=      normalize_text(batch["sentence"]    )
              return batch

       # run inference on all examples
       snake_case       :   Dict							=      dataset.map(__lowerCamelCase				,					remove_columns=dataset.column_names    )

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


if __name__ == "__main__":
   __lowerCamelCase							=       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							=       parser.parse_args()

   main(args)





