





import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
        MODEL_FOR_PRETRAINING_MAPPING,
        MegatronBertForCausalLM,
        MegatronBertForMaskedLM,
        MegatronBertForMultipleChoice,
        MegatronBertForNextSentencePrediction,
        MegatronBertForPreTraining,
        MegatronBertForQuestionAnswering,
        MegatronBertForSequenceClassification,
        MegatronBertForTokenClassification,
        MegatronBertModel,
    )





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=7					,  lowercase=True					,  lowercase=True					,  lowercase=True					,  lowercase=True					,  lowercase=99					,  lowercase=64					,  lowercase=32					,  lowercase=5					,  lowercase=4					,  lowercase=37					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=512					,  lowercase=16					,  lowercase=2					,  lowercase=0.02					,  lowercase=3					,  lowercase=4					,  lowercase=None					,  )			->						Tuple:







          '''simple docstring'''

          a__						:	int =				parent
          a__						:	Any =				batch_size
          a__						:	Union[str, Any] =				seq_length
          a__						:	Dict =				is_training
          a__						:	Optional[Any] =				use_input_mask
          a__						:	str =				use_token_type_ids
          a__						:	Union[str, Any] =				use_labels
          a__						:	List[Any] =				vocab_size
          a__						:	str =				hidden_size
          a__						:	str =				embedding_size
          a__						:	Tuple =				num_hidden_layers
          a__						:	List[str] =				num_attention_heads
          a__						:	Optional[Any] =				intermediate_size
          a__						:	List[str] =				hidden_act
          a__						:	Optional[Any] =				hidden_dropout_prob
          a__						:	Optional[int] =				attention_probs_dropout_prob
          a__						:	Dict =				max_position_embeddings
          a__						:	Optional[Any] =				type_vocab_size
          a__						:	List[str] =				type_sequence_label_size
          a__						:	Tuple =				initializer_range
          a__						:	int =				num_labels
          a__						:	str =				num_choices
          a__						:	Optional[int] =				scope

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Any =				ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size)

          a__						:	Dict =				None
          if self.use_input_mask:
                 a__						:	Union[str, Any] =				random_attention_mask([self.batch_size, self.seq_length])

          a__						:	Dict =				None
          if self.use_token_type_ids:
                 a__						:	Union[str, Any] =				ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size)

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

          a__						:	List[str] =				self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          return MegatronBertConfig(
              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					,  embedding_size=self.embedding_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					,  type_vocab_size=self.type_vocab_size					,  is_decoder=lowercase					,  initializer_range=self.initializer_range					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				MegatronBertModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[str] =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase)
          a__						:	Any =				model(lowercase					,  token_type_ids=lowercase)
          a__						:	List[str] =				model(lowercase)

          self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size))
          self.parent.assertEqual(result.pooler_output.shape					,  (self.batch_size, self.hidden_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	int =				MegatronBertForMaskedLM(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[Any] =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						List[Any]:







          '''simple docstring'''

          a__						:	Optional[Any] =				MegatronBertForCausalLM(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Dict =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Any:







          '''simple docstring'''

          a__						:	Optional[int] =				MegatronBertForNextSentencePrediction(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Any =				model(
              lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase					,  )
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, 2))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	Tuple =				MegatronBertForPreTraining(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Optional[Any] =				model(
              lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase					,  next_sentence_label=lowercase					,  )
          self.parent.assertEqual(result.prediction_logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size))
          self.parent.assertEqual(result.seq_relationship_logits.shape					,  (self.batch_size, 2))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	List[str] =				MegatronBertForQuestionAnswering(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	str =				model(
              lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  start_positions=lowercase					,  end_positions=lowercase					,  )
          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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.num_labels
          a__						:	Optional[int] =				MegatronBertForSequenceClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Any =				self.num_labels
          a__						:	Dict =				MegatronBertForTokenClassification(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Dict =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.num_labels))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	Any =				self.num_choices
          a__						:	Any =				MegatronBertForMultipleChoice(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Any =				input_ids.unsqueeze(1).expand(-1					,  self.num_choices					,  -1).contiguous()
          a__						:	Any =				token_type_ids.unsqueeze(1).expand(-1					,  self.num_choices					,  -1).contiguous()
          a__						:	List[Any] =				input_mask.unsqueeze(1).expand(-1					,  self.num_choices					,  -1).contiguous()
          a__						:	Dict =				model(
              lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase					,  )
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_choices))

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Dict =				self.prepare_config_and_inputs()
          (
              (
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							
          )						:	Optional[int] =				config_and_inputs
          a__						:	Optional[int] =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				str         =  (
       (
           MegatronBertModel,
           MegatronBertForMaskedLM,
           MegatronBertForCausalLM,
           MegatronBertForMultipleChoice,
           MegatronBertForNextSentencePrediction,
           MegatronBertForPreTraining,
           MegatronBertForQuestionAnswering,
           MegatronBertForSequenceClassification,
           MegatronBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __A   :				List[Any]         =  (
       {
           '''feature-extraction''': MegatronBertModel,
           '''fill-mask''': MegatronBertForMaskedLM,
           '''question-answering''': MegatronBertForQuestionAnswering,
           '''text-classification''': MegatronBertForSequenceClassification,
           '''text-generation''': MegatronBertForCausalLM,
           '''token-classification''': MegatronBertForTokenClassification,
           '''zero-shot''': MegatronBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __A   :				List[Any]         =  True
   # test_resize_embeddings = False
   __A   :				Tuple         =  False

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase=False)			->						Optional[int]:







          '''simple docstring'''

          a__						:	List[Any] =				super()._prepare_for_class(lowercase					,  lowercase					,  return_labels=lowercase)

          if return_labels:
                 if model_class in get_values(lowercase):
                        a__						:	List[Any] =				torch.zeros(
                            (self.model_tester.batch_size, self.model_tester.seq_length)					,  dtype=torch.long					,  device=lowercase)
                        a__						:	Union[str, Any] =				torch.zeros(
                            self.model_tester.batch_size					,  dtype=torch.long					,  device=lowercase)
          return inputs_dict

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







          '''simple docstring'''

          a__						:	int =				MegatronBertModelTester(self)
          a__						:	List[Any] =				ConfigTester(self					,  config_class=lowercase					,  hidden_size=37)

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







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	List[str] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_model(*lowercase)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowercase)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowercase)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Dict =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	str =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowercase)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	str =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowercase)

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







          '''simple docstring'''

          a__						:	str =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowercase)



def A_	(		A__				)  ->		Any:
       return torch.tensor(
           A__		,      dtype=torch.long		,      device=A__		,      )


lowercase      :       Any									=  1e-4





@require_torch
@require_sentencepiece
@require_tokenizers
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   @slow
   @unittest.skip('Model is not available.')
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	List[str] =				'nvidia/megatron-bert-uncased-345m'
          if "MYDIR" in os.environ:
                 a__						:	Optional[int] =				os.path.join(os.environ['MYDIR']					,  lowercase)
          a__						:	str =				MegatronBertModel.from_pretrained(lowercase)
          model.to(lowercase)
          model.half()
          a__						:	List[str] =				_long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]])
          with torch.no_grad():
                 a__						:	int =				model(lowercase)[0]
          a__						:	Optional[Any] =				torch.Size((1, 9, 1024))
          self.assertEqual(output.shape					,  lowercase)

          a__						:	Tuple =				[-0.60_40, -0.25_17, -0.10_25, 0.34_20, -0.67_58, -0.00_17, -0.10_89, -0.19_90, 0.57_28]
          for ii in range(3):
                 for jj in range(3):
                        a__						:	Optional[Any] =				output[0, ii, jj]
                        a__						:	Tuple =				expected[3 * ii + jj]
                        a__						:	Union[str, Any] =				'ii={} jj={} a={} b={}'.format(lowercase					,  lowercase					,  lowercase					,  lowercase)
                        self.assertTrue(math.isclose(lowercase					,  lowercase					,  rel_tol=lowercase					,  abs_tol=lowercase)					,  msg=lowercase)









import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase      :       List[str]									=  """src/transformers"""
lowercase      :       Optional[int]									=  """docs/source/en/tasks"""



def A_	(		A__		,      A__		,      A__				)  ->		Tuple:
       with open(A__		,      'r'		,      encoding='utf-8'		,      newline='\n'				) as f:
              a__						:	Any =				f.readlines()
       # Find the start prompt.
       a__						:	str =				0
       while not lines[start_index].startswith(A__				):
              start_index += 1
       start_index += 1

       a__						:	int =				start_index
       while not lines[end_index].startswith(A__				):
              end_index += 1
       end_index -= 1

       while len(lines[start_index]				) <= 1:
              start_index += 1
       while len(lines[end_index]				) <= 1:
              end_index -= 1
       end_index += 1
       return "".join(lines[start_index:end_index]				), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
lowercase      :       Tuple									=  direct_transformers_import(TRANSFORMERS_PATH)

lowercase      :       Optional[Any]									=  {
    """asr.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    """audio_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    """language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    """image_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    """masked_language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    """multiple_choice.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    """object_detection.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    """question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    """semantic_segmentation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    """sequence_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    """summarization.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """token_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    """translation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """video_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    """document_question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    """monocular_depth_estimation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
lowercase      :       Optional[Any]									=  {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}



def A_	(		A__				)  ->		Optional[int]:
       a__						:	Optional[Any] =				TASK_GUIDE_TO_MODELS[task_guide]
       a__						:	int =				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A__		,      set()				)
       a__						:	Optional[Any] =				{
           code: name
           for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
           if (code in model_maping_names or code in special_model_types)
       }
       return ", ".join([F'[{name}](../model_doc/{code})' for code, name in model_names.items()]				) + "\n"



def A_	(		A__		,      A__=False				)  ->		Optional[int]:
       a__ ,							a__ ,							a__ ,							a__						:	Dict =				_find_text_in_file(
           filename=os.path.join(A__		,      A__				)		,      start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'		,      end_prompt='<!--End of the generated tip-->'		,      )

       a__						:	List[Any] =				get_model_list_for_task(A__				)

       if current_list != new_list:
              if overwrite:
                     with open(os.path.join(A__		,      A__				)		,      'w'		,      encoding='utf-8'		,      newline='\n'				) as f:
                            f.writelines(lines[:start_index] + [new_list] + lines[end_index:]				)
              else:
                     raise ValueError(
                         F'The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'
                         ' to fix this.'				)


if __name__ == "__main__":
    lowercase      :       Dict									=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase      :       str									=  parser.parse_args()

    for task_guide in TASK_GUIDE_TO_MODELS.keys():
        check_model_list_for_task(task_guide, args.fix_and_overwrite)


