



'''simple docstring'''



import unittest

from transformers import FunnelConfig, FunnelTokenizer, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_PRETRAINING_MAPPING,
     FunnelBaseModel,
     FunnelForMaskedLM,
     FunnelForMultipleChoice,
     FunnelForPreTraining,
     FunnelForQuestionAnswering,
     FunnelForSequenceClassification,
     FunnelForTokenClassification,
     FunnelModel,
 )
class        UpperCAmelCase__  :



 def __init__(  self		,	lowercase		,	lowercase=1_3		,	lowercase=7		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=9_9		,	lowercase=[1, 1, 2]		,	lowercase=1		,	lowercase=3_2		,	lowercase=4		,	lowercase=8		,	lowercase=3_7		,	lowercase="gelu_new"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=0.0		,	lowercase=5_1_2		,	lowercase=3		,	lowercase=0.02		,	lowercase=3		,	lowercase=4		,	lowercase=None		,	lowercase=False		,	)  ->							Any:
  __UpperCamelCase				      =  parent
  __UpperCamelCase				      =  batch_size
  __UpperCamelCase				      =  seq_length
  __UpperCamelCase				      =  is_training
  __UpperCamelCase				      =  use_input_mask
  __UpperCamelCase				      =  use_token_type_ids
  __UpperCamelCase				      =  use_labels
  __UpperCamelCase				      =  vocab_size
  __UpperCamelCase				      =  block_sizes
  __UpperCamelCase				      =  num_decoder_layers
  __UpperCamelCase				      =  d_model
  __UpperCamelCase				      =  n_head
  __UpperCamelCase				      =  d_head
  __UpperCamelCase				      =  d_inner
  __UpperCamelCase				      =  hidden_act
  __UpperCamelCase				      =  hidden_dropout
  __UpperCamelCase				      =  attention_dropout
  __UpperCamelCase				      =  activation_dropout
  __UpperCamelCase				      =  max_position_embeddings
  __UpperCamelCase				      =  type_vocab_size
  __UpperCamelCase				      =  2
  __UpperCamelCase				      =  num_labels
  __UpperCamelCase				      =  num_choices
  __UpperCamelCase				      =  scope
  __UpperCamelCase				      =  initializer_std

  # Used in the tests to check the size of the first attention layer
  __UpperCamelCase				      =  n_head
  # Used in the tests to check the size of the first hidden state
  __UpperCamelCase				      =  self.d_model
  # Used in the tests to check the number of output hidden states/attentions
  __UpperCamelCase				      =  sum(self.block_sizes			) + (0 if base else self.num_decoder_layers)
  # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
  # the last hidden state of the first block (which is the first hidden state of the decoder).
  if not base:
   __UpperCamelCase				      =  self.num_hidden_layers + 2



 def    __lowerCamelCase				(  self			)  ->							Tuple:
  __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size			)

  __UpperCamelCase				      =  None
  if self.use_input_mask:
   __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	vocab_size=2			)

  __UpperCamelCase				      =  None
  if self.use_token_type_ids:
   __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size			)

  __UpperCamelCase				      =  None
  __UpperCamelCase				      =  None
  __UpperCamelCase				      =  None
  if self.use_labels:
   __UpperCamelCase				      =  ids_tensor([self.batch_size]		,	self.type_sequence_label_size			)
   __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels			)
   __UpperCamelCase				      =  ids_tensor([self.batch_size]		,	self.num_choices			)
   __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	1			)

  __UpperCamelCase				      =  self.get_config()

  return (
      config,
      input_ids,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
      fake_token_labels,
  )



 def    __lowerCamelCase				(  self			)  ->							Any:
  return FunnelConfig(
      vocab_size=self.vocab_size		,	block_sizes=self.block_sizes		,	num_decoder_layers=self.num_decoder_layers		,	d_model=self.d_model		,	n_head=self.n_head		,	d_head=self.d_head		,	d_inner=self.d_inner		,	hidden_act=self.hidden_act		,	hidden_dropout=self.hidden_dropout		,	attention_dropout=self.attention_dropout		,	activation_dropout=self.activation_dropout		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	initializer_std=self.initializer_std		,	)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Any:
  __UpperCamelCase				      =  FunnelModel(config=lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  model(lowercase		,	attention_mask=lowercase		,	token_type_ids=lowercase			)
  __UpperCamelCase				      =  model(lowercase		,	token_type_ids=lowercase			)
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.d_model)			)

  __UpperCamelCase				      =  False
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.d_model)			)

  __UpperCamelCase				      =  False
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.d_model)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							str:
  __UpperCamelCase				      =  FunnelBaseModel(config=lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  model(lowercase		,	attention_mask=lowercase		,	token_type_ids=lowercase			)
  __UpperCamelCase				      =  model(lowercase		,	token_type_ids=lowercase			)
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, 2, self.d_model)			)

  __UpperCamelCase				      =  False
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, 3, self.d_model)			)

  __UpperCamelCase				      =  False
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, 2, self.d_model)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Optional[Any]:
  __UpperCamelCase				      =  self.num_labels
  __UpperCamelCase				      =  FunnelForPreTraining(config=lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  model(lowercase		,	attention_mask=lowercase		,	token_type_ids=lowercase		,	labels=lowercase			)
  self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Optional[Any]:
  __UpperCamelCase				      =  FunnelForMaskedLM(config=lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  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    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Any:
  __UpperCamelCase				      =  self.num_labels
  __UpperCamelCase				      =  FunnelForSequenceClassification(lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  model(lowercase		,	attention_mask=lowercase		,	token_type_ids=lowercase		,	labels=lowercase			)
  self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							int:
  __UpperCamelCase				      =  self.num_choices
  __UpperCamelCase				      =  FunnelForMultipleChoice(config=lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  input_ids.unsqueeze(1			).expand(-1		,	self.num_choices		,	-1			).contiguous()
  __UpperCamelCase				      =  token_type_ids.unsqueeze(1			).expand(-1		,	self.num_choices		,	-1			).contiguous()
  __UpperCamelCase				      =  input_mask.unsqueeze(1			).expand(-1		,	self.num_choices		,	-1			).contiguous()
  __UpperCamelCase				      =  model(
      lowercase		,	attention_mask=lowercase		,	token_type_ids=lowercase		,	labels=lowercase		,	)
  self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_choices)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Optional[Any]:
  __UpperCamelCase				      =  self.num_labels
  __UpperCamelCase				      =  FunnelForTokenClassification(config=lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  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    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Any:
  __UpperCamelCase				      =  FunnelForQuestionAnswering(config=lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  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    __lowerCamelCase				(  self			)  ->							Any:
  __UpperCamelCase				      =  self.prepare_config_and_inputs()
  (
      (
      __UpperCamelCase
  )      ,		(
      __UpperCamelCase
  )      ,		(
      __UpperCamelCase
  )      ,		(
      __UpperCamelCase
  )      ,		(
      __UpperCamelCase
  )      ,		(
      __UpperCamelCase
  )      ,		(
      __UpperCamelCase
  )      ,		(
      __UpperCamelCase
  )      ,		
  )				      =  config_and_inputs
  __UpperCamelCase				      =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
  return config, inputs_dict


@require_torch
class        UpperCAmelCase__  (    UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase):
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				(
     (
         FunnelModel,
         FunnelForMaskedLM,
         FunnelForPreTraining,
         FunnelForQuestionAnswering,
         FunnelForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __SCREAMING_SNAKE_CASE					      =				(
     {
         '''feature-extraction''': (FunnelBaseModel, FunnelModel),
         '''fill-mask''': FunnelForMaskedLM,
         '''question-answering''': FunnelForQuestionAnswering,
         '''text-classification''': FunnelForSequenceClassification,
         '''token-classification''': FunnelForTokenClassification,
         '''zero-shot''': FunnelForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase=False			)  ->							Tuple:
  __UpperCamelCase				      =  super()._prepare_for_class(lowercase		,	lowercase		,	return_labels=lowercase			)

  if return_labels:
   if model_class in get_values(lowercase			):
    __UpperCamelCase				      =  torch.zeros(
        (self.model_tester.batch_size, self.model_tester.seq_length)		,	dtype=torch.long		,	device=lowercase			)
  return inputs_dict



 def    __lowerCamelCase				(  self			)  ->							List[str]:
  __UpperCamelCase				      =  FunnelModelTester(self			)
  __UpperCamelCase				      =  ConfigTester(self		,	config_class=lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Tuple:
  self.config_tester.run_common_tests()



 def    __lowerCamelCase				(  self			)  ->							List[str]:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_pretraining(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							str:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Dict:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Tuple:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*lowercase			)




 def    __lowerCamelCase				(  self		,	lowercase			)  ->							int:
  if hasattr(lowercase		,	"""weight"""			) and module.weight is not None:
   module.weight.data.fill_(3			)
  if hasattr(lowercase		,	"""bias"""			) and module.bias is not None:
   module.bias.data.fill_(3			)

  for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
   if hasattr(lowercase		,	lowercase			) and getattr(lowercase		,	lowercase			) is not None:
    __UpperCamelCase				      =  getattr(lowercase		,	lowercase			)
    weight.data.fill_(3			)


@require_torch
class        UpperCAmelCase__  (    UpperCAmelCase_							,			unittest.TestCase):
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				(
     (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
 )



 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  __UpperCamelCase				      =  FunnelModelTester(self		,	base=lowercase			)
  __UpperCamelCase				      =  ConfigTester(self		,	config_class=lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Any:
  self.config_tester.run_common_tests()



 def    __lowerCamelCase				(  self			)  ->							str:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_base_model(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Any:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Any:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_multiple_choice(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							str:
  __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  __UpperCamelCase				      =  True

  for model_class in self.all_model_classes:
   if model_class.__name__ == "FunnelBaseModel":
    continue
   __UpperCamelCase				      =  model_class(lowercase			)
   model.to(lowercase			)
   model.train()
   __UpperCamelCase				      =  self._prepare_for_class(lowercase		,	lowercase		,	return_labels=lowercase			)
   __UpperCamelCase				      =  model(**lowercase			).loss
   loss.backward()




 def    __lowerCamelCase				(  self		,	lowercase			)  ->							List[Any]:
  if hasattr(lowercase		,	"""weight"""			) and module.weight is not None:
   module.weight.data.fill_(3			)
  if hasattr(lowercase		,	"""bias"""			) and module.bias is not None:
   module.bias.data.fill_(3			)

  for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
   if hasattr(lowercase		,	lowercase			) and getattr(lowercase		,	lowercase			) is not None:
    __UpperCamelCase				      =  getattr(lowercase		,	lowercase			)
    weight.data.fill_(3			)


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



 def    __lowerCamelCase				(  self			)  ->							Tuple:
  __UpperCamelCase				      =  1_3
  __UpperCamelCase				      =  7
  __UpperCamelCase				      =  torch.arange(0		,	batch_size * sequence_length			).long().reshape(lowercase		,	lowercase			)
  __UpperCamelCase				      =  [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
  __UpperCamelCase				      =  torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]			)

  __UpperCamelCase				      =  FunnelModel.from_pretrained("""sgugger/funnel-random-tiny"""			)
  __UpperCamelCase				      =  model(lowercase		,	token_type_ids=lowercase			)[0].abs()

  __UpperCamelCase				      =  torch.tensor(2_344.8_352			)
  __UpperCamelCase				      =  torch.tensor(0.8_052			)
  self.assertTrue(torch.allclose(output.sum()		,	lowercase		,	atol=1E-4			)			)
  self.assertTrue(torch.allclose(output.mean()		,	lowercase		,	atol=1E-4			)			)

  __UpperCamelCase				      =  torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]			)
  __UpperCamelCase				      =  model(lowercase		,	attention_mask=lowercase		,	token_type_ids=lowercase			)[0].abs()

  __UpperCamelCase				      =  torch.tensor(2_343.8_425			)
  __UpperCamelCase				      =  torch.tensor(0.8_049			)
  self.assertTrue(torch.allclose(output.sum()		,	lowercase		,	atol=1E-4			)			)
  self.assertTrue(torch.allclose(output.mean()		,	lowercase		,	atol=1E-4			)			)




 @slow
 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  __UpperCamelCase				      =  FunnelTokenizer.from_pretrained("""huggingface/funnel-small"""			)
  __UpperCamelCase				      =  FunnelModel.from_pretrained("""huggingface/funnel-small"""			)
  __UpperCamelCase				      =  tokenizer("""Hello! I am the Funnel Transformer model."""		,	return_tensors="""pt"""			)
  __UpperCamelCase				      =  model(**lowercase			)[0]

  __UpperCamelCase				      =  torch.tensor(235.7_246			)
  __UpperCamelCase				      =  torch.tensor(0.0_256			)
  self.assertTrue(torch.allclose(output.sum()		,	lowercase		,	atol=1E-4			)			)
  self.assertTrue(torch.allclose(output.mean()		,	lowercase		,	atol=1E-4			)			)










'''simple docstring'''



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def _lowercase   (  __A							,__A							,__A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
 print(f"Building PyTorch model from configuration: {config}"       )
 __UpperCamelCase				      =  TaForConditionalGeneration(__A       )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(__A							,__A							,__A       )

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"       )
 model.save_pretrained(__A       )


if __name__ == "__main__":
 a__						:			int										=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help=(
         'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 a__						:			Optional[Any]										=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





