






"""simple docstring"""


import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
       import torch

       from transformers import BlipTextModel
       from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class    snake_case						:


   '''simple docstring'''

   def __init__(      self					:	Dict,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	str=12,			_lowerCamelCase					:	Optional[int]=7,			_lowerCamelCase					:	Union[str, Any]=True,			_lowerCamelCase					:	Dict=True,			_lowerCamelCase					:	Any=True,			_lowerCamelCase					:	Optional[int]=99,			_lowerCamelCase					:	List[Any]=32,			_lowerCamelCase					:	Optional[Any]=32,			_lowerCamelCase					:	str=5,			_lowerCamelCase					:	Any=4,			_lowerCamelCase					:	Optional[int]=37,			_lowerCamelCase					:	Any=0.1,			_lowerCamelCase					:	Optional[Any]=0.1,			_lowerCamelCase					:	int=5_12,			_lowerCamelCase					:	List[str]=0.02,			_lowerCamelCase					:	str=0,			_lowerCamelCase					:	List[str]=None,			):

     '''simple docstring'''
     __A		     =					parent
     __A		     =					batch_size
     __A		     =					seq_length
     __A		     =					is_training
     __A		     =					use_input_mask
     __A		     =					use_labels
     __A		     =					vocab_size
     __A		     =					hidden_size
     __A		     =					projection_dim
     __A		     =					num_hidden_layers
     __A		     =					num_attention_heads
     __A		     =					intermediate_size
     __A		     =					dropout
     __A		     =					attention_dropout
     __A		     =					max_position_embeddings
     __A		     =					initializer_range
     __A		     =					scope
     __A		     =					bos_token_id


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					ids_tensor([self.batch_size, self.seq_length],			self.vocab_size    )

     __A		     =					None
     if self.use_input_mask:
       __A		     =					random_attention_mask([self.batch_size, self.seq_length]    )

     if input_mask is not None:
       __A     ,       __A		     =					input_mask.shape
       __A		     =					np.random.randint(1,			seq_length - 1,			size=(batch_size,)    )
       for batch_idx, start_index in enumerate(_lowerCamelCase    ):
         __A		     =					1
         __A		     =					0

     __A		     =					self.get_config()

     return config, input_ids, input_mask


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     return BlipTextConfig(
         vocab_size=self.vocab_size,			hidden_size=self.hidden_size,			projection_dim=self.projection_dim,			num_hidden_layers=self.num_hidden_layers,			num_attention_heads=self.num_attention_heads,			intermediate_size=self.intermediate_size,			dropout=self.dropout,			attention_dropout=self.attention_dropout,			max_position_embeddings=self.max_position_embeddings,			initializer_range=self.initializer_range,			bos_token_id=self.bos_token_id,			)


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	str    ):

     '''simple docstring'''
     __A		     =					BlipTextModel(config=_lowerCamelCase    )
     model.to(_lowerCamelCase    )
     model.eval()
     with torch.no_grad():
       __A		     =					model(_lowerCamelCase,			attention_mask=_lowerCamelCase    )
       __A		     =					model(_lowerCamelCase    )
     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 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.prepare_config_and_inputs()
     __A     ,       __A     ,       __A		     =					config_and_inputs
     __A		     =					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
     return config, inputs_dict





@require_torch
class    snake_case						(				_lowerCAmelCase  ,      unittest.TestCase		):


   '''simple docstring'''

   A_		:      str							=					(BlipTextModel,) if is_torch_available() else ()
   A_		:      Optional[int]							=					False
   A_		:      int							=					False
   A_		:      List[str]							=					False


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					BlipTextModelTester(self    )
     __A		     =					ConfigTester(self,			config_class=_lowerCamelCase,			hidden_size=37    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     self.config_tester.run_common_tests()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCamelCase    )


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

     '''simple docstring'''
     pass


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     pass


   @unittest.skip(reason='''Blip does not use inputs_embeds'''    )
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     pass


   @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''    )
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     pass


   @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''    )
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     pass


   @slow
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __A		     =					BlipTextModel.from_pretrained(_lowerCamelCase    )
       self.assertIsNotNone(_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     super().test_pt_tf_model_equivalence(allow_missing_keys=_lowerCamelCase    )












"""simple docstring"""


import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					RoFormerConfig.from_json_file(__UpperCamelCase					)
  print(f'Building PyTorch model from configuration: {config}'					)
  __A		     =					RoFormerForMaskedLM(__UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_roformer(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}'					)
  torch.save(model.state_dict()						,			__UpperCamelCase						,			_use_new_zipfile_serialization=__UpperCamelCase					)


if __name__ == "__main__":
       lowercase_								=	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(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       lowercase_								=	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




