




'''simple docstring'''




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
    import numpy as np
    import torch

if is_vision_available():
    from PIL import Image

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      List[str]=False       ,						SCREAMING_SNAKE_CASE__			:      List[Any]=False       ,						SCREAMING_SNAKE_CASE__			:      List[Any]=False       ):
 '''simple docstring'''





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 UpperCAmelCase__										=    []
 for i in range(image_processor_tester.batch_size       ):
  if equal_resolution:
   UpperCAmelCase__										=    UpperCAmelCase__										=    image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   UpperCAmelCase__										=    image_processor_tester.min_resolution
   if getattr(SCREAMING_SNAKE_CASE__       ,						"""size_divisor"""       ,						SCREAMING_SNAKE_CASE__       ):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    UpperCAmelCase__										=    max(image_processor_tester.size_divisor       ,						SCREAMING_SNAKE_CASE__       )
   UpperCAmelCase__							,			UpperCAmelCase__										=    np.random.choice(np.arange(SCREAMING_SNAKE_CASE__       ,						image_processor_tester.max_resolution       )       ,						2       )
  image_inputs.append(
      np.random.randint(255       ,						size=(image_processor_tester.num_channels, width, height)       ,						dtype=np.uinta       )       )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  UpperCAmelCase__										=    [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__       ,						0       ,						-1       )       ) for image in image_inputs]

 if torchify:
  UpperCAmelCase__										=    [torch.from_numpy(SCREAMING_SNAKE_CASE__       ) for image in image_inputs]

 return image_inputs

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      List[str]=10       ,						SCREAMING_SNAKE_CASE__			:      int=10       ,						SCREAMING_SNAKE_CASE__			:      Any=False       ,						SCREAMING_SNAKE_CASE__			:      Optional[int]=False       ):
 '''simple docstring'''





 UpperCAmelCase__										=    []
 for i in range(image_processor_tester.num_frames       ):
  video.append(np.random.randint(255       ,						size=(image_processor_tester.num_channels, width, height)       ,						dtype=np.uinta       )       )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  UpperCAmelCase__										=    [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__       ,						0       ,						-1       )       ) for frame in video]

 if torchify:
  UpperCAmelCase__										=    [torch.from_numpy(SCREAMING_SNAKE_CASE__       ) for frame in video]

 return video

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Tuple       ,						SCREAMING_SNAKE_CASE__			:      Optional[Any]=False       ,						SCREAMING_SNAKE_CASE__			:      Any=False       ,						SCREAMING_SNAKE_CASE__			:      Tuple=False       ):
 '''simple docstring'''





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 UpperCAmelCase__										=    []
 for i in range(image_processor_tester.batch_size       ):
  if equal_resolution:
   UpperCAmelCase__										=    UpperCAmelCase__										=    image_processor_tester.max_resolution
  else:
   UpperCAmelCase__							,			UpperCAmelCase__										=    np.random.choice(
       np.arange(image_processor_tester.min_resolution       ,						image_processor_tester.max_resolution       )       ,						2       )
   UpperCAmelCase__										=    prepare_video(
       image_processor_tester=SCREAMING_SNAKE_CASE__       ,						width=SCREAMING_SNAKE_CASE__       ,						height=SCREAMING_SNAKE_CASE__       ,						numpify=SCREAMING_SNAKE_CASE__       ,						torchify=SCREAMING_SNAKE_CASE__       ,						)
  video_inputs.append(SCREAMING_SNAKE_CASE__       )

 return video_inputs







class 						lowerCAmelCase_   :







  '''simple docstring'''







  lowerCAmelCase_				:					Dict				     =						None


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.image_processing_class(**self.image_processor_dict )
   UpperCAmelCase__										=    json.loads(image_processor.to_json_string() )
   for key, value in self.image_processor_dict.items():
    self.assertEqual(obj[key]						,		_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
    UpperCAmelCase__										=    os.path.join(_UpperCAmelCase						,		"""image_processor.json""" )
    image_processor_first.to_json_file(_UpperCAmelCase )
    UpperCAmelCase__										=    self.image_processing_class.from_json_file(_UpperCAmelCase )

   self.assertEqual(image_processor_second.to_dict()						,		image_processor_first.to_dict() )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
    UpperCAmelCase__										=    image_processor_first.save_pretrained(_UpperCAmelCase )[0]
    check_json_file_has_correct_format(_UpperCAmelCase )
    UpperCAmelCase__										=    self.image_processing_class.from_pretrained(_UpperCAmelCase )

   self.assertEqual(image_processor_second.to_dict()						,		image_processor_first.to_dict() )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.image_processing_class()
   self.assertIsNotNone(_UpperCAmelCase )



  @require_torch
  @require_vision
  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   if self.test_cast_dtype is not None:
    # Initialize image_processor
    UpperCAmelCase__										=    self.image_processing_class(**self.image_processor_dict )

    # create random PyTorch tensors
    UpperCAmelCase__										=    prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_UpperCAmelCase						,		torchify=_UpperCAmelCase )

    UpperCAmelCase__										=    image_processor(_UpperCAmelCase						,		return_tensors="""pt""" )
    # for layoutLM compatiblity
    self.assertEqual(encoding.pixel_values.device						,		torch.device("""cpu""" ) )
    self.assertEqual(encoding.pixel_values.dtype						,		torch.floataa )

    UpperCAmelCase__										=    image_processor(_UpperCAmelCase						,		return_tensors="""pt""" ).to(torch.floataa )
    self.assertEqual(encoding.pixel_values.device						,		torch.device("""cpu""" ) )
    self.assertEqual(encoding.pixel_values.dtype						,		torch.floataa )

    UpperCAmelCase__										=    image_processor(_UpperCAmelCase						,		return_tensors="""pt""" ).to("""cpu"""						,		torch.bfloataa )
    self.assertEqual(encoding.pixel_values.device						,		torch.device("""cpu""" ) )
    self.assertEqual(encoding.pixel_values.dtype						,		torch.bfloataa )

    with self.assertRaises(_UpperCAmelCase ):
     UpperCAmelCase__										=    image_processor(_UpperCAmelCase						,		return_tensors="""pt""" ).to(torch.bfloataa						,		"""cpu""" )

    # Try with text + image feature
    UpperCAmelCase__										=    image_processor(_UpperCAmelCase						,		return_tensors="""pt""" )
    encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]] )} )
    UpperCAmelCase__										=    encoding.to(torch.floataa )

    self.assertEqual(encoding.pixel_values.device						,		torch.device("""cpu""" ) )
    self.assertEqual(encoding.pixel_values.dtype						,		torch.floataa )
    self.assertEqual(encoding.input_ids.dtype						,		torch.long )






'''simple docstring'''




import argparse

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


logging.set_verbosity_info()

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ):
 '''simple docstring'''





 UpperCAmelCase__										=    TaConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
 print(F'''Building PyTorch model from configuration: {config}'''       )
 UpperCAmelCase__										=    TaForConditionalGeneration(SCREAMING_SNAKE_CASE__       )

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

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
 model.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
    UpperCAmelCase_       				=      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.'
    )
    UpperCAmelCase_       				=      parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
