




'''simple docstring'''





import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ViltImageProcessor




class      UpperCamelCase       (	unittest.TestCase       ):



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=7		,     __lowerCAmelCase=3		,     __lowerCAmelCase=18		,     __lowerCAmelCase=30		,     __lowerCAmelCase=400		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     __lowerCAmelCase=2		,     __lowerCAmelCase=True		,     __lowerCAmelCase=[0.5, 0.5, 0.5]		,     __lowerCAmelCase=[0.5, 0.5, 0.5]		,     ):







      '''simple docstring'''


      _a				:							Tuple							       =      size if size is not None else {"""shortest_edge""": 30}
      _a				:							Optional[int]							       =      parent
      _a				:							Union[str, Any]							       =      batch_size
      _a				:							int							       =      num_channels
      _a				:							List[str]							       =      image_size
      _a				:							str							       =      min_resolution
      _a				:							Union[str, Any]							       =      max_resolution
      _a				:							Tuple							       =      do_resize
      _a				:							str							       =      size
      _a				:							Any							       =      size_divisor
      _a				:							List[str]							       =      do_normalize
      _a				:							Tuple							       =      image_mean
      _a				:							str							       =      image_std



  def     a_							(			self ):







      '''simple docstring'''


      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=False ):







      '''simple docstring'''


      if not batched:
          _a				:							List[Any]							       =      self.size["""shortest_edge"""]
          _a				:							Optional[int]							       =      image_inputs[0]
          if isinstance(__lowerCAmelCase		,     Image.Image ):
              _a       , _a				:							Any							       =      image.size
          else:
              _a       , _a				:							Any							       =      image.shape[1], image.shape[2]
          _a				:							int							       =      size / min(__lowerCAmelCase		,     __lowerCAmelCase )
          if h < w:
              _a       , _a				:							str							       =      size, scale * w
          else:
              _a       , _a				:							Any							       =      scale * h, size

          _a				:							List[Any]							       =      int((1333 / 800) * size )
          if max(__lowerCAmelCase		,     __lowerCAmelCase ) > max_size:
              _a				:							Optional[Any]							       =      max_size / max(__lowerCAmelCase		,     __lowerCAmelCase )
              _a				:							Optional[int]							       =      newh * scale
              _a				:							List[str]							       =      neww * scale

          _a       , _a				:							int							       =      int(newh + 0.5 ), int(neww + 0.5 )
          _a       , _a				:							Dict							       =      (
              newh // self.size_divisor * self.size_divisor,
              neww // self.size_divisor * self.size_divisor,
          )

      else:
          _a				:							int							       =      []
          for image in image_inputs:
              _a       , _a				:							Any							       =      self.get_expected_values([image] )
              expected_values.append((expected_height, expected_width) )
          _a				:							Tuple							       =      max(__lowerCAmelCase		,     key=lambda __lowerCAmelCase : item[0] )[0]
          _a				:							List[str]							       =      max(__lowerCAmelCase		,     key=lambda __lowerCAmelCase : item[1] )[1]

      return expected_height, expected_width






@require_torch
@require_vision
class      UpperCamelCase       (	snake_case__      ,		unittest.TestCase       ):
  a     :Any          =					ViltImageProcessor if is_vision_available() else None



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      ViltImageProcessingTester(self )



  @property
  def     a_							(			self ):







      '''simple docstring'''


      return self.image_processor_tester.prepare_image_processor_dict()



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """image_mean""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """image_std""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """do_normalize""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """do_resize""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """size""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """size_divisor""" ) )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size		,     {"""shortest_edge""": 30} )

      _a				:							List[Any]							       =      self.image_processing_class.from_dict(self.image_processor_dict		,     size=42 )
      self.assertEqual(image_processor.size		,     {"""shortest_edge""": 42} )



  def     a_							(			self ):







      '''simple docstring'''


      pass



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      _a				:							int							       =      prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__lowerCAmelCase )
      for image in image_inputs:
          self.assertIsInstance(__lowerCAmelCase		,     Image.Image )

      # Test not batched input
      _a				:							str							       =      image_processing(image_inputs[0]		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							int							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      _a				:							Tuple							       =      image_processing(__lowerCAmelCase		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							int							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase		,     batched=__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      _a				:							Union[str, Any]							       =      prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__lowerCAmelCase		,     numpify=__lowerCAmelCase )
      for image in image_inputs:
          self.assertIsInstance(__lowerCAmelCase		,     np.ndarray )

      # Test not batched input
      _a				:							int							       =      image_processing(image_inputs[0]		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							List[Any]							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      _a				:							List[Any]							       =      image_processing(__lowerCAmelCase		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							List[str]							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase		,     batched=__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							str							       =      self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      _a				:							List[Any]							       =      prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__lowerCAmelCase		,     torchify=__lowerCAmelCase )
      for image in image_inputs:
          self.assertIsInstance(__lowerCAmelCase		,     torch.Tensor )

      # Test not batched input
      _a				:							Optional[int]							       =      image_processing(image_inputs[0]		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							Any							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      _a				:							Optional[int]							       =      image_processing(__lowerCAmelCase		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							List[Any]							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase		,     batched=__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def 						A_			(				snake_case				: List[str]			,   snake_case				: int			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      pt_attn_layer.in_proj_weight.chunk(3			,   dim=0			)
						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      pt_attn_layer.in_proj_bias.chunk(3			,   dim=0			)

						__UpperCamelCase  	=      pt_attn_layer.out_proj.weight
						__UpperCamelCase  	=      pt_attn_layer.out_proj.bias

						__UpperCamelCase  	=      q_proj
						__UpperCamelCase  	=      q_proj_bias

						__UpperCamelCase  	=      k_proj
						__UpperCamelCase  	=      k_proj_bias

						__UpperCamelCase  	=      v_proj
						__UpperCamelCase  	=      v_proj_bias

						__UpperCamelCase  	=      out_proj_weights
						__UpperCamelCase  	=      out_proj_bias




def 						A_			(				snake_case				: Optional[Any]			,   snake_case				: int			)   ->     Any:



						'''simple docstring'''

						copy_linear(hf_mlp.fca			,   pt_mlp.c_fc			)
						copy_linear(hf_mlp.fca			,   pt_mlp.c_proj			)




def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Optional[Any]			)   ->     Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_linear.weight
						__UpperCamelCase  	=      pt_linear.bias




def 						A_			(				snake_case				: Tuple			,   snake_case				: int			)   ->     str:



						'''simple docstring'''

						copy_linear(hf_layer.layer_norma			,   pt_layer.ln_a			)
						copy_linear(hf_layer.layer_norma			,   pt_layer.ln_a			)

						# copy MLP
						copy_mlp(hf_layer.mlp			,   pt_layer.mlp			)

						# copy attn
						copy_attn_layer(hf_layer.self_attn			,   pt_layer.attn			)




def 						A_			(				snake_case				: List[Any]			,   snake_case				: Any			)   ->     Optional[int]:



						'''simple docstring'''

						for hf_layer, pt_layer in zip(snake_case			,   snake_case			):
												copy_layer(snake_case			,   snake_case			)




def 						A_			(				snake_case				: str			,   snake_case				: Dict			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.token_embedding.weight
						__UpperCamelCase  	=      pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm			,   pt_model.ln_final			)

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers			,   pt_model.transformer.resblocks			)




def 						A_			(				snake_case				: str			,   snake_case				: List[str]			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model			,   snake_case			)




def 						A_			(				snake_case				: List[str]			,   snake_case				: Any			)   ->     str:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layrnorm			,   pt_model.visual.ln_pre			)
						copy_linear(hf_model.vision_model.post_layernorm			,   pt_model.visual.ln_post			)

						# copy embeds
						__UpperCamelCase  	=      pt_model.visual.conva.weight.data
						__UpperCamelCase  	=      pt_model.visual.class_embedding
						__UpperCamelCase  	=      pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers			,   pt_model.visual.transformer.resblocks			)




@torch.no_grad()
def 						A_			(				snake_case				: List[Any]			,   snake_case				: int			,   snake_case				: Any=None			)   ->     List[Any]:



						'''simple docstring'''

						if config_path is not None:
												__UpperCamelCase  	=      CLIPConfig.from_pretrained(snake_case			)
						else:
												__UpperCamelCase  	=      CLIPConfig(projection_dim=512			,   text_config={}			,   vision_config={}			)

						__UpperCamelCase  	=      CLIPModel(snake_case			).eval()

						__UpperCamelCase	,				__UpperCamelCase  	=      load(snake_case			,   device='''cpu'''			,   jit=snake_case			)
						__UpperCamelCase  	=      pt_model.eval()

						copy_text_model_and_projection(snake_case			,   snake_case			)
						copy_vison_model_and_projection(snake_case			,   snake_case			)
						__UpperCamelCase  	=      pt_model.logit_scale

						__UpperCamelCase  	=      torch.arange(0			,   77			).unsqueeze(0			)
						__UpperCamelCase  	=      torch.randn(1			,   3			,   224			,   224			)

						__UpperCamelCase  	=      hf_model(input_ids=snake_case			,   pixel_values=snake_case			,   return_dict=snake_case			)
						__UpperCamelCase  	=      hf_outputs.logits_per_image
						__UpperCamelCase  	=      hf_outputs.logits_per_text
						__UpperCamelCase	,				__UpperCamelCase  	=      pt_model(snake_case			,   snake_case			)

						assert torch.allclose(snake_case			,   snake_case			,   atol=1e-3			)
						assert torch.allclose(snake_case			,   snake_case			,   atol=1e-3			)

						hf_model.save_pretrained(snake_case			)


if __name__ == "__main__":
				lowercase__				:				Union[str, Any]						=      argparse.ArgumentParser()
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				lowercase__				:				str						=      parser.parse_args()

				convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




