

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 ConvNextImageProcessor





class    __UpperCAmelCase	(			unittest.TestCase		):



   def __init__(				self				:	int,						__A				:	Tuple,						__A				:	Optional[Any]=7,						__A				:	Optional[int]=3,						__A				:	Optional[Any]=1_8,						__A				:	Any=3_0,						__A				:	Any=4_0_0,						__A				:	Union[str, Any]=True,						__A				:	Union[str, Any]=None,						__A				:	List[str]=0.8_7_5,						__A				:	Union[str, Any]=True,						__A				:	int=[0.5, 0.5, 0.5],						__A				:	Dict=[0.5, 0.5, 0.5],						):
     UpperCAmelCase			:				Optional[Any]										=					size if size is not None else {'''shortest_edge''': 2_0}
     UpperCAmelCase			:				Union[str, Any]										=					parent
     UpperCAmelCase			:				Dict										=					batch_size
     UpperCAmelCase			:				str										=					num_channels
     UpperCAmelCase			:				Optional[int]										=					image_size
     UpperCAmelCase			:				str										=					min_resolution
     UpperCAmelCase			:				int										=					max_resolution
     UpperCAmelCase			:				str										=					do_resize
     UpperCAmelCase			:				Tuple										=					size
     UpperCAmelCase			:				Optional[Any]										=					crop_pct
     UpperCAmelCase			:				List[Any]										=					do_normalize
     UpperCAmelCase			:				List[Any]										=					image_mean
     UpperCAmelCase			:				List[Any]										=					image_std





   def 			__magic_name__       (				self				:	List[Any]		):
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "crop_pct": self.crop_pct,
     }






@require_torch
@require_vision
class    __UpperCAmelCase	(			snake_case_     ,    unittest.TestCase		):
   UpperCamelCase  	=	ConvNextImageProcessor if is_vision_available() else None



   def 			__magic_name__       (				self				:	Tuple		):
     UpperCAmelCase			:				List[str]										=					ConvNextImageProcessingTester(self		)



   @property
   def 			__magic_name__       (				self				:	Any		):
     return self.image_processor_tester.prepare_image_processor_dict()



   def 			__magic_name__       (				self				:	str		):
     UpperCAmelCase			:				Union[str, Any]										=					self.image_processing_class(**self.image_processor_dict		)
     self.assertTrue(hasattr(__A,						'''do_resize'''		)		)
     self.assertTrue(hasattr(__A,						'''size'''		)		)
     self.assertTrue(hasattr(__A,						'''crop_pct'''		)		)
     self.assertTrue(hasattr(__A,						'''do_normalize'''		)		)
     self.assertTrue(hasattr(__A,						'''image_mean'''		)		)
     self.assertTrue(hasattr(__A,						'''image_std'''		)		)



   def 			__magic_name__       (				self				:	Any		):
     UpperCAmelCase			:				str										=					self.image_processing_class.from_dict(self.image_processor_dict		)
     self.assertEqual(image_processor.size,						{'''shortest_edge''': 2_0}		)

     UpperCAmelCase			:				Union[str, Any]										=					self.image_processing_class.from_dict(self.image_processor_dict,						size=4_2		)
     self.assertEqual(image_processor.size,						{'''shortest_edge''': 4_2}		)



   def 			__magic_name__       (				self				:	Tuple		):
     pass



   def 			__magic_name__       (				self				:	List[str]		):
     UpperCAmelCase			:				int										=					self.image_processing_class(**self.image_processor_dict		)
     # create random PIL images
     UpperCAmelCase			:				Tuple										=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=__A		)
     for image in image_inputs:
       self.assertIsInstance(__A,						Image.Image		)

     # Test not batched input
     UpperCAmelCase			:				Tuple										=					image_processing(image_inputs[0],						return_tensors='''pt'''		).pixel_values
     self.assertEqual(
         encoded_images.shape,						(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''shortest_edge'''],
             self.image_processor_tester.size['''shortest_edge'''],
         ),						)

     # Test batched
     UpperCAmelCase			:				Tuple										=					image_processing(__A,						return_tensors='''pt'''		).pixel_values
     self.assertEqual(
         encoded_images.shape,						(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''shortest_edge'''],
             self.image_processor_tester.size['''shortest_edge'''],
         ),						)



   def 			__magic_name__       (				self				:	Optional[int]		):
     UpperCAmelCase			:				Dict										=					self.image_processing_class(**self.image_processor_dict		)
     # create random numpy tensors
     UpperCAmelCase			:				Optional[int]										=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=__A,						numpify=__A		)
     for image in image_inputs:
       self.assertIsInstance(__A,						np.ndarray		)

     # Test not batched input
     UpperCAmelCase			:				Union[str, Any]										=					image_processing(image_inputs[0],						return_tensors='''pt'''		).pixel_values
     self.assertEqual(
         encoded_images.shape,						(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''shortest_edge'''],
             self.image_processor_tester.size['''shortest_edge'''],
         ),						)

     # Test batched
     UpperCAmelCase			:				Optional[Any]										=					image_processing(__A,						return_tensors='''pt'''		).pixel_values
     self.assertEqual(
         encoded_images.shape,						(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''shortest_edge'''],
             self.image_processor_tester.size['''shortest_edge'''],
         ),						)





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

     # Test not batched input
     UpperCAmelCase			:				List[Any]										=					image_processing(image_inputs[0],						return_tensors='''pt'''		).pixel_values
     self.assertEqual(
         encoded_images.shape,						(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''shortest_edge'''],
             self.image_processor_tester.size['''shortest_edge'''],
         ),						)

     # Test batched
     UpperCAmelCase			:				Dict										=					image_processing(__A,						return_tensors='''pt'''		).pixel_values
     self.assertEqual(
         encoded_images.shape,						(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''shortest_edge'''],
             self.image_processor_tester.size['''shortest_edge'''],
         ),						)









import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[int]  ,			__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



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

	lowercase_			 = pt_attn_layer.out_proj.weight
	lowercase_			 = pt_attn_layer.out_proj.bias

	lowercase_			 = q_proj
	lowercase_			 = q_proj_bias

	lowercase_			 = k_proj
	lowercase_			 = k_proj_bias

	lowercase_			 = v_proj
	lowercase_			 = v_proj_bias

	lowercase_			 = out_proj_weights
	lowercase_			 = out_proj_bias




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		List[str]  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		Dict  ):







	'''simple docstring'''



	lowercase_			 = pt_linear.weight
	lowercase_			 = pt_linear.bias




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		Any  ):







	'''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 SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		str  ,			__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		str  ):







	'''simple docstring'''



	lowercase_			 = pt_model.token_embedding.weight
	lowercase_			 = 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 SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		Any  ):







	'''simple docstring'''



	lowercase_			 = pt_model.text_projection.data.T

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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		List[Any]  ):







	'''simple docstring'''



	lowercase_			 = 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
	lowercase_			 = pt_model.visual.conva.weight.data
	lowercase_			 = pt_model.visual.class_embedding
	lowercase_			 = 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 SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		Dict  ,			__lowerCamelCase:		Dict=None  ):







	'''simple docstring'''



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

	lowercase_			 = CLIPModel(__lowerCamelCase  ).eval()

	lowercase_ ,	lowercase_			 = load(__lowerCamelCase  ,			device="cpu"  ,			jit=__lowerCamelCase  )
	lowercase_			 = pt_model.eval()

	copy_text_model_and_projection(__lowerCamelCase  ,			__lowerCamelCase  )
	copy_vison_model_and_projection(__lowerCamelCase  ,			__lowerCamelCase  )
	lowercase_			 = pt_model.logit_scale

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

	lowercase_			 = hf_model(input_ids=__lowerCamelCase  ,			pixel_values=__lowerCamelCase  ,			return_dict=__lowerCamelCase  )
	lowercase_			 = hf_outputs.logits_per_image
	lowercase_			 = hf_outputs.logits_per_text
	lowercase_ ,	lowercase_			 = pt_model(__lowerCamelCase  ,			__lowerCamelCase  )

	assert torch.allclose(__lowerCamelCase  ,			__lowerCamelCase  ,			atol=1E-3  )
	assert torch.allclose(__lowerCamelCase  ,			__lowerCamelCase  ,			atol=1E-3  )

	hf_model.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				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""")
							SCREAMING_SNAKE_CASE__       			=				parser.parse_args()

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

