
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowercase__				:				str						=      logging.get_logger(__name__)


if is_vision_available():
				import PIL






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	['pixel_values']



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = 1 / 255  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> None:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      size if size is not None else {'''height''': 2560, '''width''': 1920}
													if isinstance(SCREAMING_SNAKE_CASE_  ,						(tuple, list)      ):
																			# The previous feature extractor size parameter was in (width, height) format
																			__UpperCamelCase  	=      size[::-1]
													__UpperCamelCase  	=      get_size_dict(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      do_resize
													__UpperCamelCase  	=      size
													__UpperCamelCase  	=      resample
													__UpperCamelCase  	=      do_thumbnail
													__UpperCamelCase  	=      do_align_long_axis
													__UpperCamelCase  	=      do_pad
													__UpperCamelCase  	=      do_rescale
													__UpperCamelCase  	=      rescale_factor
													__UpperCamelCase  	=      do_normalize
													__UpperCamelCase  	=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													__UpperCamelCase  	=      image_std if image_std is not None else IMAGENET_STANDARD_STD



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None      )-> np.ndarray:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      get_image_size(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase	,				__UpperCamelCase  	=      size['''height'''], size['''width''']

													if (output_width < output_height and input_width > input_height) or (
													    output_width > output_height and input_width < input_height
													):
																			__UpperCamelCase  	=      np.rotaa(SCREAMING_SNAKE_CASE_  ,						3      )

													if data_format is not None:
																			__UpperCamelCase  	=      to_channel_dimension_format(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

													return image



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = None  ,						)-> np.ndarray:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      size['''height'''], size['''width''']
													__UpperCamelCase	,				__UpperCamelCase  	=      get_image_size(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      output_width - input_width
													__UpperCamelCase  	=      output_height - input_height

													if random_padding:
																			__UpperCamelCase  	=      np.random.randint(low=0  ,						high=delta_height + 1      )
																			__UpperCamelCase  	=      np.random.randint(low=0  ,						high=delta_width + 1      )
													else:
																			__UpperCamelCase  	=      delta_height // 2
																			__UpperCamelCase  	=      delta_width // 2

													__UpperCamelCase  	=      delta_height - pad_top
													__UpperCamelCase  	=      delta_width - pad_left

													__UpperCamelCase  	=      ((pad_top, pad_bottom), (pad_left, pad_right))
													return pad(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						data_format=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''      )
													return self.pad_image(*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> np.ndarray:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      get_image_size(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase	,				__UpperCamelCase  	=      size['''height'''], size['''width''']

													# We always resize to the smallest of either the input or output size.
													__UpperCamelCase  	=      min(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      min(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

													if height == input_height and width == input_width:
																			return image

													if input_height > input_width:
																			__UpperCamelCase  	=      int(input_width * height / input_height      )
													elif input_width > input_height:
																			__UpperCamelCase  	=      int(input_height * width / input_width      )

													return resize(
													    SCREAMING_SNAKE_CASE_  ,						size=(height, width)  ,						resample=SCREAMING_SNAKE_CASE_  ,						reducing_gap=2.0  ,						data_format=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> np.ndarray:





													'''simple docstring'''






													__UpperCamelCase  	=      get_size_dict(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      min(size['''height''']  ,						size['''width''']      )
													__UpperCamelCase  	=      get_resize_output_image_size(SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_  ,						default_to_square=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      resize(SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_  ,						resample=SCREAMING_SNAKE_CASE_  ,						data_format=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )
													return resized_image



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> List[str]:





													'''simple docstring'''






													return rescale(SCREAMING_SNAKE_CASE_  ,						scale=SCREAMING_SNAKE_CASE_  ,						data_format=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> np.ndarray:





													'''simple docstring'''






													return normalize(SCREAMING_SNAKE_CASE_  ,						mean=SCREAMING_SNAKE_CASE_  ,						std=SCREAMING_SNAKE_CASE_  ,						data_format=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST  ,						**SCREAMING_SNAKE_CASE_  ,						)-> PIL.Image.Image:





													'''simple docstring'''






													__UpperCamelCase  	=      do_resize if do_resize is not None else self.do_resize
													__UpperCamelCase  	=      size if size is not None else self.size
													if isinstance(SCREAMING_SNAKE_CASE_  ,						(tuple, list)      ):
																			# Previous feature extractor had size in (width, height) format
																			__UpperCamelCase  	=      size[::-1]
													__UpperCamelCase  	=      get_size_dict(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      resample if resample is not None else self.resample
													__UpperCamelCase  	=      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
													__UpperCamelCase  	=      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
													__UpperCamelCase  	=      do_pad if do_pad is not None else self.do_pad
													__UpperCamelCase  	=      do_rescale if do_rescale is not None else self.do_rescale
													__UpperCamelCase  	=      rescale_factor if rescale_factor is not None else self.rescale_factor
													__UpperCamelCase  	=      do_normalize if do_normalize is not None else self.do_normalize
													__UpperCamelCase  	=      image_mean if image_mean is not None else self.image_mean
													__UpperCamelCase  	=      image_std if image_std is not None else self.image_std

													__UpperCamelCase  	=      make_list_of_images(SCREAMING_SNAKE_CASE_      )

													if not valid_images(SCREAMING_SNAKE_CASE_      ):
																			raise ValueError(
																			    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																			    '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

													if do_resize and size is None:
																			raise ValueError('''Size must be specified if do_resize is True.'''      )

													if do_rescale and rescale_factor is None:
																			raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

													if do_pad and size is None:
																			raise ValueError('''Size must be specified if do_pad is True.'''      )

													if do_normalize and (image_mean is None or image_std is None):
																			raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

													# All transformations expect numpy arrays.
													__UpperCamelCase  	=      [to_numpy_array(SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_align_long_axis:
																			__UpperCamelCase  	=      [self.align_long_axis(SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_resize:
																			__UpperCamelCase  	=      [self.resize(image=SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_  ,						resample=SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_thumbnail:
																			__UpperCamelCase  	=      [self.thumbnail(image=SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_pad:
																			__UpperCamelCase  	=      [self.pad_image(image=SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_  ,						random_padding=SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_rescale:
																			__UpperCamelCase  	=      [self.rescale(image=SCREAMING_SNAKE_CASE_  ,						scale=SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_normalize:
																			__UpperCamelCase  	=      [self.normalize(image=SCREAMING_SNAKE_CASE_  ,						mean=SCREAMING_SNAKE_CASE_  ,						std=SCREAMING_SNAKE_CASE_      ) for image in images]

													__UpperCamelCase  	=      [to_channel_dimension_format(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) for image in images]

													__UpperCamelCase  	=      {'''pixel_values''': images}
													return BatchFeature(data=SCREAMING_SNAKE_CASE_  ,						tensor_type=SCREAMING_SNAKE_CASE_      )






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)




