


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Optional[int]		      =		pt_attn_layer.in_proj_weight.chunk(3   ,	dim=0				)
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		pt_attn_layer.in_proj_bias.chunk(3   ,	dim=0				)

						__SCREAMING_SNAKE_CASE						:					List[str]		      =		pt_attn_layer.out_proj.weight
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		pt_attn_layer.out_proj.bias

						__SCREAMING_SNAKE_CASE						:					Any		      =		q_proj
						__SCREAMING_SNAKE_CASE						:					str		      =		q_proj_bias

						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		k_proj
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		k_proj_bias

						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		v_proj
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		v_proj_bias

						__SCREAMING_SNAKE_CASE						:					Dict		      =		out_proj_weights
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		out_proj_bias


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						copy_linear(hf_mlp.fca   ,	pt_mlp.c_fc				)
						copy_linear(hf_mlp.fca   ,	pt_mlp.c_proj				)


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		pt_linear.weight
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		pt_linear.bias


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						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 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):





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


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Dict		      =		pt_model.token_embedding.weight
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		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 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		pt_model.text_projection.data.T

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


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		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
						__SCREAMING_SNAKE_CASE						:					Dict		      =		pt_model.visual.conva.weight.data
						__SCREAMING_SNAKE_CASE						:					int		      =		pt_model.visual.class_embedding
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		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 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__=None				):





						if config_path is not None:
												__SCREAMING_SNAKE_CASE						:					int		      =		CLIPConfig.from_pretrained(A_				)
						else:
												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		CLIPConfig(projection_dim=512   ,	text_config={}   ,	vision_config={}				)

						__SCREAMING_SNAKE_CASE						:					Tuple		      =		CLIPModel(A_				).eval()

						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		load(A_   ,	device='''cpu'''   ,	jit=A_				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		pt_model.eval()

						copy_text_model_and_projection(A_   ,	A_				)
						copy_vison_model_and_projection(A_   ,	A_				)
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		pt_model.logit_scale

						__SCREAMING_SNAKE_CASE						:					Dict		      =		torch.arange(0   ,	77				).unsqueeze(0				)
						__SCREAMING_SNAKE_CASE						:					int		      =		torch.randn(1   ,	3   ,	224   ,	224				)

						__SCREAMING_SNAKE_CASE						:					int		      =		hf_model(input_ids=A_   ,	pixel_values=A_   ,	return_dict=A_				)
						__SCREAMING_SNAKE_CASE						:					Any		      =		hf_outputs.logits_per_image
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		hf_outputs.logits_per_text
						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					List[Any]		      =		pt_model(A_   ,	A_				)

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

						hf_model.save_pretrained(A_				)


if __name__ == "__main__":
						__lowerCAmelCase     :			Tuple      =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')
						__lowerCAmelCase     :			Union[str, Any]      =parser.parse_args()

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




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		c_float


				@property
				def 		__magic_name__(	self      :int     )		->      int:
										return sizeof(self.value     )




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__ = 0				):
						# < 0 is possible if using a dynamic axis
						if dimension <= 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		fixed_dimension

						dimension -= num_token_to_add
						return dimension


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						return num_parameters * dtype.size


def 	_UpperCamelCase							(				lowercase__				):
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

						try:
												return AutoProcessor.from_pretrained(lowercase__				)
						except (ValueError, OSError, KeyError):
												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Tuple		      =		None, None
												try:
																		__SCREAMING_SNAKE_CASE						:					List[Any]		      =		AutoTokenizer.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass
												try:
																		__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		AutoFeatureExtractor.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass

												if tokenizer is not None and feature_extractor is not None:
																		raise ValueError(
																		    F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
												elif tokenizer is None and feature_extractor is None:
																		return None
												elif tokenizer is not None:
																		return tokenizer
												else:
																		return feature_extractor
