





'''simple docstring'''



import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def       _lowerCAmelCase  ( lowerCamelCase_	:     Dict						,				lowerCamelCase_	:     Union[str, Any] ):
			__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       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     Dict ):
			copy_linear(hf_mlp.fca						,				pt_mlp.c_fc )
			copy_linear(hf_mlp.fca						,				pt_mlp.c_proj )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[Any]						,				lowerCamelCase_	:     str ):
			__lowercase   =							pt_linear.weight
			__lowercase   =							pt_linear.bias




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     int ):
			# copy layer norms
			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       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     List[str] ):
			for hf_layer, pt_layer in zip(lowerCamelCase_						,				lowerCamelCase_ ):
						copy_layer(lowerCamelCase_						,				lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     Dict ):
			# copy  embeds
			__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       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     Dict ):
			# copy projection
			__lowercase   =							pt_model.text_projection.data.T

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




def       _lowerCAmelCase  ( lowerCamelCase_	:     str						,				lowerCamelCase_	:     Dict ):
			# copy projection
			__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       _lowerCAmelCase  ( lowerCamelCase_	:     int						,				lowerCamelCase_	:     Any						,				lowerCamelCase_	:     str=None ):
			if config_path is not None:
						__lowercase   =							CLIPConfig.from_pretrained(lowerCamelCase_ )
			else:
						__lowercase   =							CLIPConfig(projection_dim=5_1_2						,				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						,				7_7 ).unsqueeze(0 )
			__lowercase   =							torch.randn(1						,				3						,				2_2_4						,				2_2_4 )

			__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)









from __future__ import annotations

from collections.abc import Callable







def   lowerCamelCase(      a__		,a__		,a__		,a__ = 100		,):
				_SCREAMING_SNAKE_CASE	     =x_start
				_SCREAMING_SNAKE_CASE	     =fnc(a__)
				_SCREAMING_SNAKE_CASE	     =0.0

				for _ in range(a__):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_SCREAMING_SNAKE_CASE	     =(x_end - x_start) / steps + xa
								_SCREAMING_SNAKE_CASE	     =fnc(a__)
								area += abs(fxa + fxa) * (xa - xa) / 2

								# Increment step
								_SCREAMING_SNAKE_CASE	     =xa
								_SCREAMING_SNAKE_CASE	     =fxa
				return area


if __name__ == "__main__":







							def   lowerCamelCase(      a__):
											return x**3

							print('''f(x) = x^3''')
							print('''The area between the curve, x = -10, x = 10 and the x axis is:''')
							snake_case_  :	Any							  =							10
							while i <= 10_00_00:
														snake_case_  :	Tuple							  =							trapezoidal_area(f, -5, 5, i)
														print(f"""with {i} steps: {area}""")
														i *= 10