




'''simple docstring'''





import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def   UpperCamelCase_(			snake_case							: str							,       snake_case							: List[str]  ):

							'''simple docstring'''






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

							snake_case_			      =   pt_attn_layer.out_proj.weight
							snake_case_			      =   pt_attn_layer.out_proj.bias

							snake_case_			      =   q_proj
							snake_case_			      =   q_proj_bias

							snake_case_			      =   k_proj
							snake_case_			      =   k_proj_bias

							snake_case_			      =   v_proj
							snake_case_			      =   v_proj_bias

							snake_case_			      =   out_proj_weights
							snake_case_			      =   out_proj_bias


def   UpperCamelCase_(			snake_case							: int							,       snake_case							: Any  ):

							'''simple docstring'''






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


def   UpperCamelCase_(			snake_case							: Union[str, Any]							,       snake_case							: List[Any]  ):

							'''simple docstring'''






							snake_case_			      =   pt_linear.weight
							snake_case_			      =   pt_linear.bias


def   UpperCamelCase_(			snake_case							: str							,       snake_case							: List[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   UpperCamelCase_(			snake_case							: Tuple							,       snake_case							: Optional[Any]  ):

							'''simple docstring'''






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


def   UpperCamelCase_(			snake_case							: List[str]							,       snake_case							: Union[str, Any]  ):

							'''simple docstring'''






							snake_case_			      =   pt_model.token_embedding.weight
							snake_case_			      =   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_(			snake_case							: Any							,       snake_case							: Any  ):

							'''simple docstring'''






							snake_case_			      =   pt_model.text_projection.data.T

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


def   UpperCamelCase_(			snake_case							: int							,       snake_case							: int  ):

							'''simple docstring'''






							snake_case_			      =   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
							snake_case_			      =   pt_model.visual.conva.weight.data
							snake_case_			      =   pt_model.visual.class_embedding
							snake_case_			      =   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_(			snake_case							: Dict							,       snake_case							: List[str]							,       snake_case							: Optional[int]=None  ):

							'''simple docstring'''






							if config_path is not None:
														snake_case_			      =   CLIPConfig.from_pretrained(snake_case  )
							else:
														snake_case_			      =   CLIPConfig(projection_dim=5_1_2							,       text_config={}							,       vision_config={}  )

							snake_case_			      =   CLIPModel(snake_case  ).eval()

							snake_case_      ,   snake_case_			      =   load(snake_case							,       device="cpu"							,       jit=snake_case  )
							snake_case_			      =   pt_model.eval()

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

							snake_case_			      =   torch.arange(0							,       7_7  ).unsqueeze(0  )
							snake_case_			      =   torch.randn(1							,       3							,       2_2_4							,       2_2_4  )

							snake_case_			      =   hf_model(input_ids=snake_case							,       pixel_values=snake_case							,       return_dict=snake_case  )
							snake_case_			      =   hf_outputs.logits_per_image
							snake_case_			      =   hf_outputs.logits_per_text
							snake_case_      ,   snake_case_			      =   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__":
		_SCREAMING_SNAKE_CASE							:     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")
		_SCREAMING_SNAKE_CASE							:     Union[str, Any]   	=				parser.parse_args()

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










'''simple docstring'''





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils




class 				_snake_case	(			unittest.TestCase							):




			@patch("diffusers.utils.hub_utils.get_full_repo_name"							)
			def      lowerCAmelCase__       (      self       ,							a__							)       ->       None:

										'''simple docstring'''




										snake_case_			      =   "full_repo_name"
										with TemporaryDirectory() as tmpdir:
																	# Dummy args values
																	snake_case_			      =   Mock()
																	snake_case_			      =   tmpdir
																	snake_case_			      =   0
																	snake_case_			      =   "hub_token"
																	snake_case_			      =   "dataset_name"
																	snake_case_			      =   0.0_1
																	snake_case_			      =   100_000
																	snake_case_			      =   10_000
																	snake_case_			      =   0.0_1
																	snake_case_			      =   0.0_2
																	snake_case_			      =   0.0_3
																	snake_case_			      =   0.0_0_0_5
																	snake_case_			      =   0.0_0_0_0_0_1
																	snake_case_			      =   1
																	snake_case_			      =   10
																	snake_case_			      =   0.0_0_1
																	snake_case_			      =   0.1
																	snake_case_			      =   0.2
																	snake_case_			      =   True

																	# Model card mush be rendered and saved
																	diffusers.utils.hub_utils.create_model_card(a__       ,							model_name="model_name"							)
																	self.assertTrue((Path(a__							) / "README.md").is_file()							)




