import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def SCREAMING_SNAKE_CASE					(  lowercase_:						Tuple		,    lowercase_:						Union[str, Any]					):
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							List[Any]	    =	pt_attn_layer.in_proj_weight.chunk(3		,    dim=0					)
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	pt_attn_layer.in_proj_bias.chunk(3		,    dim=0					)

       _UpperCamelCase  :							int	    =	pt_attn_layer.out_proj.weight
       _UpperCamelCase  :							Dict	    =	pt_attn_layer.out_proj.bias

       _UpperCamelCase  :							Dict	    =	q_proj
       _UpperCamelCase  :							Tuple	    =	q_proj_bias

       _UpperCamelCase  :							Optional[Any]	    =	k_proj
       _UpperCamelCase  :							Any	    =	k_proj_bias

       _UpperCamelCase  :							Optional[int]	    =	v_proj
       _UpperCamelCase  :							Tuple	    =	v_proj_bias

       _UpperCamelCase  :							Union[str, Any]	    =	out_proj_weights
       _UpperCamelCase  :							Any	    =	out_proj_bias


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[Any]		,    lowercase_:						List[str]					):
       copy_linear(hf_mlp.fca		,    pt_mlp.c_fc					)
       copy_linear(hf_mlp.fca		,    pt_mlp.c_proj					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						Union[str, Any]					):
       _UpperCamelCase  :							Tuple	    =	pt_linear.weight
       _UpperCamelCase  :							int	    =	pt_linear.bias


def SCREAMING_SNAKE_CASE					(  lowercase_:						str		,    lowercase_:						Optional[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 SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						int					):
       for hf_layer, pt_layer in zip(lowercase_		,    lowercase_					):
              copy_layer(lowercase_		,    lowercase_					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[Any]		,    lowercase_:						Optional[Any]					):
       # copy  embeds
       _UpperCamelCase  :							List[Any]	    =	pt_model.token_embedding.weight
       _UpperCamelCase  :							str	    =	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					(  lowercase_:						Dict		,    lowercase_:						List[str]					):
       # copy projection
       _UpperCamelCase  :							Union[str, Any]	    =	pt_model.text_projection.data.T

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


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						str					):
       # copy projection
       _UpperCamelCase  :							Optional[int]	    =	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  :							str	    =	pt_model.visual.conva.weight.data
       _UpperCamelCase  :							Optional[int]	    =	pt_model.visual.class_embedding
       _UpperCamelCase  :							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 SCREAMING_SNAKE_CASE					(  lowercase_:						str		,    lowercase_:						Tuple		,    lowercase_:						Optional[Any]=None					):
       if config_path is not None:
              _UpperCamelCase  :							Dict	    =	CLIPConfig.from_pretrained(lowercase_					)
       else:
              _UpperCamelCase  :							Optional[int]	    =	CLIPConfig(projection_dim=512		,    text_config={}		,    vision_config={}					)

       _UpperCamelCase  :							Union[str, Any]	    =	CLIPModel(lowercase_					).eval()

       _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	load(lowercase_		,    device='cpu'		,    jit=lowercase_					)
       _UpperCamelCase  :							Dict	    =	pt_model.eval()

       copy_text_model_and_projection(lowercase_		,    lowercase_					)
       copy_vison_model_and_projection(lowercase_		,    lowercase_					)
       _UpperCamelCase  :							Dict	    =	pt_model.logit_scale

       _UpperCamelCase  :							List[str]	    =	torch.arange(0		,    77					).unsqueeze(0					)
       _UpperCamelCase  :							Optional[Any]	    =	torch.randn(1		,    3		,    224		,    224					)

       _UpperCamelCase  :							Tuple	    =	hf_model(input_ids=lowercase_		,    pixel_values=lowercase_		,    return_dict=lowercase_					)
       _UpperCamelCase  :							str	    =	hf_outputs.logits_per_image
       _UpperCamelCase  :							Tuple	    =	hf_outputs.logits_per_text
       _UpperCamelCase    ,   _UpperCamelCase  :							Optional[Any]	    =	pt_model(lowercase_		,    lowercase_					)

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

       hf_model.save_pretrained(lowercase_					)


if __name__ == "__main__":
 UpperCAmelCase      		=					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''')
 UpperCAmelCase      		=					parser.parse_args()

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




import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput




@dataclass
class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Union[List[PIL.Image.Image], np.ndarray]







class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		int	)			->				Tuple:
        _UpperCamelCase  :							str	    =	CustomOutput(images=np.random.rand(1							,      3							,      4							,      4	)	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							str	    =	CustomOutput(images=[PIL.Image.new('RGB'							,      (4, 4)	)]	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)

 def        __snake_case       (      self							:		Any	)			->				Dict:
        # test output reinitialization with a `dict` for compatibility with `accelerate`
        _UpperCamelCase  :							Tuple	    =	CustomOutput({'images': np.random.rand(1							,      3							,      4							,      4	)}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							int	    =	CustomOutput({'images': [PIL.Image.new('RGB'							,      (4, 4)	)]}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)



