

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel





def 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    pt_attn_layer.in_proj_weight.chunk(3			,							dim=0						)
    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    pt_attn_layer.in_proj_bias.chunk(3			,							dim=0						)

    SCREAMING_SNAKE_CASE__         =    pt_attn_layer.out_proj.weight
    SCREAMING_SNAKE_CASE__         =    pt_attn_layer.out_proj.bias

    SCREAMING_SNAKE_CASE__         =    q_proj
    SCREAMING_SNAKE_CASE__         =    q_proj_bias

    SCREAMING_SNAKE_CASE__         =    k_proj
    SCREAMING_SNAKE_CASE__         =    k_proj_bias

    SCREAMING_SNAKE_CASE__         =    v_proj
    SCREAMING_SNAKE_CASE__         =    v_proj_bias

    SCREAMING_SNAKE_CASE__         =    out_proj_weights
    SCREAMING_SNAKE_CASE__         =    out_proj_bias





def 				__a							(       __A			,							__A						):
    copy_linear(hf_mlp.fca			,							pt_mlp.c_fc						)
    copy_linear(hf_mlp.fca			,							pt_mlp.c_proj						)





def 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    pt_linear.weight
    SCREAMING_SNAKE_CASE__         =    pt_linear.bias





def 				__a							(       __A			,							__A						):
    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							(       __A			,							__A						):





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





def 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    pt_model.token_embedding.weight
    SCREAMING_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 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    pt_model.text_projection.data.T

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





def 				__a							(       __A			,							__A						):
    SCREAMING_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
    SCREAMING_SNAKE_CASE__         =    pt_model.visual.conva.weight.data
    SCREAMING_SNAKE_CASE__         =    pt_model.visual.class_embedding
    SCREAMING_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 				__a							(       __A			,							__A			,							__A=None						):





    if config_path is not None:
        SCREAMING_SNAKE_CASE__         =    CLIPConfig.from_pretrained(__A						)
    else:
        SCREAMING_SNAKE_CASE__         =    CLIPConfig(projection_dim=512			,							text_config={}			,							vision_config={}						)

    SCREAMING_SNAKE_CASE__         =    CLIPModel(__A						).eval()

    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    load(__A			,							device="cpu"			,							jit=__A						)
    SCREAMING_SNAKE_CASE__         =    pt_model.eval()

    copy_text_model_and_projection(__A			,							__A						)
    copy_vison_model_and_projection(__A			,							__A						)
    SCREAMING_SNAKE_CASE__         =    pt_model.logit_scale

    SCREAMING_SNAKE_CASE__         =    torch.arange(0			,							77						).unsqueeze(0						)
    SCREAMING_SNAKE_CASE__         =    torch.randn(1			,							3			,							224			,							224						)

    SCREAMING_SNAKE_CASE__         =    hf_model(input_ids=__A			,							pixel_values=__A			,							return_dict=__A						)
    SCREAMING_SNAKE_CASE__         =    hf_outputs.logits_per_image
    SCREAMING_SNAKE_CASE__         =    hf_outputs.logits_per_text
    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    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__":
  A__										=      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")
  A__										=      parser.parse_args()

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







from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_lowercase:					List[str]      =						logging.get_logger(__name__)

_lowercase:					str      =						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase:					Union[str, Any]      =						{
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
    "tokenizer_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"},
}

_lowercase:					int      =						{
    "openai-gpt": 512,
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				VOCAB_FILES_NAMES
	__A		      =				PRETRAINED_VOCAB_FILES_MAP
	__A		      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A		      =				["input_ids", "attention_mask"]
	__A		      =				OpenAIGPTTokenizer




	def __init__(self						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_="<unk>"						,			**lowerCamelCase_						):
						"""simple docstring"""

						super().__init__(lowerCamelCase_						,			lowerCamelCase_						,			tokenizer_file=lowerCamelCase_						,			unk_token=lowerCamelCase_						,			**lowerCamelCase_						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return True




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						):
						"""simple docstring"""

						a   	=  self._tokenizer.model.save(lowerCamelCase_						,			name=lowerCamelCase_						)
						return tuple(lowerCamelCase_						)
