


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xglm import XGLMTokenizer
else:
							SCREAMING_SNAKE_CASE__       			=				None


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__       			=				{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__       			=				{
    """facebook/xglm-564M""": 2_0_4_8,
}







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				VOCAB_FILES_NAMES
							lowerCAmelCase__          =				PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__          =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__          =				["input_ids", "attention_mask"]
							lowerCAmelCase__          =				XGLMTokenizer




							def __init__(  self						,       UpperCAmelCase=None						,       UpperCAmelCase=None						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="<unk>"						,       UpperCAmelCase="<pad>"						,       **UpperCAmelCase						,       )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = 7
								lowercase_			 = [F'<madeupword{i}>' for i in range(self.num_madeup_words  )]

								lowercase_			 = kwargs.get("additional_special_tokens"						,       []  )
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    UpperCAmelCase						,       tokenizer_file=UpperCAmelCase						,       bos_token=UpperCAmelCase						,       eos_token=UpperCAmelCase						,       sep_token=UpperCAmelCase						,       cls_token=UpperCAmelCase						,       unk_token=UpperCAmelCase						,       pad_token=UpperCAmelCase						,       **UpperCAmelCase						,       )

								lowercase_			 = vocab_file
								lowercase_			 = False if not self.vocab_file else True




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
								lowercase_			 = [self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								lowercase_			 = [self.sep_token_id]

								if token_ids_a is None:
									return len(sep + token_ids_a  ) * [0]
								return len(sep + token_ids_a + sep + sep + token_ids_a  ) * [0]




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       Tuple[str]:






								'''simple docstring'''






								if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."  )

								if not os.path.isdir(UpperCAmelCase  ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory.'  )
									return
								lowercase_			 = os.path.join(
								    UpperCAmelCase						,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCAmelCase  ):
									copyfile(self.vocab_file						,       UpperCAmelCase  )

								return (out_vocab_file,)





import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[int]  ,			__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



	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 SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		List[str]  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		Dict  ):







	'''simple docstring'''



	lowercase_			 = pt_linear.weight
	lowercase_			 = pt_linear.bias




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		Any  ):







	'''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 SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		str  ,			__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		str  ):







	'''simple docstring'''



	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 SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		Any  ):







	'''simple docstring'''



	lowercase_			 = pt_model.text_projection.data.T

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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		List[Any]  ):







	'''simple docstring'''



	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 SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		Dict  ,			__lowerCamelCase:		Dict=None  ):







	'''simple docstring'''



	if config_path is not None:
		lowercase_			 = CLIPConfig.from_pretrained(__lowerCamelCase  )
	else:
		lowercase_			 = CLIPConfig(projection_dim=512  ,			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  ,			77  ).unsqueeze(0  )
	lowercase_			 = torch.randn(1  ,			3  ,			224  ,			224  )

	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)

