


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel

def  lowerCAmelCase__				(    a__:	Optional[int]    ,						a__:	Dict )						->	str:


							'''simple docstring'''





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

							_UpperCAmelCase					=    pt_attn_layer.out_proj.weight
							_UpperCAmelCase					=    pt_attn_layer.out_proj.bias

							_UpperCAmelCase					=    q_proj
							_UpperCAmelCase					=    q_proj_bias

							_UpperCAmelCase					=    k_proj
							_UpperCAmelCase					=    k_proj_bias

							_UpperCAmelCase					=    v_proj
							_UpperCAmelCase					=    v_proj_bias

							_UpperCAmelCase					=    out_proj_weights
							_UpperCAmelCase					=    out_proj_bias

def  lowerCAmelCase__				(    a__:	Any    ,						a__:	Any )						->	Optional[int]:


							'''simple docstring'''





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

def  lowerCAmelCase__				(    a__:	int    ,						a__:	Optional[int] )						->	int:


							'''simple docstring'''





							_UpperCAmelCase					=    pt_linear.weight
							_UpperCAmelCase					=    pt_linear.bias

def  lowerCAmelCase__				(    a__:	Dict    ,						a__:	Dict )						->	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  lowerCAmelCase__				(    a__:	str    ,						a__:	Dict )						->	List[str]:


							'''simple docstring'''





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

def  lowerCAmelCase__				(    a__:	str    ,						a__:	Tuple )						->	int:


							'''simple docstring'''





							_UpperCAmelCase					=    pt_model.token_embedding.weight
							_UpperCAmelCase					=    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__				(    a__:	Tuple    ,						a__:	str )						->	Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase					=    pt_model.text_projection.data.T

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

def  lowerCAmelCase__				(    a__:	Union[str, Any]    ,						a__:	int )						->	List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=    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					=    pt_model.visual.conva.weight.data
							_UpperCAmelCase					=    pt_model.visual.class_embedding
							_UpperCAmelCase					=    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__				(    a__:	Union[str, Any]    ,						a__:	str    ,						a__:	List[str]=None )						->	Union[str, Any]:


							'''simple docstring'''





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

							_UpperCAmelCase					=    CLIPModel(a__ ).eval()

							_UpperCAmelCase	, _UpperCAmelCase					=    load(a__    ,						device='cpu'    ,						jit=a__ )
							_UpperCAmelCase					=    pt_model.eval()

							copy_text_model_and_projection(a__    ,						a__ )
							copy_vison_model_and_projection(a__    ,						a__ )
							_UpperCAmelCase					=    pt_model.logit_scale

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

							_UpperCAmelCase					=    hf_model(input_ids=a__    ,						pixel_values=a__    ,						return_dict=a__ )
							_UpperCAmelCase					=    hf_outputs.logits_per_image
							_UpperCAmelCase					=    hf_outputs.logits_per_text
							_UpperCAmelCase	, _UpperCAmelCase					=    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__":
			lowerCAmelCase__						:List[Any]         =							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''')
			lowerCAmelCase__						:List[Any]         =							parser.parse_args()

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




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

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


if is_sentencepiece_available():
		from .tokenization_deberta_va import DebertaVaTokenizer
else:
		lowerCAmelCase__						:int         =							None

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

lowerCAmelCase__						:List[str]         =							{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase__						:List[str]         =							{
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

lowerCAmelCase__						:List[str]         =							{
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

lowerCAmelCase__						:Any         =							{
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}

class 	__a   (       UpperCAmelCase    ):
		_a			:						Tuple    			=			VOCAB_FILES_NAMES
		_a			:						int    			=			PRETRAINED_VOCAB_FILES_MAP
		_a			:						Optional[Any]    			=			PRETRAINED_INIT_CONFIGURATION
		_a			:						str    			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a			:						Optional[int]    			=			DebertaVaTokenizer
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[UNK]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[PAD]"    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[MASK]"    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> None:




									"""simple docstring"""





									super().__init__(
									    _SCREAMING_SNAKE_CASE    ,					tokenizer_file=_SCREAMING_SNAKE_CASE    ,					do_lower_case=_SCREAMING_SNAKE_CASE    ,					bos_token=_SCREAMING_SNAKE_CASE    ,					eos_token=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					sep_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					cls_token=_SCREAMING_SNAKE_CASE    ,					mask_token=_SCREAMING_SNAKE_CASE    ,					split_by_punct=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

									_UpperCAmelCase					=    do_lower_case
									_UpperCAmelCase					=    split_by_punct
									_UpperCAmelCase					=    vocab_file
									_UpperCAmelCase					=    False if not self.vocab_file else True
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> str:




									"""simple docstring"""





									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									_UpperCAmelCase					=    [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False		)				-> Any:




									"""simple docstring"""





									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_SCREAMING_SNAKE_CASE    ,					token_ids_a=_SCREAMING_SNAKE_CASE    ,					already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

									if token_ids_a is not None:
																return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									if token_ids_a is None:
																return len(cls + token_ids_a + sep		) * [0]
									return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = 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(_SCREAMING_SNAKE_CASE		):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																return
									_UpperCAmelCase					=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

									return (out_vocab_file,)
