

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A    	=			logging.get_logger(__name__)

__A    	=			{
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}



class 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	str		   =       "flaubert"
      UpperCamelCase_    :	Optional[Any]		   =       {
          "hidden_size": "emb_dim",
          "num_attention_heads": "n_heads",
          "num_hidden_layers": "n_layers",
          "n_words": "vocab_size",  # For backward compatibility
      }



      def __init__(    self   :       Optional[Any]    ,      A__   :       Union[str, Any]=False    ,      A__   :       int=0.0    ,      A__   :       Union[str, Any]=30145    ,      A__   :       int=2048    ,      A__   :       Optional[int]=12    ,      A__   :       Union[str, Any]=16    ,      A__   :       Optional[Any]=0.1    ,      A__   :       Union[str, Any]=0.1    ,      A__   :       List[str]=True    ,      A__   :       int=False    ,      A__   :       Any=False    ,      A__   :       Optional[Any]=False    ,      A__   :       Tuple=1    ,      A__   :       Optional[Any]=True    ,      A__   :       List[Any]=512    ,      A__   :       int=2048**-0.5    ,      A__   :       Optional[Any]=1e-12    ,      A__   :       List[Any]=0.02    ,      A__   :       Dict=0    ,      A__   :       List[Any]=1    ,      A__   :       List[str]=2    ,      A__   :       Dict=3    ,      A__   :       Any=5    ,      A__   :       Optional[int]=True    ,      A__   :       int="first"    ,      A__   :       Any=True    ,      A__   :       Union[str, Any]=None    ,      A__   :       Optional[int]=True    ,      A__   :       List[Any]=0.1    ,      A__   :       Any=5    ,      A__   :       Tuple=5    ,      A__   :       Optional[Any]=0    ,      A__   :       List[Any]=0    ,      A__   :       Optional[Any]=2    ,      A__   :       Tuple=0    ,      **A__   :       List[str]    ,      )					->  Tuple:
        _snake_case		=							pre_norm
        _snake_case		=							layerdrop
        _snake_case		=							vocab_size
        _snake_case		=							emb_dim
        _snake_case		=							n_layers
        _snake_case		=							n_heads
        _snake_case		=							dropout
        _snake_case		=							attention_dropout
        _snake_case		=							gelu_activation
        _snake_case		=							sinusoidal_embeddings
        _snake_case		=							causal
        _snake_case		=							asm
        _snake_case		=							n_langs
        _snake_case		=							use_lang_emb
        _snake_case		=							layer_norm_eps
        _snake_case		=							bos_index
        _snake_case		=							eos_index
        _snake_case		=							pad_index
        _snake_case		=							unk_index
        _snake_case		=							mask_index
        _snake_case		=							is_encoder
        _snake_case		=							max_position_embeddings
        _snake_case		=							embed_init_std
        _snake_case		=							init_std
        _snake_case		=							summary_type
        _snake_case		=							summary_use_proj
        _snake_case		=							summary_activation
        _snake_case		=							summary_proj_to_labels
        _snake_case		=							summary_first_dropout
        _snake_case		=							start_n_top
        _snake_case		=							end_n_top
        _snake_case		=							mask_token_id
        _snake_case		=							lang_id

        if "n_words" in kwargs:
          _snake_case		=							kwargs['''n_words''']

        super().__init__(pad_token_id=A__    ,      bos_token_id=A__    ,      **A__	)



class 						lowercase_					(						__lowercase  ):



      @property
      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
          _snake_case		=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
          _snake_case		=							{0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
            ]	)




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Optional[Any]:



  """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    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[str]:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							pt_linear.weight
  _snake_case		=							pt_linear.bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """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    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[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    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  _snake_case		=							pt_model.text_projection.data.T

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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			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    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase=None  )							->			Any:



  """simple docstring"""






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

  _snake_case		=							CLIPModel(_UpperCamelCase  ).eval()

  _snake_case,  _snake_case		=							load(_UpperCamelCase				, device='''cpu'''				, jit=_UpperCamelCase  )
  _snake_case		=							pt_model.eval()

  copy_text_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  copy_vison_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  _snake_case		=							pt_model.logit_scale

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

  _snake_case		=							hf_model(input_ids=_UpperCamelCase				, pixel_values=_UpperCamelCase				, return_dict=_UpperCamelCase  )
  _snake_case		=							hf_outputs.logits_per_image
  _snake_case		=							hf_outputs.logits_per_text
  _snake_case,  _snake_case		=							pt_model(_UpperCamelCase				, _UpperCamelCase  )

  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )
  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )

  hf_model.save_pretrained(_UpperCamelCase  )


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)

