'''simple docstring'''
import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel







def 							__UpperCamelCase   (				__UpperCAmelCase     :				str   ,      __UpperCAmelCase     :				Optional[Any] )       ->  Dict:
  lowercase							,			lowercase							,			lowercase							:       Dict											=      pt_attn_layer.in_proj_weight.chunk(3   ,      dim=0 )
  lowercase							,			lowercase							,			lowercase							:       Optional[Any]											=      pt_attn_layer.in_proj_bias.chunk(3   ,      dim=0 )

  lowercase							:       List[Any]											=      pt_attn_layer.out_proj.weight
  lowercase							:       Optional[Any]											=      pt_attn_layer.out_proj.bias

  lowercase							:       List[Any]											=      q_proj
  lowercase							:       Dict											=      q_proj_bias

  lowercase							:       Tuple											=      k_proj
  lowercase							:       int											=      k_proj_bias

  lowercase							:       str											=      v_proj
  lowercase							:       Union[str, Any]											=      v_proj_bias

  lowercase							:       List[str]											=      out_proj_weights
  lowercase							:       Dict											=      out_proj_bias







def 							__UpperCamelCase   (				__UpperCAmelCase     :				List[str]   ,      __UpperCAmelCase     :				Optional[int] )       ->  Dict:
  copy_linear(hf_mlp.fca   ,      pt_mlp.c_fc )
  copy_linear(hf_mlp.fca   ,      pt_mlp.c_proj )







def 							__UpperCamelCase   (				__UpperCAmelCase     :				int   ,      __UpperCAmelCase     :				List[str] )       ->  Dict:
  lowercase							:       Optional[Any]											=      pt_linear.weight
  lowercase							:       List[Any]											=      pt_linear.bias







def 							__UpperCamelCase   (				__UpperCAmelCase     :				List[str]   ,      __UpperCAmelCase     :				Tuple )       ->  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 							__UpperCamelCase   (				__UpperCAmelCase     :				int   ,      __UpperCAmelCase     :				Any )       ->  List[Any]:
  for hf_layer, pt_layer in zip(__UpperCAmelCase   ,      __UpperCAmelCase ):
    copy_layer(__UpperCAmelCase   ,      __UpperCAmelCase )







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Tuple   ,      __UpperCAmelCase     :				List[str] )       ->  Any:
  # copy  embeds
  lowercase							:       List[Any]											=      pt_model.token_embedding.weight
  lowercase							:       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 							__UpperCamelCase   (				__UpperCAmelCase     :				List[str]   ,      __UpperCAmelCase     :				List[str] )       ->  str:
  # copy projection
  lowercase							:       Optional[int]											=      pt_model.text_projection.data.T

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







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Tuple   ,      __UpperCAmelCase     :				Optional[int] )       ->  Optional[Any]:
  # copy projection
  lowercase							:       Union[str, Any]											=      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							:       Any											=      pt_model.visual.conva.weight.data
  lowercase							:       List[Any]											=      pt_model.visual.class_embedding
  lowercase							:       Optional[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 							__UpperCamelCase   (				__UpperCAmelCase     :				Union[str, Any]   ,      __UpperCAmelCase     :				List[Any]   ,      __UpperCAmelCase     :				Any=None )       ->  List[str]:
  if config_path is not None:
    lowercase							:       Optional[Any]											=      CLIPConfig.from_pretrained(__UpperCAmelCase )
  else:
    lowercase							:       str											=      CLIPConfig(projection_dim=512   ,      text_config={}   ,      vision_config={} )

  lowercase							:       Dict											=      CLIPModel(__UpperCAmelCase ).eval()

  lowercase							,			lowercase							:       List[str]											=      load(__UpperCAmelCase   ,      device='''cpu'''   ,      jit=__UpperCAmelCase )
  lowercase							:       int											=      pt_model.eval()

  copy_text_model_and_projection(__UpperCAmelCase   ,      __UpperCAmelCase )
  copy_vison_model_and_projection(__UpperCAmelCase   ,      __UpperCAmelCase )
  lowercase							:       Optional[int]											=      pt_model.logit_scale

  lowercase							:       Tuple											=      torch.arange(0   ,      77 ).unsqueeze(0 )
  lowercase							:       List[str]											=      torch.randn(1   ,      3   ,      224   ,      224 )

  lowercase							:       Tuple											=      hf_model(input_ids=__UpperCAmelCase   ,      pixel_values=__UpperCAmelCase   ,      return_dict=__UpperCAmelCase )
  lowercase							:       Optional[Any]											=      hf_outputs.logits_per_image
  lowercase							:       List[Any]											=      hf_outputs.logits_per_text
  lowercase							,			lowercase							:       Tuple											=      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		:			Union[str, 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''')
 _A		:			List[Any]	      =parser.parse_args()

 convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A		:			int	      ={'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:			str	      =[
     '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CvtForImageClassification''',
     '''CvtModel''',
     '''CvtPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:			Optional[Any]	      =[
     '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFCvtForImageClassification''',
     '''TFCvtModel''',
     '''TFCvtPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_cvt import (
      CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
      CvtForImageClassification,
      CvtModel,
      CvtPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_cvt import (
      TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCvtForImageClassification,
      TFCvtModel,
      TFCvtPreTrainedModel,
  )


else:
 import sys

 _A		:			Tuple	      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)