

import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


__A     :   str    	=			get_logger(__name__)


if TYPE_CHECKING:
 from .builder import DatasetBuilder



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



  """simple docstring"""






  _snake_case		=							importlib.import_module(snake_case__  )

  # TODO(QL): always update the module to add subsequent new authentication
  if hasattr(snake_case__				, '''_patched_for_streaming'''  ) and module._patched_for_streaming:
    return

  def wrap_auth(_UpperCamelCase  ):
    @wraps(snake_case__  )
    def wrapper(*_UpperCamelCase				, **_UpperCamelCase  ):
      return function(*snake_case__				, use_auth_token=snake_case__				, **snake_case__  )

    _snake_case		=							'wrap_auth'
    return wrapper

  # open files in a streaming fashion
  patch_submodule(snake_case__				, '''open'''				, wrap_auth(snake_case__  )  ).start()
  patch_submodule(snake_case__				, '''os.listdir'''				, wrap_auth(snake_case__  )  ).start()
  patch_submodule(snake_case__				, '''os.walk'''				, wrap_auth(snake_case__  )  ).start()
  patch_submodule(snake_case__				, '''glob.glob'''				, wrap_auth(snake_case__  )  ).start()
  # allow to navigate in remote zip files
  patch_submodule(snake_case__				, '''os.path.join'''				, snake_case__  ).start()
  patch_submodule(snake_case__				, '''os.path.dirname'''				, snake_case__  ).start()
  patch_submodule(snake_case__				, '''os.path.basename'''				, snake_case__  ).start()
  patch_submodule(snake_case__				, '''os.path.relpath'''				, snake_case__  ).start()
  patch_submodule(snake_case__				, '''os.path.split'''				, snake_case__  ).start()
  patch_submodule(snake_case__				, '''os.path.splitext'''				, snake_case__  ).start()
  # allow checks on paths
  patch_submodule(snake_case__				, '''os.path.exists'''				, wrap_auth(snake_case__  )  ).start()
  patch_submodule(snake_case__				, '''os.path.isdir'''				, wrap_auth(snake_case__  )  ).start()
  patch_submodule(snake_case__				, '''os.path.isfile'''				, wrap_auth(snake_case__  )  ).start()
  patch_submodule(snake_case__				, '''os.path.getsize'''				, wrap_auth(snake_case__  )  ).start()
  patch_submodule(snake_case__				, '''pathlib.Path'''				, snake_case__  ).start()
  # file readers
  patch_submodule(snake_case__				, '''gzip.open'''				, wrap_auth(snake_case__  )  ).start()
  patch_submodule(snake_case__				, '''numpy.load'''				, wrap_auth(snake_case__  )  ).start()
  patch_submodule(snake_case__				, '''pandas.read_csv'''				, wrap_auth(snake_case__  )				, attrs=['''__version__''']  ).start()
  patch_submodule(snake_case__				, '''pandas.read_excel'''				, wrap_auth(snake_case__  )				, attrs=['''__version__''']  ).start()
  patch_submodule(snake_case__				, '''scipy.io.loadmat'''				, wrap_auth(snake_case__  )				, attrs=['''__version__''']  ).start()
  patch_submodule(snake_case__				, '''xml.etree.ElementTree.parse'''				, wrap_auth(snake_case__  )  ).start()
  patch_submodule(snake_case__				, '''xml.dom.minidom.parse'''				, wrap_auth(snake_case__  )  ).start()
  _snake_case		=							True



def    snake_case_(_UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  extend_module_for_streaming(builder.__module__				, use_auth_token=builder.use_auth_token  )
  # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
  if not builder.__module__.startswith('''datasets.'''  ):  # check that it's not a packaged builder like csv
    for imports in get_imports(inspect.getfile(builder.__class__  )  ):
      if imports[0] == "internal":
        _snake_case		=							imports[1]
        _snake_case		=							'.'.join(builder.__module__.split('''.'''  )[:-1] + [internal_import_name]  )
        extend_module_for_streaming(snake_case__				, use_auth_token=builder.use_auth_token  )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
  from .builder import DatasetBuilder

  _snake_case		=							[
      cls.__module__
      for cls in type(snake_case__  ).__mro__[1:]  # make sure it's not the same module we've already patched
      if issubclass(cls				, snake_case__  ) and cls.__module__ != DatasetBuilder.__module__
  ]  # check it's not a standard builder from datasets.builder
  for module in parent_builder_modules:
    extend_module_for_streaming(snake_case__				, use_auth_token=builder.use_auth_token  )




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)

