
'''simple docstring'''







import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Union[str, Any]   ,   __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						lowercase_		,				lowercase_		,				lowercase_			:  str									= pt_attn_layer.in_proj_weight.chunk(3   ,   dim=0							)
						lowercase_		,				lowercase_		,				lowercase_			:  Dict									= pt_attn_layer.in_proj_bias.chunk(3   ,   dim=0							)

						lowercase_			:  str									= pt_attn_layer.out_proj.weight
						lowercase_			:  List[Any]									= pt_attn_layer.out_proj.bias

						lowercase_			:  List[str]									= q_proj
						lowercase_			:  Dict									= q_proj_bias

						lowercase_			:  int									= k_proj
						lowercase_			:  int									= k_proj_bias

						lowercase_			:  int									= v_proj
						lowercase_			:  int									= v_proj_bias

						lowercase_			:  Optional[Any]									= out_proj_weights
						lowercase_			:  int									= out_proj_bias
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				List[Any]   ,   __SCREAMING_SNAKE_CASE      :				List[Any]							):



						"""simple docstring"""





						copy_linear(hf_mlp.fca   ,   pt_mlp.c_fc							)
						copy_linear(hf_mlp.fca   ,   pt_mlp.c_proj							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				List[Any]							):



						"""simple docstring"""





						lowercase_			:  Union[str, Any]									= pt_linear.weight
						lowercase_			:  Optional[Any]									= pt_linear.bias
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				List[Any]   ,   __SCREAMING_SNAKE_CASE      :				int							):



						"""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_	(       __SCREAMING_SNAKE_CASE      :				Union[str, Any]   ,   __SCREAMING_SNAKE_CASE      :				List[str]							):



						"""simple docstring"""





						for hf_layer, pt_layer in zip(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							):
												copy_layer(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				Optional[Any]							):



						"""simple docstring"""





						lowercase_			:  List[Any]									= pt_model.token_embedding.weight
						lowercase_			:  int									= 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_	(       __SCREAMING_SNAKE_CASE      :				List[str]   ,   __SCREAMING_SNAKE_CASE      :				Dict							):



						"""simple docstring"""





						lowercase_			:  Optional[Any]									= pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model   ,   __SCREAMING_SNAKE_CASE							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				Optional[Any]							):



						"""simple docstring"""





						lowercase_			:  List[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_			:  Tuple									= pt_model.visual.conva.weight.data
						lowercase_			:  Tuple									= pt_model.visual.class_embedding
						lowercase_			:  int									= 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_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				Optional[int]   ,   __SCREAMING_SNAKE_CASE      :				Dict=None							):



						"""simple docstring"""





						if config_path is not None:
												lowercase_			:  Union[str, Any]									= CLIPConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
						else:
												lowercase_			:  List[Any]									= CLIPConfig(projection_dim=512   ,   text_config={}   ,   vision_config={}							)

						lowercase_			:  Optional[int]									= CLIPModel(__SCREAMING_SNAKE_CASE							).eval()

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

						copy_text_model_and_projection(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)
						copy_vison_model_and_projection(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)
						lowercase_			:  Union[str, Any]									= pt_model.logit_scale

						lowercase_			:  Optional[Any]									= torch.arange(0   ,   77							).unsqueeze(0							)
						lowercase_			:  Tuple									= torch.randn(1   ,   3   ,   224   ,   224							)

						lowercase_			:  Optional[int]									= hf_model(input_ids=__SCREAMING_SNAKE_CASE   ,   pixel_values=__SCREAMING_SNAKE_CASE   ,   return_dict=__SCREAMING_SNAKE_CASE							)
						lowercase_			:  Optional[int]									= hf_outputs.logits_per_image
						lowercase_			:  Any									= hf_outputs.logits_per_text
						lowercase_		,				lowercase_			:  List[str]									= pt_model(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)

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

						hf_model.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							_lowercase				:						int												= 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")
							_lowercase				:						Optional[int]												= parser.parse_args()

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





'''simple docstring'''







import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 				lowerCAmelCase__     (						lowerCamelCase_  ):






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = False       ,							__SCREAMING_SNAKE_CASE = False       ,							__SCREAMING_SNAKE_CASE = None       ,							**__SCREAMING_SNAKE_CASE       ,							):


											"""simple docstring"""





											super().__init__(
											    __SCREAMING_SNAKE_CASE       ,							split=__SCREAMING_SNAKE_CASE       ,							features=__SCREAMING_SNAKE_CASE       ,							cache_dir=__SCREAMING_SNAKE_CASE       ,							keep_in_memory=__SCREAMING_SNAKE_CASE       ,							streaming=__SCREAMING_SNAKE_CASE       ,							num_proc=__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE       ,							)
											lowercase_			:  int									= path_or_paths if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) else {self.split: path_or_paths}
											lowercase_			:  List[Any]									= Csv(
											    cache_dir=__SCREAMING_SNAKE_CASE       ,							data_files=__SCREAMING_SNAKE_CASE       ,							features=__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE       ,							)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											if self.streaming:
																	lowercase_			:  List[str]									= self.builder.as_streaming_dataset(split=self.split	)
											# Build regular (map-style) dataset
											else:
																	lowercase_			:  Any									= None
																	lowercase_			:  Optional[Any]									= None
																	lowercase_			:  str									= None
																	lowercase_			:  Optional[int]									= None

																	self.builder.download_and_prepare(
																	    download_config=__SCREAMING_SNAKE_CASE       ,							download_mode=__SCREAMING_SNAKE_CASE       ,							verification_mode=__SCREAMING_SNAKE_CASE       ,							base_path=__SCREAMING_SNAKE_CASE       ,							num_proc=self.num_proc       ,							)
																	lowercase_			:  Tuple									= self.builder.as_dataset(
																	    split=self.split       ,							verification_mode=__SCREAMING_SNAKE_CASE       ,							in_memory=self.keep_in_memory	)
											return dataset







class 				lowerCAmelCase__     :






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							**__SCREAMING_SNAKE_CASE       ,							):


											"""simple docstring"""





											if num_proc is not None and num_proc <= 0:
																	raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''	)

											lowercase_			:  List[Any]									= dataset
											lowercase_			:  Dict									= path_or_buf
											lowercase_			:  int									= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											lowercase_			:  int									= num_proc
											lowercase_			:  Optional[int]									= '''utf-8'''
											lowercase_			:  Optional[Any]									= to_csv_kwargs






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  List[str]									= self.to_csv_kwargs.pop('''path_or_buf'''       ,							__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Optional[Any]									= self.to_csv_kwargs.pop('''header'''       ,							__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Dict									= self.to_csv_kwargs.pop('''index'''       ,							__SCREAMING_SNAKE_CASE	)

											if isinstance(self.path_or_buf       ,							(str, bytes, os.PathLike)	):
																	with open(self.path_or_buf       ,							'''wb+'''	) as buffer:
																							lowercase_			:  Any									= self._write(file_obj=__SCREAMING_SNAKE_CASE       ,							header=__SCREAMING_SNAKE_CASE       ,							index=__SCREAMING_SNAKE_CASE       ,							**self.to_csv_kwargs	)
											else:
																	lowercase_			:  str									= self._write(file_obj=self.path_or_buf       ,							header=__SCREAMING_SNAKE_CASE       ,							index=__SCREAMING_SNAKE_CASE       ,							**self.to_csv_kwargs	)
											return written






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_		,				lowercase_		,				lowercase_		,				lowercase_			:  int									= args

											lowercase_			:  List[str]									= query_table(
											    table=self.dataset.data       ,							key=slice(__SCREAMING_SNAKE_CASE       ,							offset + self.batch_size	)       ,							indices=self.dataset._indices       ,							)
											lowercase_			:  Dict									= batch.to_pandas().to_csv(
											    path_or_buf=__SCREAMING_SNAKE_CASE       ,							header=header if (offset == 0) else False       ,							index=__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)
											return csv_str.encode(self.encoding	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  Dict									= 0

											if self.num_proc is None or self.num_proc == 1:
																	for offset in logging.tqdm(
																	    range(0       ,							len(self.dataset	)       ,							self.batch_size	)       ,							unit='''ba'''       ,							disable=not logging.is_progress_bar_enabled()       ,							desc='''Creating CSV from Arrow format'''       ,							):
																							lowercase_			:  Any									= self._batch_csv((offset, header, index, to_csv_kwargs)	)
																							written += file_obj.write(__SCREAMING_SNAKE_CASE	)

											else:
																	lowercase_		,				lowercase_			:  Union[str, Any]									= len(self.dataset	), self.batch_size
																	with multiprocessing.Pool(self.num_proc	) as pool:
																							for csv_str in logging.tqdm(
																							    pool.imap(
																							        self._batch_csv       ,							[(offset, header, index, to_csv_kwargs) for offset in range(0       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)]       ,							)       ,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size       ,							unit='''ba'''       ,							disable=not logging.is_progress_bar_enabled()       ,							desc='''Creating CSV from Arrow format'''       ,							):
																													written += file_obj.write(__SCREAMING_SNAKE_CASE	)

											return written



