
'''simple docstring'''
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


lowercase__						=			logging.get_logger(__name__)

lowercase__						=			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase__						=			{
    "vocab_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json",
    },
    "merges_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "openai/clip-vit-base-patch32": (
            "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"
        ),
    },
}

lowercase__						=			{
    "openai/clip-vit-base-patch32": 7_7,
}






class   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		lowerCamelCase           =	VOCAB_FILES_NAMES
		lowerCamelCase           =	PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase           =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase           =	["""input_ids""", """attention_mask"""]
		lowerCamelCase           =	CLIPTokenizer



		def __init__(			self							: List[Any]     ,  UpperCamelCase__							: Dict=None     ,  UpperCamelCase__							: int=None     ,  UpperCamelCase__							: Optional[Any]=None     ,  UpperCamelCase__							: str="<|endoftext|>"     ,  UpperCamelCase__							: List[str]="<|startoftext|>"     ,  UpperCamelCase__							: List[Any]="<|endoftext|>"     ,  UpperCamelCase__							: List[str]="<|endoftext|>"     ,  **UpperCamelCase__							: Union[str, Any]     ,  )	-> Optional[Any]:






				"""simple docstring"""







				super().__init__(
				    UpperCamelCase__     ,  UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

				if not isinstance(self.backend_tokenizer.pre_tokenizer     ,  pre_tokenizers.Sequence			):
						raise ValueError(
						    '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
						    ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
						    ''' to be compatible with this version.The easiest way to do so is'''
						    ''' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
						    ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
						    ''' transformers.'''			)

				self._wrap_decode_method_backend_tokenizer()



		def 			lowerCAmelCase							(			self							: Dict			)	-> str:






				"""simple docstring"""







				snake_case     :					Tuple     =	self.backend_tokenizer.decode

				def new_decode_method(*UpperCamelCase__							: Any     ,  **UpperCamelCase__							: Any			):
						snake_case     :					List[str]     =	orig_decode_method(*UpperCamelCase__     ,  **UpperCamelCase__			)
						snake_case     :					List[Any]     =	text.replace(self.backend_tokenizer.model.end_of_word_suffix     ,  ''' '''			).strip()
						return text

				snake_case     :					Optional[Any]     =	new_decode_method



		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: List[int]     ,  UpperCamelCase__							: Optional[List[int]] = None			)	-> List[int]:






				"""simple docstring"""







				snake_case     :					Tuple     =	[self.bos_token_id]
				snake_case     :					Any     =	[self.eos_token_id]

				if token_ids_a is None:
						return bos_token + token_ids_a + eos_token
				return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token



		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: List[int]     ,  UpperCamelCase__							: Optional[List[int]] = None			)	-> List[int]:






				"""simple docstring"""







				snake_case     :					Optional[int]     =	[self.bos_token_id]
				snake_case     :					Optional[int]     =	[self.eos_token_id]

				if token_ids_a is None:
						return len(bos_token + token_ids_a + eos_token			) * [0]
				return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token			) * [0]



		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Optional[str] = None			)	-> Tuple[str]:






				"""simple docstring"""







				snake_case     :					int     =	self._tokenizer.model.save(UpperCamelCase__     ,  name=UpperCamelCase__			)
				return tuple(UpperCamelCase__			)




'''simple docstring'''
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:


		'''simple docstring'''


		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
		snake_case     :					Any     =	GPTNeoConfig(
		    hidden_size=config_json['''n_embd''']   ,					num_layers=config_json['''n_layer''']   ,					num_heads=config_json['''n_head''']   ,					attention_types=config_json['''attention_types''']   ,					max_position_embeddings=config_json['''n_positions''']   ,					resid_dropout=config_json['''res_dropout''']   ,					embed_dropout=config_json['''embed_dropout''']   ,					attention_dropout=config_json['''attn_dropout''']   ,					)
		print(F'Building PyTorch model from configuration: {config}' )
		snake_case     :					Union[str, Any]     =	GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__ )

		# Load weights from tf checkpoint
		load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )

		# Save pytorch-model
		print(F'Save PyTorch model to {pytorch_dump_path}' )
		model.save_pretrained(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
						lowercase__						=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
						)
						parser.add_argument(
						    "--config_file",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        "The config json file corresponding to the pre-trained mesh-tf model. \n"
						        "This specifies the model architecture."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						lowercase__						=			parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


