
"""simple docstring"""





import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
							import torch
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  str						   =		'''char'''
		__lowercase							:  int						   =		'''bpe'''
		__lowercase							:  Tuple						   =		'''wp'''



__magic_name__       =			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		['''image_processor''', '''char_tokenizer''']
		__lowercase							:  Dict						   =		'''ViTImageProcessor'''
		__lowercase							:  Union[str, Any]						   =		'''MgpstrTokenizer'''




		def __init__(     self							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = None
				if "feature_extractor" in kwargs:
						warnings.warn(
						    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
						    """ instead."""							,  lowerCAmelCase__							,  )
						__SCREAMING_SNAKE_CASE            = kwargs.pop("""feature_extractor""")

				__SCREAMING_SNAKE_CASE            = image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
						raise ValueError("""You need to specify an `image_processor`.""")
				if tokenizer is None:
						raise ValueError("""You need to specify a `tokenizer`.""")

				__SCREAMING_SNAKE_CASE            = tokenizer
				__SCREAMING_SNAKE_CASE            = AutoTokenizer.from_pretrained("""gpt2""")
				__SCREAMING_SNAKE_CASE            = AutoTokenizer.from_pretrained("""bert-base-uncased""")

				super().__init__(lowerCAmelCase__							,  lowerCAmelCase__)




		def __call__(     self							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  **lowerCAmelCase__):
				if images is None and text is None:
						raise ValueError("""You need to specify either an `images` or `text` input to process.""")

				if images is not None:
						__SCREAMING_SNAKE_CASE            = self.image_processor(lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  **lowerCAmelCase__)
				if text is not None:
						__SCREAMING_SNAKE_CASE            = self.char_tokenizer(lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  **lowerCAmelCase__)

				if text is None:
						return inputs
				elif images is None:
						return encodings
				else:
						__SCREAMING_SNAKE_CASE            = encodings["""input_ids"""]
						return inputs




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = sequences
				__SCREAMING_SNAKE_CASE            = char_preds.size(0)

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = self._decode_helper(lowerCAmelCase__							,  """char""")
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = self._decode_helper(lowerCAmelCase__							,  """bpe""")
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = self._decode_helper(lowerCAmelCase__							,  """wp""")

				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = []
				for i in range(lowerCAmelCase__):
						__SCREAMING_SNAKE_CASE            = [char_scores[i], bpe_scores[i], wp_scores[i]]
						__SCREAMING_SNAKE_CASE            = [char_strs[i], bpe_strs[i], wp_strs[i]]
						__SCREAMING_SNAKE_CASE            = scores.index(max(lowerCAmelCase__))
						final_strs.append(strs[max_score_index])
						final_scores.append(scores[max_score_index])

				__SCREAMING_SNAKE_CASE            = {}
				__SCREAMING_SNAKE_CASE            = final_strs
				__SCREAMING_SNAKE_CASE            = final_scores
				__SCREAMING_SNAKE_CASE            = char_strs
				__SCREAMING_SNAKE_CASE            = bpe_strs
				__SCREAMING_SNAKE_CASE            = wp_strs
				return out




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				if format == DecodeType.CHARACTER:
						__SCREAMING_SNAKE_CASE            = self.char_decode
						__SCREAMING_SNAKE_CASE            = 1
						__SCREAMING_SNAKE_CASE            = """[s]"""
				elif format == DecodeType.BPE:
						__SCREAMING_SNAKE_CASE            = self.bpe_decode
						__SCREAMING_SNAKE_CASE            = 2
						__SCREAMING_SNAKE_CASE            = """#"""
				elif format == DecodeType.WORDPIECE:
						__SCREAMING_SNAKE_CASE            = self.wp_decode
						__SCREAMING_SNAKE_CASE            = 1_0_2
						__SCREAMING_SNAKE_CASE            = """[SEP]"""
				else:
						raise ValueError(f"Format {format} is not supported.")

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = [], []
				__SCREAMING_SNAKE_CASE            = pred_logits.size(0)
				__SCREAMING_SNAKE_CASE            = pred_logits.size(1)
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = pred_logits.topk(1							,  dim=-1							,  largest=lowerCAmelCase__							,  sorted=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = preds_index.view(-1							,  lowerCAmelCase__)[:, 1:]
				__SCREAMING_SNAKE_CASE            = decoder(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = torch.nn.functional.softmax(lowerCAmelCase__							,  dim=2).max(dim=2)
				__SCREAMING_SNAKE_CASE            = preds_max_prob[:, 1:]

				for index in range(lowerCAmelCase__):
						__SCREAMING_SNAKE_CASE            = preds_str[index].find(lowerCAmelCase__)
						__SCREAMING_SNAKE_CASE            = preds_str[index][:pred_eos]
						__SCREAMING_SNAKE_CASE            = preds_index[index].cpu().tolist()
						__SCREAMING_SNAKE_CASE            = pred_index.index(lowerCAmelCase__) if eos_token in pred_index else -1
						__SCREAMING_SNAKE_CASE            = preds_max_prob[index][: pred_eos_index + 1]
						__SCREAMING_SNAKE_CASE            = pred_max_prob.cumprod(dim=0)[-1] if pred_max_prob.nelement() != 0 else 0.0
						dec_strs.append(lowerCAmelCase__)
						conf_scores.append(lowerCAmelCase__)

				return dec_strs, conf_scores




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = [seq.replace(""" """							,  """""") for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__)]
				return decode_strs




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				return self.bpe_tokenizer.batch_decode(lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = [seq.replace(""" """							,  """""") for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__)]
				return decode_strs








"""simple docstring"""


import argparse
import os
import re

import packaging.version


A     :   Any			    =			"examples/"
A     :   Optional[Any]			    =			{
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
A     :   Optional[int]			    =			{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
A     :   List[Any]			    =			"README.md"


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			with open(_UpperCamelCase    , "r"    , encoding="utf-8"    , newline="\n"      ) as f:
						__lowerCAmelCase				    =			f.read()
			__lowerCAmelCase    ,	__lowerCAmelCase				    =			REPLACE_PATTERNS[pattern]
			__lowerCAmelCase				    =			replace.replace("VERSION"    , _UpperCamelCase      )
			__lowerCAmelCase				    =			re_pattern.sub(_UpperCamelCase    , _UpperCamelCase      )
			with open(_UpperCamelCase    , "w"    , encoding="utf-8"    , newline="\n"      ) as f:
						f.write(_UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			for folder, directories, fnames in os.walk(_UpperCamelCase      ):
						# Removing some of the folders with non-actively maintained examples from the walk
						if "research_projects" in directories:
									directories.remove("research_projects"      )
						if "legacy" in directories:
									directories.remove("legacy"      )
						for fname in fnames:
									if fname.endswith(".py"      ):
												update_version_in_file(os.path.join(_UpperCamelCase    , _UpperCamelCase      )    , _UpperCamelCase    , pattern="examples"      )


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=False      ):

			'''simple docstring'''




			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )
			if not patch:
						update_version_in_examples(_UpperCamelCase      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"🤗 Transformers currently provides the following architectures"
			__lowerCAmelCase				    =			"1. Want to contribute a new model?"
			with open(_UpperCamelCase    , "r"    , encoding="utf-8"    , newline="\n"      ) as f:
						__lowerCAmelCase				    =			f.readlines()

			# Find the start of the list.
			__lowerCAmelCase				    =			0
			while not lines[start_index].startswith(_start_prompt      ):
						start_index += 1
			start_index += 1

			__lowerCAmelCase				    =			start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt      ):
						if lines[index].startswith("1."      ):
									__lowerCAmelCase				    =			lines[index].replace(
									    "https://huggingface.co/docs/transformers/main/model_doc"    , "https://huggingface.co/docs/transformers/model_doc"    , )
						index += 1

			with open(_UpperCamelCase    , "w"    , encoding="utf-8"    , newline="\n"      ) as f:
						f.writelines(_UpperCamelCase      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			with open(REPLACE_FILES["init"]    , "r"      ) as f:
						__lowerCAmelCase				    =			f.read()
			__lowerCAmelCase				    =			REPLACE_PATTERNS["init"][0].search(_UpperCamelCase      ).groups()[0]
			return packaging.version.parse(_UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase=False      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			get_version()
			if patch and default_version.is_devrelease:
						raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"      )
			if default_version.is_devrelease:
						__lowerCAmelCase				    =			default_version.base_version
			elif patch:
						__lowerCAmelCase				    =			f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
			else:
						__lowerCAmelCase				    =			f"{default_version.major}.{default_version.minor + 1}.0"

			# Now let's ask nicely if that's the right one.
			__lowerCAmelCase				    =			input(f"Which version are you releasing? [{default_version}]"      )
			if len(_UpperCamelCase      ) == 0:
						__lowerCAmelCase				    =			default_version

			print(f"Updating version to {version}."      )
			global_version_update(_UpperCamelCase    , patch=_UpperCamelCase      )
			if not patch:
						print("Cleaning main README, don't forget to run `make fix-copies`."      )
						clean_main_ref_in_model_list()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			get_version()
			__lowerCAmelCase				    =			f"{current_version.major}.{current_version.minor + 1}.0.dev0"
			__lowerCAmelCase				    =			current_version.base_version

			# Check with the user we got that right.
			__lowerCAmelCase				    =			input(f"Which version are we developing now? [{dev_version}]"      )
			if len(_UpperCamelCase      ) == 0:
						__lowerCAmelCase				    =			dev_version

			print(f"Updating version to {version}."      )
			global_version_update(_UpperCamelCase      )
			print("Cleaning main README, don't forget to run `make fix-copies`."      )
			clean_main_ref_in_model_list()


if __name__ == "__main__":
	A     :   Union[str, Any]			    =			argparse.ArgumentParser()
	parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
	parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
	A     :   Dict			    =			parser.parse_args()
	if not args.post_release:
		pre_release_work(patch=args.patch)
	elif args.patch:
		print("Nothing to do after a patch :-)")
	else:
		post_release_work()




