





"""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        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					List[str]      					="""char"""
							lowerCamelCase   :					Optional[Any]      					="""bpe"""
							lowerCamelCase   :					int      					="""wp"""
a						: Optional[int]     	=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Any      					=["""image_processor""", """char_tokenizer"""]
							lowerCamelCase   :					int      					="""ViTImageProcessor"""
							lowerCamelCase   :					Any      					="""MgpstrTokenizer"""






							def __init__(   self      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					**lowerCAmelCase__      )	-> Optional[Any]:
								a      :		Dict	=				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__      ,					)
									a      :		Optional[int]	=				kwargs.pop("feature_extractor"      )

								a      :		Optional[Any]	=				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`."      )

								a      :		Tuple	=				tokenizer
								a      :		int	=				AutoTokenizer.from_pretrained("gpt2"      )
								a      :		Tuple	=				AutoTokenizer.from_pretrained("bert-base-uncased"      )

								super().__init__(lowerCAmelCase__      ,					lowerCAmelCase__      )






							def __call__(   self      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					**lowerCAmelCase__      )	-> Optional[int]:
								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:
									a      :		Dict	=				self.image_processor(lowerCAmelCase__      ,					return_tensors=lowerCAmelCase__      ,					**lowerCAmelCase__      )
								if text is not None:
									a      :		Tuple	=				self.char_tokenizer(lowerCAmelCase__      ,					return_tensors=lowerCAmelCase__      ,					**lowerCAmelCase__      )

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






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Dict:
								a,       a,       a      :		str	=				sequences
								a      :		int	=				char_preds.size(0      )

								a,       a      :		Union[str, Any]	=				self._decode_helper(lowerCAmelCase__      ,					"char"      )
								a,       a      :		Dict	=				self._decode_helper(lowerCAmelCase__      ,					"bpe"      )
								a,       a      :		Any	=				self._decode_helper(lowerCAmelCase__      ,					"wp"      )

								a      :		Dict	=				[]
								a      :		int	=				[]
								for i in range(lowerCAmelCase__      ):
									a      :		Union[str, Any]	=				[char_scores[i], bpe_scores[i], wp_scores[i]]
									a      :		Optional[int]	=				[char_strs[i], bpe_strs[i], wp_strs[i]]
									a      :		str	=				scores.index(max(lowerCAmelCase__      )      )
									final_strs.append(strs[max_score_index]      )
									final_scores.append(scores[max_score_index]      )

								a      :		List[str]	=				{}
								a      :		int	=				final_strs
								a      :		List[str]	=				final_scores
								a      :		List[Any]	=				char_strs
								a      :		Tuple	=				bpe_strs
								a      :		Any	=				wp_strs
								return out






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> int:
								if format == DecodeType.CHARACTER:
									a      :		Optional[int]	=				self.char_decode
									a      :		Optional[int]	=				1
									a      :		Optional[int]	=				"[s]"
								elif format == DecodeType.BPE:
									a      :		Any	=				self.bpe_decode
									a      :		Union[str, Any]	=				2
									a      :		str	=				"#"
								elif format == DecodeType.WORDPIECE:
									a      :		Dict	=				self.wp_decode
									a      :		str	=				102
									a      :		Optional[Any]	=				"[SEP]"
								else:
									raise ValueError(f"""Format {format} is not supported."""      )

								a,       a      :		List[Any]	=				[], []
								a      :		Optional[int]	=				pred_logits.size(0      )
								a      :		List[Any]	=				pred_logits.size(1      )
								a,       a      :		List[Any]	=				pred_logits.topk(1      ,					dim=-1      ,					largest=lowerCAmelCase__      ,					sorted=lowerCAmelCase__      )
								a      :		List[str]	=				preds_index.view(-1      ,					lowerCAmelCase__      )[:, 1:]
								a      :		Dict	=				decoder(lowerCAmelCase__      )
								a,       a      :		Dict	=				torch.nn.functional.softmax(lowerCAmelCase__      ,					dim=2      ).max(dim=2      )
								a      :		List[Any]	=				preds_max_prob[:, 1:]

								for index in range(lowerCAmelCase__      ):
									a      :		Optional[int]	=				preds_str[index].find(lowerCAmelCase__      )
									a      :		List[Any]	=				preds_str[index][:pred_eos]
									a      :		Optional[int]	=				preds_index[index].cpu().tolist()
									a      :		Any	=				pred_index.index(lowerCAmelCase__      ) if eos_token in pred_index else -1
									a      :		Dict	=				preds_max_prob[index][: pred_eos_index + 1]
									a      :		List[str]	=				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     __a   (   self      ,					lowerCAmelCase__      )	-> str:
								a      :		List[Any]	=				[seq.replace(" "      ,					""      ) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__      )]
								return decode_strs






							def     __a   (   self      ,					lowerCAmelCase__      )	-> str:
								return self.bpe_tokenizer.batch_decode(lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Dict:
								a      :		Optional[int]	=				[seq.replace(" "      ,					""      ) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__      )]
								return decode_strs







'''simple docstring'''




import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
							from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
							from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
							from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


a   :			str					  =    DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

a   :			int					  =    """main"""
# Default branch name
a   :			Any					  =    """f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"""
# One particular commit (not the top of `main`)
a   :			str					  =    """aaaaaaa"""
# This commit does not exist, so we should 404.

a   :			int					  =    """d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"""
# Sha-1 of config.json on the top of `main`, for checking purposes
a   :			Any					  =    """4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"""



@contextlib.contextmanager
def       __lowerCamelCase  (					)      ->			List[str]:
							print("""Welcome!"""		)
							yield
							print("""Bye!"""		)



@contextlib.contextmanager
def       __lowerCamelCase  (					)      ->			Optional[int]:
							print("""Bonjour!"""		)
							yield
							print("""Au revoir!"""		)

class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			List[Any]:
								# If the spec is missing, importlib would not be able to import the module dynamically.
								assert transformers.__spec__ is not None
								assert importlib.util.find_spec("""transformers"""    ) is not None




class 			UpperCamelCase_ (							unittest.TestCase			):





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Tuple:
								with ContextManagers([]    ):
															print("""Transformers are awesome!"""    )
								# The print statement adds a new line at the end of the output
								self.assertEqual(mock_stdout.getvalue()		,		"""Transformers are awesome!\n"""    )





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Dict:
								with ContextManagers([context_en()]    ):
															print("""Transformers are awesome!"""    )
								# The output should be wrapped with an English welcome and goodbye
								self.assertEqual(mock_stdout.getvalue()		,		"""Welcome!\nTransformers are awesome!\nBye!\n"""    )





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Union[str, Any]:
								with ContextManagers([context_fr(), context_en()]    ):
															print("""Transformers are awesome!"""    )
								# The output should be wrapped with an English and French welcome and goodbye
								self.assertEqual(mock_stdout.getvalue()		,		"""Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"""    )





	@require_torch
	def  _lowercase(  self    )			->			Optional[int]:
								self.assertEqual(find_labels(A    )		,		["""labels"""]    )
								self.assertEqual(find_labels(A    )		,		["""labels""", """next_sentence_label"""]    )
								self.assertEqual(find_labels(A    )		,		["""start_positions""", """end_positions"""]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		["""labels"""]    )





	@require_tf
	def  _lowercase(  self    )			->			int:
								self.assertEqual(find_labels(A    )		,		["""labels"""]    )
								self.assertEqual(find_labels(A    )		,		["""labels""", """next_sentence_label"""]    )
								self.assertEqual(find_labels(A    )		,		["""start_positions""", """end_positions"""]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		["""labels"""]    )






	@require_flax
	def  _lowercase(  self    )			->			Any:
								# Flax models don't have labels
								self.assertEqual(find_labels(A    )		,		[]    )
								self.assertEqual(find_labels(A    )		,		[]    )
								self.assertEqual(find_labels(A    )		,		[]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		[]    )

