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       lowercase__		(     _UpperCamelCase):
						UpperCamelCase_  =	"""char"""
						UpperCamelCase_  =	"""bpe"""
						UpperCamelCase_  =	"""wp"""






__UpperCamelCase	: int    		=  (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class       lowercase__		(     _UpperCamelCase):
						UpperCamelCase_  =	["""image_processor""", """char_tokenizer"""]
						UpperCamelCase_  =	"""ViTImageProcessor"""
						UpperCamelCase_  =	"""MgpstrTokenizer"""


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       List[Any]=None					,	UpperCamelCase__			:       int=None					,	**UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							None
												if "feature_extractor" in kwargs:
																		warnings.warn(
																		    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																		    ''' instead.'''					,	UpperCamelCase__					,	)
																		SCREAMING_SNAKE_CASE							:					List[str]           =							kwargs.pop('''feature_extractor'''		)

												SCREAMING_SNAKE_CASE							:					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`.'''		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							tokenizer
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							AutoTokenizer.from_pretrained('''gpt2'''		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							AutoTokenizer.from_pretrained('''bert-base-uncased'''		)

												super().__init__(UpperCamelCase__					,	UpperCamelCase__		)


						def __call__( self			:       Any					,	UpperCamelCase__			:       Dict=None					,	UpperCamelCase__			:       List[Any]=None					,	UpperCamelCase__			:       str=None					,	**UpperCamelCase__			:       int		):





												'''simple docstring'''





												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							:					Any           =							self.image_processor(UpperCamelCase__					,	return_tensors=UpperCamelCase__					,	**UpperCamelCase__		)
												if text is not None:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							self.char_tokenizer(UpperCamelCase__					,	return_tensors=UpperCamelCase__					,	**UpperCamelCase__		)

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


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							sequences
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							char_preds.size(0		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							self._decode_helper(UpperCamelCase__					,	'''char'''		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self._decode_helper(UpperCamelCase__					,	'''bpe'''		)
												SCREAMING_SNAKE_CASE							:					Any           =							self._decode_helper(UpperCamelCase__					,	'''wp'''		)

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

												SCREAMING_SNAKE_CASE							:					str           =							{}
												SCREAMING_SNAKE_CASE							:					int           =							final_strs
												SCREAMING_SNAKE_CASE							:					List[str]           =							final_scores
												SCREAMING_SNAKE_CASE							:					int           =							char_strs
												SCREAMING_SNAKE_CASE							:					Any           =							bpe_strs
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							wp_strs
												return out


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												if format == DecodeType.CHARACTER:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							self.char_decode
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							1
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							"[s]"
												elif format == DecodeType.BPE:
																		SCREAMING_SNAKE_CASE							:					int           =							self.bpe_decode
																		SCREAMING_SNAKE_CASE							:					Dict           =							2
																		SCREAMING_SNAKE_CASE							:					str           =							"#"
												elif format == DecodeType.WORDPIECE:
																		SCREAMING_SNAKE_CASE							:					str           =							self.wp_decode
																		SCREAMING_SNAKE_CASE							:					List[str]           =							102
																		SCREAMING_SNAKE_CASE							:					Dict           =							"[SEP]"
												else:
																		raise ValueError(f"""Format {format} is not supported."""		)

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

												for index in range(UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					List[str]           =							preds_str[index].find(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							preds_str[index][:pred_eos]
																		SCREAMING_SNAKE_CASE							:					int           =							preds_index[index].cpu().tolist()
																		SCREAMING_SNAKE_CASE							:					Tuple           =							pred_index.index(UpperCamelCase__		) if eos_token in pred_index else -1
																		SCREAMING_SNAKE_CASE							:					Dict           =							preds_max_prob[index][: pred_eos_index + 1]
																		SCREAMING_SNAKE_CASE							:					int           =							pred_max_prob.cumprod(dim=0		)[-1] if pred_max_prob.nelement() != 0 else 0.0
																		dec_strs.append(UpperCamelCase__		)
																		conf_scores.append(UpperCamelCase__		)

												return dec_strs, conf_scores


						def  __A ( self			:       str					,	UpperCamelCase__			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							[seq.replace(''' '''					,	''''''		) for seq in self.char_tokenizer.batch_decode(UpperCamelCase__		)]
												return decode_strs


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												return self.bpe_tokenizer.batch_decode(UpperCamelCase__		)



						def  __A ( self			:       Any					,	UpperCamelCase__			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							[seq.replace(''' '''					,	''''''		) for seq in self.wp_tokenizer.batch_decode(UpperCamelCase__		)]
												return decode_strs






import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

__UpperCamelCase	: Dict    		=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCamelCase	: Tuple    		=  [file for file in filepaths if file != file.lower()]
if upper_files:
					print(f"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if ' ' in file]
if space_files:
					print(f"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if '-' in file]
if hyphen_files:
					print(f"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(f"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

__UpperCamelCase	: Optional[Any]    		=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)





