




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  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  '''char'''
						lowerCAmelCase_							=  '''bpe'''
						lowerCAmelCase_							=  '''wp'''







lowercase__							:	List[Any]											=		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ['''image_processor''', '''char_tokenizer''']
						lowerCAmelCase_							=  '''ViTImageProcessor'''
						lowerCAmelCase_							=  '''MgpstrTokenizer'''






						def __init__(		self				:	Union[str, Any]			,      __lowercase				:	int=None			,      __lowercase				:	Dict=None			,      **__lowercase				:	Optional[Any]		):






							"""simple docstring"""






							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."			,      __lowercase			,      )
								snake_case_						       =	kwargs.pop("feature_extractor"		)

							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`."		)

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

							super().__init__(__lowercase			,      __lowercase		)






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






							"""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:
								snake_case_						       =	self.image_processor(__lowercase			,      return_tensors=__lowercase			,      **__lowercase		)
							if text is not None:
								snake_case_						       =	self.char_tokenizer(__lowercase			,      return_tensors=__lowercase			,      **__lowercase		)

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






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	Tuple		):






							"""simple docstring"""






							snake_case_       , snake_case_       , snake_case_						       =	sequences
							snake_case_						       =	char_preds.size(0		)

							snake_case_       , snake_case_						       =	self._decode_helper(__lowercase			,      "char"		)
							snake_case_       , snake_case_						       =	self._decode_helper(__lowercase			,      "bpe"		)
							snake_case_       , snake_case_						       =	self._decode_helper(__lowercase			,      "wp"		)

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

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






						def 			snake_case__		(		self				:	str			,      __lowercase				:	List[Any]			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							if format == DecodeType.CHARACTER:
								snake_case_						       =	self.char_decode
								snake_case_						       =	1
								snake_case_						       =	"[s]"
							elif format == DecodeType.BPE:
								snake_case_						       =	self.bpe_decode
								snake_case_						       =	2
								snake_case_						       =	"#"
							elif format == DecodeType.WORDPIECE:
								snake_case_						       =	self.wp_decode
								snake_case_						       =	1_02
								snake_case_						       =	"[SEP]"
							else:
								raise ValueError(f"Format {format} is not supported."		)

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

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

							return dec_strs, conf_scores






						def 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	[seq.replace(" "			,      ""		) for seq in self.char_tokenizer.batch_decode(__lowercase		)]
							return decode_strs






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	Any		):






							"""simple docstring"""






							return self.bpe_tokenizer.batch_decode(__lowercase		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	[seq.replace(" "			,      ""		) for seq in self.wp_tokenizer.batch_decode(__lowercase		)]
							return decode_strs






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DPTForDepthEstimation",
		    "DPTForSemanticSegmentation",
		    "DPTModel",
		    "DPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

		lowercase__							:	List[Any]											=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
