



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 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	'char'
		SCREAMING_SNAKE_CASE__								=	'bpe'
		SCREAMING_SNAKE_CASE__								=	'wp'



_lowerCAmelCase	:   Union[str, Any]    =   (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	['image_processor', 'char_tokenizer']
		SCREAMING_SNAKE_CASE__								=	'ViTImageProcessor'
		SCREAMING_SNAKE_CASE__								=	'MgpstrTokenizer'
		def __init__(							self    ,a_=None    ,a_=None    ,**a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None
						if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'    ,a_    ,)
										lowerCAmelCase__		  =  kwargs.pop('feature_extractor'						)

						lowerCAmelCase__		  =  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`.'						)

						lowerCAmelCase__		  =  tokenizer
						lowerCAmelCase__		  =  AutoTokenizer.from_pretrained('gpt2'						)
						lowerCAmelCase__		  =  AutoTokenizer.from_pretrained('bert-base-uncased'						)

						super().__init__(a_    ,a_						)
		def __call__(							self    ,a_=None    ,a_=None    ,a_=None    ,**a_						):







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

						if text is None:
										return inputs
						elif images is None:
										return encodings
						else:
										lowerCAmelCase__		  =  encodings['input_ids']
										return inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  sequences
						lowerCAmelCase__		  =  char_preds.size(0						)

						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self._decode_helper(a_    ,'char'						)
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self._decode_helper(a_    ,'bpe'						)
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self._decode_helper(a_    ,'wp'						)

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

						lowerCAmelCase__		  =  {}
						lowerCAmelCase__		  =  final_strs
						lowerCAmelCase__		  =  final_scores
						lowerCAmelCase__		  =  char_strs
						lowerCAmelCase__		  =  bpe_strs
						lowerCAmelCase__		  =  wp_strs
						return out
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_						):







						"""simple docstring"""
						if format == DecodeType.CHARACTER:
										lowerCAmelCase__		  =  self.char_decode
										lowerCAmelCase__		  =  1
										lowerCAmelCase__		  =  '[s]'
						elif format == DecodeType.BPE:
										lowerCAmelCase__		  =  self.bpe_decode
										lowerCAmelCase__		  =  2
										lowerCAmelCase__		  =  '#'
						elif format == DecodeType.WORDPIECE:
										lowerCAmelCase__		  =  self.wp_decode
										lowerCAmelCase__		  =  102
										lowerCAmelCase__		  =  '[SEP]'
						else:
										raise ValueError(f'Format {format} is not supported.'						)

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

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

						return dec_strs, conf_scores
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [seq.replace(' '    ,''						) for seq in self.char_tokenizer.batch_decode(a_						)]
						return decode_strs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						return self.bpe_tokenizer.batch_decode(a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [seq.replace(' '    ,''						) for seq in self.wp_tokenizer.batch_decode(a_						)]
						return decode_strs





from typing import TYPE_CHECKING

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


_lowerCAmelCase	:   Any    =   {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   str    =   ["ChineseCLIPFeatureExtractor"]
	_lowerCAmelCase	:   Any    =   ["ChineseCLIPImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   Optional[int]    =   [
	    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ChineseCLIPModel",
	    "ChineseCLIPPreTrainedModel",
	    "ChineseCLIPTextModel",
	    "ChineseCLIPVisionModel",
	]

if TYPE_CHECKING:
	from .configuration_chinese_clip import (
	    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ChineseCLIPConfig,
	    ChineseCLIPOnnxConfig,
	    ChineseCLIPTextConfig,
	    ChineseCLIPVisionConfig,
	)
	from .processing_chinese_clip import ChineseCLIPProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_chinese_clip import (
		    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ChineseCLIPModel,
		    ChineseCLIPPreTrainedModel,
		    ChineseCLIPTextModel,
		    ChineseCLIPVisionModel,
		)

else:
	import sys

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