
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							):



	"""simple docstring"""




	a			:Optional[Any]         =	'char'
	a			:List[str]         =	'bpe'
	a			:List[Any]         =	'wp'

__a  			=					(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:List[Any]         =	['image_processor', 'char_tokenizer']
	a			:Optional[int]         =	'ViTImageProcessor'
	a			:int         =	'MgpstrTokenizer'
	def __init__(       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			Dict=None       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=None       ,							**SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     Any:
					lowercase_       =					None
					if "feature_extractor" in kwargs:
									warnings.warn(
									    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
									    ''' instead.'''       ,							SCREAMING_SNAKE_CASE_       ,							)
									lowercase_       =					kwargs.pop('''feature_extractor'''       )

					lowercase_       =					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`.'''       )

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

					super().__init__(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
	def __call__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Dict=None       ,							SCREAMING_SNAKE_CASE_  :			int=None       ,							SCREAMING_SNAKE_CASE_  :			List[str]=None       ,							**SCREAMING_SNAKE_CASE_  :			Any       )       ->     List[Any]:
					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:
									lowercase_       =					self.image_processor(SCREAMING_SNAKE_CASE_       ,							return_tensors=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
					if text is not None:
									lowercase_       =					self.char_tokenizer(SCREAMING_SNAKE_CASE_       ,							return_tensors=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					if text is None:
									return inputs
					elif images is None:
									return encodings
					else:
									lowercase_       =					encodings['''input_ids''']
									return inputs
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     str:
					lowercase_  ,	lowercase_  ,	lowercase_       =					sequences
					lowercase_       =					char_preds.size(0       )

					lowercase_  ,	lowercase_       =					self._decode_helper(SCREAMING_SNAKE_CASE_       ,							'''char'''       )
					lowercase_  ,	lowercase_       =					self._decode_helper(SCREAMING_SNAKE_CASE_       ,							'''bpe'''       )
					lowercase_  ,	lowercase_       =					self._decode_helper(SCREAMING_SNAKE_CASE_       ,							'''wp'''       )

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

					lowercase_       =					{}
					lowercase_       =					final_strs
					lowercase_       =					final_scores
					lowercase_       =					char_strs
					lowercase_       =					bpe_strs
					lowercase_       =					wp_strs
					return out
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Tuple       )       ->     List[str]:
					if format == DecodeType.CHARACTER:
									lowercase_       =					self.char_decode
									lowercase_       =					1
									lowercase_       =					'''[s]'''
					elif format == DecodeType.BPE:
									lowercase_       =					self.bpe_decode
									lowercase_       =					2
									lowercase_       =					'''#'''
					elif format == DecodeType.WORDPIECE:
									lowercase_       =					self.wp_decode
									lowercase_       =					1_0_2
									lowercase_       =					'''[SEP]'''
					else:
									raise ValueError(f'''Format {format} is not supported.'''       )

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

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

					return dec_strs, conf_scores
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     Union[str, Any]:
					lowercase_       =					[seq.replace(''' '''       ,							''''''       ) for seq in self.char_tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       )]
					return decode_strs
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     str:
					return self.bpe_tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Tuple       )       ->     Union[str, Any]:
					lowercase_       =					[seq.replace(''' '''       ,							''''''       ) for seq in self.wp_tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       )]
					return decode_strs






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel




