






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  :							Dict			  =    logging.get_logger(__name__)

lowercase  :							Optional[int]			  =    {
    '''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Optional[Any]						=					'visual_bert'






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=3_0522		,				_SCREAMING_SNAKE_CASE=768		,				_SCREAMING_SNAKE_CASE=512		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=3072		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=512		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-12		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE=2		,				**_SCREAMING_SNAKE_CASE		,				) ->				Optional[int]:
								super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		,				bos_token_id=_SCREAMING_SNAKE_CASE		,				eos_token_id=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[str]						 =       vocab_size
								snake_case_				:							Optional[Any]						 =       max_position_embeddings
								snake_case_				:							Tuple						 =       hidden_size
								snake_case_				:							List[str]						 =       visual_embedding_dim
								snake_case_				:							str						 =       num_hidden_layers
								snake_case_				:							Any						 =       num_attention_heads
								snake_case_				:							List[str]						 =       intermediate_size
								snake_case_				:							Optional[int]						 =       hidden_act
								snake_case_				:							Any						 =       hidden_dropout_prob
								snake_case_				:							Dict						 =       attention_probs_dropout_prob
								snake_case_				:							Tuple						 =       initializer_range
								snake_case_				:							List[str]						 =       type_vocab_size
								snake_case_				:							int						 =       layer_norm_eps
								snake_case_				:							Optional[Any]						 =       bypass_transformer
								snake_case_				:							Union[str, Any]						 =       special_visual_initialize








from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


lowercase  :							Dict			  =    logging.get_logger(__name__)




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							List[str]						 =       [label.strip() for label in labels.split(","				) if label.strip()]
								return labels






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								if len(_SCREAMING_SNAKE_CASE				) == 0 or len(_SCREAMING_SNAKE_CASE				) == 0:
										raise ValueError("You must include at least one label and at least one sequence."				)
								if hypothesis_template.format(labels[0]				) == hypothesis_template:
										raise ValueError(
										    (
										        "The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. "
										        "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
										    ).format(_SCREAMING_SNAKE_CASE				)				)

								if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							List[str]						 =       [sequences]

								snake_case_				:							List[str]						 =       []
								for sequence in sequences:
										sequence_pairs.extend([[sequence, hypothesis_template.format(_SCREAMING_SNAKE_CASE				)] for label in labels]				)

								return sequence_pairs, sequences




@add_end_docstrings(SCREAMING_SNAKE_CASE__					)
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE=ZeroShotClassificationArgumentHandler()		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								snake_case_				:							Dict						 =       args_parser
								super().__init__(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)
								if self.entailment_id == -1:
										logger.warning(
										    "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to "
										    "-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."				)






						@property
						def       _lowerCAmelCase       (	self				) ->				str:
								for label, ind in self.model.config.labelaid.items():
										if label.lower().startswith("entail"				):
												return ind
								return -1






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=TruncationStrategy.ONLY_FIRST		,				**_SCREAMING_SNAKE_CASE				) ->				Dict:
								snake_case_				:							List[Any]						 =       self.framework
								if self.tokenizer.pad_token is None:
										# Override for tokenizers not supporting padding
										logger.error(
										    "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
										    " `pad_token=eos_token`"				)
										snake_case_				:							List[str]						 =       self.tokenizer.eos_token
								try:
										snake_case_				:							Union[str, Any]						 =       self.tokenizer(
										    _SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				)
								except Exception as e:
										if "too short" in str(_SCREAMING_SNAKE_CASE				):
												# tokenizers might yell that we want to truncate
												# to a value that is not even reached by the input.
												# In that case we don't want to truncate.
												# It seems there's not a really better way to catch that
												# exception.

												snake_case_				:							Union[str, Any]						 =       self.tokenizer(
												    _SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				truncation=TruncationStrategy.DO_NOT_TRUNCATE		,				)
										else:
												raise e

								return inputs






						def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
								if kwargs.get("multi_class"		,				_SCREAMING_SNAKE_CASE				) is not None:
										snake_case_				:							Any						 =       kwargs["multi_class"]
										logger.warning(
										    "The `multi_class` argument has been deprecated and renamed to `multi_label`. "
										    "`multi_class` will be removed in a future version of Transformers."				)
								snake_case_				:							Any						 =       {}
								if "candidate_labels" in kwargs:
										snake_case_				:							Tuple						 =       self._args_parser._parse_labels(kwargs["candidate_labels"]				)
								if "hypothesis_template" in kwargs:
										snake_case_				:							Optional[Any]						 =       kwargs["hypothesis_template"]

								snake_case_				:							Dict						 =       {}
								if "multi_label" in kwargs:
										snake_case_				:							List[Any]						 =       kwargs["multi_label"]
								return preprocess_params, {}, postprocess_params






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				) ->				str:
								if len(_SCREAMING_SNAKE_CASE				) == 0:
										pass
								elif len(_SCREAMING_SNAKE_CASE				) == 1 and "candidate_labels" not in kwargs:
										snake_case_				:							int						 =       args[0]
								else:
										raise ValueError(f'''Unable to understand extra arguments {args}'''				)

								return super().__call__(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE="This example is {}."				) ->				str:
								snake_case_ ,       snake_case_				:							Optional[int]						 =       self._args_parser(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								for i, (candidate_label, sequence_pair) in enumerate(zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)				):
										snake_case_				:							str						 =       self._parse_and_tokenize([sequence_pair]				)

										yield {
										    "candidate_label": candidate_label,
										    "sequence": sequences[0],
										    "is_last": i == len(_SCREAMING_SNAKE_CASE				) - 1,
										    **model_input,
										}






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								snake_case_				:							Optional[Any]						 =       inputs["candidate_label"]
								snake_case_				:							Dict						 =       inputs["sequence"]
								snake_case_				:							Optional[int]						 =       {k: inputs[k] for k in self.tokenizer.model_input_names}
								snake_case_				:							Dict						 =       self.model(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       {
								    "candidate_label": candidate_label,
								    "sequence": sequence,
								    "is_last": inputs["is_last"],
								    **outputs,
								}
								return model_outputs






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=False				) ->				Any:
								snake_case_				:							str						 =       [outputs["candidate_label"] for outputs in model_outputs]
								snake_case_				:							Union[str, Any]						 =       [outputs["sequence"] for outputs in model_outputs]
								snake_case_				:							List[Any]						 =       np.concatenate([output["logits"].numpy() for output in model_outputs]				)
								snake_case_				:							Tuple						 =       logits.shape[0]
								snake_case_				:							List[Any]						 =       len(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							int						 =       N // n
								snake_case_				:							Optional[Any]						 =       logits.reshape((num_sequences, n, -1)				)

								if multi_label or len(_SCREAMING_SNAKE_CASE				) == 1:
										# softmax over the entailment vs. contradiction dim for each label independently
										snake_case_				:							Any						 =       self.entailment_id
										snake_case_				:							List[str]						 =       -1 if entailment_id == 0 else 0
										snake_case_				:							Dict						 =       reshaped_outputs[..., [contradiction_id, entailment_id]]
										snake_case_				:							List[str]						 =       np.exp(_SCREAMING_SNAKE_CASE				) / np.exp(_SCREAMING_SNAKE_CASE				).sum(-1		,				keepdims=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       scores[..., 1]
								else:
										# softmax the "entailment" logits over all candidate labels
										snake_case_				:							List[str]						 =       reshaped_outputs[..., self.entailment_id]
										snake_case_				:							Optional[Any]						 =       np.exp(_SCREAMING_SNAKE_CASE				) / np.exp(_SCREAMING_SNAKE_CASE				).sum(-1		,				keepdims=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							int						 =       list(reversed(scores[0].argsort()				)				)
								return {
								    "sequence": sequences[0],
								    "labels": [candidate_labels[i] for i in top_inds],
								    "scores": scores[0, top_inds].tolist(),
								}
