

from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

lowerCAmelCase_       	=	logging.get_logger(__name__)





@add_end_docstrings(__A					)
class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				Any							,   *_UpperCamelCase :				int							,   **_UpperCamelCase :				Optional[Any]							)      ->Union[str, Any]:
									super().__init__(*_UpperCamelCase							,   **_UpperCamelCase							)
									self.check_model_type(_UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				Dict=None							,   _UpperCamelCase :				Dict=None							,   _UpperCamelCase :				Optional[int]=None							,   **_UpperCamelCase :				List[str]							)      ->str:
									snake_case_,    snake_case_   =	{}, {}
									if padding is not None:
															snake_case_   =	padding
									if truncation is not None:
															snake_case_   =	truncation
									if top_k is not None:
															snake_case_   =	top_k
									return preprocess_params, {}, postprocess_params







			def __call__(     self :				Optional[Any]							,   _UpperCamelCase :				Union["Image.Image", str]							,   _UpperCamelCase :				str = None							,   **_UpperCamelCase :				Any							)      ->Any:
									if isinstance(_UpperCamelCase							,   (Image.Image, str)							) and isinstance(_UpperCamelCase							,   _UpperCamelCase							):
															snake_case_   =	{'''image''': image, '''question''': question}
									else:
															snake_case_   =	image
									snake_case_   =	super().__call__(_UpperCamelCase							,   **_UpperCamelCase							)
									return results







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Tuple=False							,   _UpperCamelCase :				List[Any]=False							)      ->Optional[Any]:
									snake_case_   =	load_image(inputs['''image''']							)
									snake_case_   =	self.tokenizer(
									    inputs['''question''']							,   return_tensors=self.framework							,   padding=_UpperCamelCase							,   truncation=_UpperCamelCase							)
									snake_case_   =	self.image_processor(images=_UpperCamelCase							,   return_tensors=self.framework							)
									model_inputs.update(_UpperCamelCase							)
									return model_inputs







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				List[Any]							)      ->List[Any]:
									snake_case_   =	self.model(**_UpperCamelCase							)
									return model_outputs







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Optional[int]=5							)      ->Any:
									if top_k > self.model.config.num_labels:
															snake_case_   =	self.model.config.num_labels

									if self.framework == "pt":
															snake_case_   =	model_outputs.logits.sigmoid()[0]
															snake_case_,    snake_case_   =	probs.topk(_UpperCamelCase							)
									else:
															raise ValueError(f'''Unsupported framework: {self.framework}'''							)

									snake_case_   =	scores.tolist()
									snake_case_   =	ids.tolist()
									return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase							,   _UpperCamelCase							)]


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''XGLMTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''XGLMTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''XGLMForCausalLM''',
					    '''XGLMModel''',
					    '''XGLMPreTrainedModel''',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxXGLMForCausalLM''',
					    '''FlaxXGLMModel''',
					    '''FlaxXGLMPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFXGLMForCausalLM''',
					    '''TFXGLMModel''',
					    '''TFXGLMPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm import XGLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm_fast import XGLMTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xglm import (
										    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXGLMForCausalLM,
										    TFXGLMModel,
										    TFXGLMPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)