

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
					from PIL import Image





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Dict									=				"dandelin/vilt-b32-finetuned-vqa"
			SCREAMING_SNAKE_CASE     :   str									=				(
			    "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
			    "image containing the information, as well as a `question` which should be the question in English. It "
			    "returns a text that is the answer to the question."
			)
			SCREAMING_SNAKE_CASE     :   Any									=				"image_qa"
			SCREAMING_SNAKE_CASE     :   str									=				AutoProcessor
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				AutoModelForVisualQuestionAnswering

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["image", "text"]
			SCREAMING_SNAKE_CASE     :   List[Any]									=				["text"]







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







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				"Image"							,   _UpperCamelCase :				str							)      ->Union[str, Any]:
									return self.pre_processor(_UpperCamelCase							,   _UpperCamelCase							,   return_tensors='''pt'''							)







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Dict							)      ->int:
									with torch.no_grad():
															return self.model(**_UpperCamelCase							).logits







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Tuple							)      ->Tuple:
									snake_case_   =	outputs.argmax(-1							).item()
									return self.model.config.idalabel[idx]


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)