

# 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


__snake_case							:			int      		= """Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"""



class 					A__(__UpperCAmelCase       ):
						"""simple docstring"""






						_A						:							Optional[Any]									= '''google/flan-t5-base'''
						_A						:							List[Any]									= (
						    '''This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the '''
						    '''text where to find the answer, and `question`, which is the question, and returns the answer to the question.'''
						)
						_A						:							List[str]									= '''text_qa'''
						_A						:							List[Any]									= AutoTokenizer
						_A						:							Any									= AutoModelForSeqaSeqLM

						_A						:							List[Any]									= ['''text''', '''text''']
						_A						:							Tuple									= ['''text''']





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase						)    ->   Tuple:
									a_	:	List[str]          =   QA_PROMPT.format(text=lowercase_     ,			question=lowercase_						)
									return self.pre_processor(lowercase_     ,			return_tensors="""pt"""						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   Optional[int]:
									a_	:	Tuple          =   self.model.generate(**lowercase_						)

									a_	:	Optional[int]          =   inputs['''input_ids'''].shape
									a_	:	Dict          =   output_ids.shape[0]

									return output_ids.reshape(lowercase_     ,			out_b // in_b     ,			*output_ids.shape[1:]						)[0][0]





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   Any:
									return self.pre_processor.decode(lowercase_     ,			skip_special_tokens=lowercase_     ,			clean_up_tokenization_spaces=lowercase_						)





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__snake_case							:			int      		= logging.get_logger(__name__)

__snake_case							:			Dict      		= {
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class 					A__(a_       ):
						"""simple docstring"""






						_A						:							Dict									= '''vit-hybrid'''





						def __init__(						self     ,			_lowercase=None     ,			_lowercase=768     ,			_lowercase=12     ,			_lowercase=12     ,			_lowercase=3_072     ,			_lowercase="gelu"     ,			_lowercase=0.0     ,			_lowercase=0.0     ,			_lowercase=0.0_2     ,			_lowercase=1e-12     ,			_lowercase=224     ,			_lowercase=1     ,			_lowercase=3     ,			_lowercase=[1, 1_024, 24, 24]     ,			_lowercase=True     ,			**_lowercase     ,			)    ->   Tuple:
									super().__init__(**_lowercase						)

									if backbone_config is None:
												logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""						)
												a_	:	Union[str, Any]          =   {
												    """global_padding""": """same""",
												    """layer_type""": """bottleneck""",
												    """depths""": [3, 4, 9],
												    """out_features""": ["""stage3"""],
												    """embedding_dynamic_padding""": True,
												}

									if isinstance(_lowercase     ,			_lowercase						):
												if "model_type" in backbone_config:
															a_	:	Any          =   CONFIG_MAPPING[backbone_config["""model_type"""]]
												else:
															logger.info(
															    """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""						)
															a_	:	Optional[int]          =   BitConfig
												a_	:	Dict          =   backbone_config_class(**_lowercase						)

									a_	:	List[str]          =   backbone_featmap_shape
									a_	:	int          =   backbone_config
									a_	:	Union[str, Any]          =   hidden_size
									a_	:	Optional[int]          =   num_hidden_layers
									a_	:	Dict          =   num_attention_heads
									a_	:	Optional[int]          =   intermediate_size
									a_	:	Dict          =   hidden_act
									a_	:	Optional[Any]          =   hidden_dropout_prob
									a_	:	List[str]          =   attention_probs_dropout_prob
									a_	:	Tuple          =   initializer_range
									a_	:	Any          =   layer_norm_eps
									a_	:	List[Any]          =   image_size
									a_	:	Optional[Any]          =   patch_size
									a_	:	Optional[Any]          =   num_channels
									a_	:	str          =   qkv_bias





						def  UpperCamelCase__				(						self						)    ->   Dict[str, any]:
									a_	:	List[str]          =   copy.deepcopy(self.__dict__						)
									a_	:	Optional[int]          =   self.backbone_config.to_dict()
									a_	:	List[str]          =   self.__class__.model_type
									return output


