




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


a_										=       logging.get_logger(__name__)

a_										=       {
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Any      =   "musicgen_decoder"
      lowerCAmelCase__     :	List[str]      =   ["past_key_values"]







      def __init__(       self   :	Tuple	,    snake_case   :	Optional[int]=2048	,    snake_case   :	Optional[Any]=2048	,    snake_case   :	Tuple=24	,    snake_case   :	Any=4096	,    snake_case   :	Tuple=16	,    snake_case   :	int=0.0	,    snake_case   :	List[str]=True	,    snake_case   :	List[Any]="gelu"	,    snake_case   :	Union[str, Any]=1024	,    snake_case   :	Optional[Any]=0.1	,    snake_case   :	str=0.0	,    snake_case   :	Optional[int]=0.0	,    snake_case   :	Dict=0.02	,    snake_case   :	int=False	,    snake_case   :	int=4	,    snake_case   :	Optional[int]=2048	,    snake_case   :	List[str]=2048	,    snake_case   :	Optional[Any]=None	,    snake_case   :	Optional[Any]=False	,    **snake_case   :	Optional[Any]	,    ):
          __UpperCamelCase											=  vocab_size
          __UpperCamelCase											=  max_position_embeddings
          __UpperCamelCase											=  hidden_size
          __UpperCamelCase											=  ffn_dim
          __UpperCamelCase											=  num_hidden_layers
          __UpperCamelCase											=  num_attention_heads
          __UpperCamelCase											=  dropout
          __UpperCamelCase											=  attention_dropout
          __UpperCamelCase											=  activation_dropout
          __UpperCamelCase											=  activation_function
          __UpperCamelCase											=  initializer_factor
          __UpperCamelCase											=  layerdrop
          __UpperCamelCase											=  use_cache
          __UpperCamelCase											=  scale_embedding  # scale factor will be sqrt(d_model) if True
          __UpperCamelCase											=  num_codebooks
          super().__init__(
              pad_token_id=snake_case	,    bos_token_id=snake_case	,    eos_token_id=snake_case	,    tie_word_embeddings=snake_case	,    **snake_case	,    )







class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Optional[Any]      =   "musicgen"
      lowerCAmelCase__     :	List[str]      =   True







      def __init__(       self   :	Any	,    **snake_case   :	Optional[Any]			):
          super().__init__(**snake_case			)
          if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
              raise ValueError('''Config has to be initialized with text_encoder, audio_encoder and decoder config'''			)

          __UpperCamelCase											=  kwargs.pop('''text_encoder'''			)
          __UpperCamelCase											=  text_encoder_config.pop('''model_type'''			)

          __UpperCamelCase											=  kwargs.pop('''audio_encoder'''			)
          __UpperCamelCase											=  audio_encoder_config.pop('''model_type'''			)

          __UpperCamelCase											=  kwargs.pop('''decoder'''			)

          __UpperCamelCase											=  AutoConfig.for_model(snake_case	,    **snake_case			)
          __UpperCamelCase											=  AutoConfig.for_model(snake_case	,    **snake_case			)
          __UpperCamelCase											=  MusicgenDecoderConfig(**snake_case			)
          __UpperCamelCase											=  True







      @classmethod
      def  snake_case		(       cls   :	str	,    snake_case   :	PretrainedConfig	,    snake_case   :	PretrainedConfig	,    snake_case   :	MusicgenDecoderConfig	,    **snake_case   :	Optional[int]	,    ):
          return cls(
              text_encoder=text_encoder_config.to_dict()	,    audio_encoder=audio_encoder_config.to_dict()	,    decoder=decoder_config.to_dict()	,    **snake_case	,    )







      def  snake_case		(       self   :	Tuple			):
          __UpperCamelCase											=  copy.deepcopy(self.__dict__			)
          __UpperCamelCase											=  self.text_encoder.to_dict()
          __UpperCamelCase											=  self.audio_encoder.to_dict()
          __UpperCamelCase											=  self.decoder.to_dict()
          __UpperCamelCase											=  self.__class__.model_type
          return output












# 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


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

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   "google/flan-t5-base"
      lowerCAmelCase__     :	Union[str, 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."
      )
      lowerCAmelCase__     :	List[Any]      =   "text_qa"
      lowerCAmelCase__     :	int      =   AutoTokenizer
      lowerCAmelCase__     :	Optional[Any]      =   AutoModelForSeqaSeqLM

      lowerCAmelCase__     :	Tuple      =   ["text", "text"]
      lowerCAmelCase__     :	Any      =   ["text"]







      def  snake_case		(       self   :	Union[str, Any]	,    snake_case   :	str	,    snake_case   :	str			):
          __UpperCamelCase											=  QA_PROMPT.format(text=snake_case	,    question=snake_case			)
          return self.pre_processor(snake_case	,    return_tensors='''pt'''			)







      def  snake_case		(       self   :	Any	,    snake_case   :	Optional[int]			):
          __UpperCamelCase											=  self.model.generate(**snake_case			)

          __UpperCamelCase						, __UpperCamelCase											=  inputs['''input_ids'''].shape
          __UpperCamelCase											=  output_ids.shape[0]

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







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Optional[Any]			):
          return self.pre_processor.decode(snake_case	,    skip_special_tokens=snake_case	,    clean_up_tokenization_spaces=snake_case			)






