



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase_									=    {
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    ["ConditionalDetrFeatureExtractor"]
  lowercase_									=    ["ConditionalDetrImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    [
      "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ConditionalDetrForObjectDetection",
      "ConditionalDetrForSegmentation",
      "ConditionalDetrModel",
      "ConditionalDetrPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ConditionalDetrConfig,
      ConditionalDetrOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
    from .image_processing_conditional_detr import ConditionalDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_conditional_detr import (
        CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConditionalDetrForObjectDetection,
        ConditionalDetrForSegmentation,
        ConditionalDetrModel,
        ConditionalDetrPreTrainedModel,
    )

else:
  import sys

  lowercase_									=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











'''simple docstring'''







# 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.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					"openai/whisper-base"
			snake_case__	=					(
			    "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
			    "transcribed text."
			)
			snake_case__	=					"transcriber"
			snake_case__	=					WhisperProcessor
			snake_case__	=					WhisperForConditionalGeneration

			snake_case__	=					["audio"]
			snake_case__	=					["text"]






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      str:
							return self.pre_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''       ).input_features






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      List[str]:
							return self.model.generate(inputs=__SCREAMING_SNAKE_CASE       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      Optional[Any]:
							return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE	,    skip_special_tokens=__SCREAMING_SNAKE_CASE       )[0]




