




from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							{
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_    :       List[str]       =							[
     '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''FlaubertForMultipleChoice''',
     '''FlaubertForQuestionAnswering''',
     '''FlaubertForQuestionAnsweringSimple''',
     '''FlaubertForSequenceClassification''',
     '''FlaubertForTokenClassification''',
     '''FlaubertModel''',
     '''FlaubertWithLMHeadModel''',
     '''FlaubertPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_    :       Any       =							[
     '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFFlaubertForMultipleChoice''',
     '''TFFlaubertForQuestionAnsweringSimple''',
     '''TFFlaubertForSequenceClassification''',
     '''TFFlaubertForTokenClassification''',
     '''TFFlaubertModel''',
     '''TFFlaubertPreTrainedModel''',
     '''TFFlaubertWithLMHeadModel''',
 ]


if TYPE_CHECKING:
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
 from .tokenization_flaubert import FlaubertTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flaubert import (
      FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertForMultipleChoice,
      FlaubertForQuestionAnswering,
      FlaubertForQuestionAnsweringSimple,
      FlaubertForSequenceClassification,
      FlaubertForTokenClassification,
      FlaubertModel,
      FlaubertPreTrainedModel,
      FlaubertWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_flaubert import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertPreTrainedModel,
      TFFlaubertWithLMHeadModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_    :       str       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












# 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

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_    :       int       =							{
    '''agents''': ['''Agent''', '''AzureOpenAiAgent''', '''HfAgent''', '''LocalAgent''', '''OpenAiAgent'''],
    '''base''': ['''PipelineTool''', '''RemoteTool''', '''Tool''', '''launch_gradio_demo''', '''load_tool'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_    :       int       =							['''DocumentQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''ImageCaptioningTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''ImageQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       str       =							['''ImageSegmentationTool''']
 SCREAMING_SNAKE_CASE_    :       Any       =							['''SpeechToTextTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''TextClassificationTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''TextQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TextSummarizationTool''']
 SCREAMING_SNAKE_CASE_    :       Optional[int]       =							['''TextToSpeechTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TranslationTool''']

if TYPE_CHECKING:
 from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
 from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .document_question_answering import DocumentQuestionAnsweringTool
  from .image_captioning import ImageCaptioningTool
  from .image_question_answering import ImageQuestionAnsweringTool
  from .image_segmentation import ImageSegmentationTool
  from .speech_to_text import SpeechToTextTool
  from .text_classification import TextClassificationTool
  from .text_question_answering import TextQuestionAnsweringTool
  from .text_summarization import TextSummarizationTool
  from .text_to_speech import TextToSpeechTool
  from .translation import TranslationTool
else:
 import sys

 SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






