




import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax







@require_flax
class 		__UpperCAmelCase:





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Optional[int]:






         """simple docstring"""
         a__			,		a__  			=							self.prepare_init_args_and_inputs_for_common()

         a__  			=							self.model_class(**A     )
         a__  			=							model.init(inputs_dict["""prng_key"""]    ,					inputs_dict["""sample"""]     )
         jax.lax.stop_gradient(A     )

         a__  			=							model.apply(A    ,					inputs_dict["""sample"""]     )

         if isinstance(A    ,					A     ):
             a__  			=							output.sample

         self.assertIsNotNone(A     )
         a__  			=							inputs_dict["""sample"""].shape
         self.assertEqual(output.shape    ,					A    ,					"""Input and output shapes do not match"""     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      int:






         """simple docstring"""
         a__			,		a__  			=							self.prepare_init_args_and_inputs_for_common()

         a__  			=							16
         a__  			=							(16, 32)

         a__  			=							self.model_class(**A     )
         a__  			=							model.init(inputs_dict["""prng_key"""]    ,					inputs_dict["""sample"""]     )
         jax.lax.stop_gradient(A     )

         a__  			=							model.apply(A    ,					inputs_dict["""sample"""]     )

         if isinstance(A    ,					A     ):
             a__  			=							output.sample

         self.assertIsNotNone(A     )
         a__  			=							inputs_dict["""sample"""].shape
         self.assertEqual(output.shape    ,					A    ,					"""Input and output shapes do not match"""     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      int:






         """simple docstring"""
         a__  			=							"""kwargs""" in inspect.signature(self.model_class.__init__     ).parameters
         a__  			=							len(self.model_class._deprecated_kwargs     ) > 0

         if has_kwarg_in_model_class and not has_deprecated_kwarg:
             raise ValueError(
                 f"""{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"""
                 """ under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"""
                 """ no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="""
                 """ [<deprecated_argument>]`"""     )

         if not has_kwarg_in_model_class and has_deprecated_kwarg:
             raise ValueError(
                 f"""{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"""
                 """ under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"""
                 f""" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"""
                 """ from `_deprecated_kwargs = [<deprecated_argument>]`"""     )












# 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__)






