


'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__							=		{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__							=		[
       "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
       "GPTNeoForCausalLM",
       "GPTNeoForQuestionAnswering",
       "GPTNeoForSequenceClassification",
       "GPTNeoForTokenClassification",
       "GPTNeoModel",
       "GPTNeoPreTrainedModel",
       "load_tf_weights_in_gpt_neo",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__							=		[
       "FlaxGPTNeoForCausalLM",
       "FlaxGPTNeoModel",
       "FlaxGPTNeoPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpt_neo import (
          GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTNeoForCausalLM,
          GPTNeoForQuestionAnswering,
          GPTNeoForSequenceClassification,
          GPTNeoForTokenClassification,
          GPTNeoModel,
          GPTNeoPreTrainedModel,
          load_tf_weights_in_gpt_neo,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
   import sys

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





'''simple docstring'''

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ,			lowerCamelCase__ ):






     def  a_					(			self		)       ->    Tuple:
          _a						=		load_tool("text-classification"		)
          self.tool.setup()
          _a						=		load_tool("text-classification"     ,							remote=__UpperCamelCase		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.tool("That's quite cool"     ,							["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    Union[str, Any]:
          _a						=		self.remote_tool("That's quite cool"     ,							["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.tool(text="That's quite cool"     ,							labels=["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    str:
          _a						=		self.remote_tool(text="That's quite cool"     ,							labels=["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)

