from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_				:		Union[str, Any]       						=       {
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		Any       						=       ["""GPT2TokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		str       						=       [
	    """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPT2DoubleHeadsModel""",
	    """GPT2ForQuestionAnswering""",
	    """GPT2ForSequenceClassification""",
	    """GPT2ForTokenClassification""",
	    """GPT2LMHeadModel""",
	    """GPT2Model""",
	    """GPT2PreTrainedModel""",
	    """load_tf_weights_in_gpt2""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		Optional[int]       						=       [
	    """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFGPT2DoubleHeadsModel""",
	    """TFGPT2ForSequenceClassification""",
	    """TFGPT2LMHeadModel""",
	    """TFGPT2MainLayer""",
	    """TFGPT2Model""",
	    """TFGPT2PreTrainedModel""",
	]

try:
	if not is_keras_nlp_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		Dict       						=       ["""TFGPT2Tokenizer"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		List[Any]       						=       ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
	from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
	from .tokenization_gpta import GPTaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpta_fast import GPTaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpta import (
		    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTaDoubleHeadsModel,
		    GPTaForQuestionAnswering,
		    GPTaForSequenceClassification,
		    GPTaForTokenClassification,
		    GPTaLMHeadModel,
		    GPTaModel,
		    GPTaPreTrainedModel,
		    load_tf_weights_in_gpta,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gpta import (
		    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFGPTaDoubleHeadsModel,
		    TFGPTaForSequenceClassification,
		    TFGPTaLMHeadModel,
		    TFGPTaMainLayer,
		    TFGPTaModel,
		    TFGPTaPreTrainedModel,
		)

	try:
		if not is_keras_nlp_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpta_tf import TFGPTaTokenizer

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
	import sys

	lowerCamelCase_				:		int       						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types



class     lowercase						(       unittest.TestCase ,				_UpperCamelCase					):



 '''simple docstring'''


 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				load_tool('translation' )
  self.tool.setup()
  UpperCAmelCase__							=				load_tool('translation'       ,       remote=__a )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tool('Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				self.remote_tool('Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tool(text='Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				self.remote_tool(text='Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']
  UpperCAmelCase__							=				self.tool(*__a )

  # There is a single output
  if len(self.tool.outputs ) == 1:
   UpperCAmelCase__							=				[outputs]

  self.assertListEqual(output_types(__a )       ,       self.tool.outputs )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']
  UpperCAmelCase__							=				self.tool(*__a )

  if not isinstance(__a       ,       __a ):
   UpperCAmelCase__							=				[outputs]

  self.assertEqual(len(__a )       ,       len(self.tool.outputs ) )

  for output, output_type in zip(__a       ,       self.tool.outputs ):
   UpperCAmelCase__							=				AGENT_TYPE_MAPPING[output_type]
   self.assertTrue(isinstance(__a       ,       __a ) )




 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']

  UpperCAmelCase__							=				[]

  for _input, input_type in zip(__a       ,       self.tool.inputs ):
   if isinstance(__a       ,       __a ):
    _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
   else:
    _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )

        # Should not raise an error
  UpperCAmelCase__							=				self.tool(*__a )

  if not isinstance(__a       ,       __a ):
   UpperCAmelCase__							=				[outputs]

  self.assertEqual(len(__a )       ,       len(self.tool.outputs ) )



