"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers.models.openai.modeling_tf_openai import (
         TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFOpenAIGPTDoubleHeadsModel,
         TFOpenAIGPTForSequenceClassification,
         TFOpenAIGPTLMHeadModel,
         TFOpenAIGPTModel,
     )






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					13
      __lowerCamelCase    		=					7
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					99
      __lowerCamelCase    		=					32
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					37
      __lowerCamelCase    		=					'gelu'
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					512
      __lowerCamelCase    		=					16
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					0.02
      __lowerCamelCase    		=					3
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					self.vocab_size - 1







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size	)

      __lowerCamelCase    		=					None
      if self.use_input_mask:
         __lowerCamelCase    		=					random_attention_mask([self.batch_size, self.seq_length]	)

      __lowerCamelCase    		=					None
      if self.use_token_type_ids:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size	)

      __lowerCamelCase    		=					None
      if self.use_mc_token_ids:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.num_choices] ,				self.seq_length	)

      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.num_choices	)

      __lowerCamelCase    		=					OpenAIGPTConfig(
          vocab_size=self.vocab_size ,				n_embd=self.hidden_size ,				n_layer=self.num_hidden_layers ,				n_head=self.num_attention_heads ,				n_positions=self.max_position_embeddings ,				pad_token_id=self.pad_token_id ,				)

      __lowerCamelCase    		=					ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,				2	)

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
          token_type_ids,
          mc_token_ids,
          sequence_labels,
          token_labels,
          choice_labels,
      )







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFOpenAIGPTModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					[input_ids, input_mask]
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFOpenAIGPTLMHeadModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFOpenAIGPTDoubleHeadsModel(config=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE ,				1	) ,				(1, self.num_choices, 1)	)
      __lowerCamelCase    		=					tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE ,				1	) ,				(1, self.num_choices, 1)	)
      __lowerCamelCase    		=					tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE ,				1	) ,				(1, self.num_choices, 1)	)

      __lowerCamelCase    		=					{
          'input_ids': multiple_choice_inputs_ids,
          'mc_token_ids': mc_token_ids,
          'attention_mask': multiple_choice_input_mask,
          'token_type_ids': multiple_choice_token_type_ids,
      }
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(
          result.logits.shape ,				(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)	)
      self.parent.assertEqual(result.mc_logits.shape ,				(self.batch_size, self.num_choices)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
      __lowerCamelCase    		=					{
          'input_ids': input_ids,
          'attention_mask': input_mask,
          'token_type_ids': token_type_ids,
          'labels': sequence_labels,
      }
      __lowerCamelCase    		=					TFOpenAIGPTForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()

      (
          (
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,
      )    		=					config_and_inputs

      __lowerCamelCase    		=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict






@require_tf
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							List[Any]    =    (
       (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
       if is_tf_available()
       else ()
   )
   __UpperCAmelCase	:							Any    =    (
       (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
   __UpperCAmelCase	:							List[str]    =    (
       {
           "feature-extraction": TFOpenAIGPTModel,
           "text-classification": TFOpenAIGPTForSequenceClassification,
           "text-generation": TFOpenAIGPTLMHeadModel,
           "zero-shot": TFOpenAIGPTForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase	:							List[Any]    =    False
   __UpperCAmelCase	:							Optional[int]    =    False







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
         # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
         # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
         # tiny config could not be created.
         return True

      return False







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFOpenAIGPTModelTester(self	)
      __lowerCamelCase    		=					ConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE ,				n_embd=37	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_openai_gpt_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_openai_gpt_lm_head(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_openai_gpt_double_head(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase    		=					TFOpenAIGPTModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)






@require_tf
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFOpenAIGPTLMHeadModel.from_pretrained('openai-gpt'	)
      __lowerCamelCase    		=					tf.convert_to_tensor([[481, 4735, 544]] ,				dtype=tf.intaa	)  # the president is
      __lowerCamelCase    		=					[
          481,
          4735,
          544,
          246,
          963,
          870,
          762,
          239,
          244,
          40477,
          244,
          249,
          719,
          881,
          487,
          544,
          240,
          244,
          603,
          481,
      ]  # the president is a very good man. " \n " i\'m sure he is, " said the

      __lowerCamelCase    		=					model.generate(_SCREAMING_SNAKE_CASE ,				do_sample=_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(output_ids[0].numpy().tolist() ,				_SCREAMING_SNAKE_CASE	)







from typing import TYPE_CHECKING

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


lowerCAmelCase__	:  int						=  {
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  List[Any]						=  [
				    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwitchTransformersEncoderModel''',
				    '''SwitchTransformersForConditionalGeneration''',
				    '''SwitchTransformersModel''',
				    '''SwitchTransformersPreTrainedModel''',
				    '''SwitchTransformersTop1Router''',
				    '''SwitchTransformersSparseMLP''',
				]


if TYPE_CHECKING:
				from .configuration_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SwitchTransformersConfig,
				    SwitchTransformersOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_switch_transformers import (
								    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwitchTransformersEncoderModel,
								    SwitchTransformersForConditionalGeneration,
								    SwitchTransformersModel,
								    SwitchTransformersPreTrainedModel,
								    SwitchTransformersSparseMLP,
								    SwitchTransformersTopaRouter,
								)


else:
				import sys

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


