

'''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 lowerCamelCase   :

				def __init__(       self,						lowercase_,						)    ->							Any:
					snake_case							=					parent
					snake_case							=					13
					snake_case							=					7
					snake_case							=					True
					snake_case							=					True
					snake_case							=					True
					snake_case							=					True
					snake_case							=					True
					snake_case							=					99
					snake_case							=					32
					snake_case							=					2
					snake_case							=					4
					snake_case							=					37
					snake_case							=					'gelu'
					snake_case							=					0.1
					snake_case							=					0.1
					snake_case							=					512
					snake_case							=					16
					snake_case							=					2
					snake_case							=					0.02
					snake_case							=					3
					snake_case							=					4
					snake_case							=					None
					snake_case							=					self.vocab_size - 1

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					ids_tensor([self.batch_size, self.seq_length],						self.vocab_size )

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

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

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

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

					snake_case							=					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,						)

					snake_case							=					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 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						*lowercase_ )    ->							Dict:
					snake_case							=					TFOpenAIGPTModel(config=A__ )
					snake_case							=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
					snake_case							=					model(A__ )

					snake_case							=					[input_ids, input_mask]
					snake_case							=					model(A__ )

					snake_case							=					model(A__ )

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

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						*lowercase_ )    ->							List[Any]:
					snake_case							=					TFOpenAIGPTLMHeadModel(config=A__ )
					snake_case							=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
					snake_case							=					model(A__ )
					self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.seq_length, self.vocab_size) )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						*lowercase_ )    ->							Tuple:
					snake_case							=					TFOpenAIGPTDoubleHeadsModel(config=A__ )

					snake_case							=					tf.tile(tf.expand_dims(A__,						1 ),						(1, self.num_choices, 1) )
					snake_case							=					tf.tile(tf.expand_dims(A__,						1 ),						(1, self.num_choices, 1) )
					snake_case							=					tf.tile(tf.expand_dims(A__,						1 ),						(1, self.num_choices, 1) )

					snake_case							=					{
					    '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,
					}
					snake_case							=					model(A__ )
					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 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						*lowercase_ )    ->							Union[str, Any]:
					snake_case							=					self.num_labels
					snake_case							=					ids_tensor([self.batch_size],						self.type_sequence_label_size )
					snake_case							=					{
					    'input_ids': input_ids,
					    'attention_mask': input_mask,
					    'token_type_ids': token_type_ids,
					    'labels': sequence_labels,
					}
					snake_case							=					TFOpenAIGPTForSequenceClassification(A__ )
					snake_case							=					model(A__ )
					self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.num_labels) )






				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					self.prepare_config_and_inputs()

					(
					    (
					    snake_case		
					)			,			(
					    snake_case		
					)			,			(
					    snake_case		
					)			,			(
					    snake_case		
					)			,			(
					    snake_case		
					)			,			(
					    snake_case		
					)			,			(
					    snake_case		
					)			,			(
					    snake_case		
					)			,			(
					    snake_case		
					)			,					
					)							=					config_and_inputs

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







@require_tf
class lowerCamelCase   (    _lowerCAmelCase				,				_lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = (
				    (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
				    if is_tf_available()
				    else ()
				)
				snake_case_							     = (
				    (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
				snake_case_							     = (
				    {
				        "feature-extraction": TFOpenAIGPTModel,
				        "text-classification": TFOpenAIGPTForSequenceClassification,
				        "text-generation": TFOpenAIGPTLMHeadModel,
				        "zero-shot": TFOpenAIGPTForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				snake_case_							     = False
				snake_case_							     = False

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							List[str]:
					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 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					TFOpenAIGPTModelTester(self )
					snake_case							=					ConfigTester(self,						config_class=A__,						n_embd=37 )

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					self.config_tester.run_common_tests()

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_openai_gpt_model(*A__ )

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_openai_gpt_lm_head(*A__ )

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_openai_gpt_double_head(*A__ )

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*A__ )






				@slow
				def 					_lowerCamelCase    (       self )    ->							Dict:
					for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case							=					TFOpenAIGPTModel.from_pretrained(A__ )
						self.assertIsNotNone(A__ )







@require_tf
class lowerCamelCase   (    unittest.TestCase				):

				@slow
				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					TFOpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
					snake_case							=					tf.convert_to_tensor([[481, 4735, 544]],						dtype=tf.intaa )  # the president is
					snake_case							=					[
					    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

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











import math

def       lowerCAmelCase_      (	lowerCamelCase:							int    )       ->      str:


      '''simple docstring'''




      if number == 0:
            lowerCamelCase__   	=					'Zero'
            return words
      else:
            lowerCamelCase__   	=					math.logaa(lowerCamelCase    )
            lowerCamelCase__   	=					digits + 1
            lowerCamelCase__   	=					{}
            lowerCamelCase__   	=					''
            lowerCamelCase__   	=					'One'
            lowerCamelCase__   	=					'Two'
            lowerCamelCase__   	=					'Three'
            lowerCamelCase__   	=					'Four'
            lowerCamelCase__   	=					'Five'
            lowerCamelCase__   	=					'Six'
            lowerCamelCase__   	=					'Seven'
            lowerCamelCase__   	=					'Eight'
            lowerCamelCase__   	=					'Nine'

            lowerCamelCase__   	=					{}
            lowerCamelCase__   	=					''
            lowerCamelCase__   	=					'Twenty'
            lowerCamelCase__   	=					'Thirty'
            lowerCamelCase__   	=					'Forty'
            lowerCamelCase__   	=					'Fifty'
            lowerCamelCase__   	=					'Sixty'
            lowerCamelCase__   	=					'Seventy'
            lowerCamelCase__   	=					'Eighty'
            lowerCamelCase__   	=					'Ninety'

            lowerCamelCase__   	=					{}
            lowerCamelCase__   	=					'Ten'
            lowerCamelCase__   	=					'Eleven'
            lowerCamelCase__   	=					'Twelve'
            lowerCamelCase__   	=					'Thirteen'
            lowerCamelCase__   	=					'Fourteen'
            lowerCamelCase__   	=					'Fifteen'
            lowerCamelCase__   	=					'Sixteen'
            lowerCamelCase__   	=					'Seventeen'
            lowerCamelCase__   	=					'Eighteen'
            lowerCamelCase__   	=					'Nineteen'

            lowerCamelCase__   	=					{}
            lowerCamelCase__   	=					'Hundred,'
            lowerCamelCase__   	=					'Thousand,'
            lowerCamelCase__   	=					'Lakh,'
            lowerCamelCase__   	=					'Crore,'

            lowerCamelCase__   	=					number
            lowerCamelCase__   	=					''
            lowerCamelCase__   	=					0
            lowerCamelCase__   	=					int(lowerCamelCase    )
            while counter < digits:
                  lowerCamelCase__   	=					temp_num % 10
                  if counter % 2 == 0:
                        lowerCamelCase__   	=					''
                        if counter in placevalue and current != 0:
                              lowerCamelCase__   	=					placevalue[counter]
                        if counter == 2:
                              lowerCamelCase__   	=					singles[current] + addition + words
                        elif counter == 0:
                              if ((temp_num % 1_00) // 10) == 1:
                                    lowerCamelCase__   	=					teens[current] + addition + words
                                    lowerCamelCase__   	=					temp_num // 10
                                    counter += 1
                              else:
                                    lowerCamelCase__   	=					singles[current] + addition + words

                        else:
                              lowerCamelCase__   	=					doubles[current] + addition + words

                  else:
                        if counter == 1:
                              if current == 1:
                                    lowerCamelCase__   	=					teens[number % 10] + words
                              else:
                                    lowerCamelCase__   	=					''
                                    if counter in placevalue:
                                          lowerCamelCase__   	=					placevalue[counter]
                                    lowerCamelCase__   	=					doubles[current] + addition + words
                        else:
                              lowerCamelCase__   	=					''
                              if counter in placevalue:
                                    if current == 0 and ((temp_num % 1_00) // 10) == 0:
                                          lowerCamelCase__   	=					''
                                    else:
                                          lowerCamelCase__   	=					placevalue[counter]
                              if ((temp_num % 1_00) // 10) == 1:
                                    lowerCamelCase__   	=					teens[current] + addition + words
                                    lowerCamelCase__   	=					temp_num // 10
                                    counter += 1
                              else:
                                    lowerCamelCase__   	=					singles[current] + addition + words
                  counter += 1
                  lowerCamelCase__   	=					temp_num // 10
      return words


if __name__ == "__main__":
      import doctest

      doctest.testmod()