


'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


lowercase				=['gpt2']
lowercase				='gpt2'

if is_tf_available():






 class    __magic_name__				(						tf.Module							):






        def __init__(     self    ,				snake_case)		->    Optional[Any]:




           '''simple docstring'''
           super().__init__()
           _UpperCAmelCase  :  int				       =tokenizer
           _UpperCAmelCase  :  Dict				       =AutoConfig.from_pretrained(__a)
           _UpperCAmelCase  :  Optional[int]				       =TFGPTaLMHeadModel.from_config(__a)






        @tf.function(input_signature=(tf.TensorSpec((None,)    ,				tf.string    ,				name='text'),))
        def      lowerCAmelCase						(     self    ,				snake_case)		->    List[str]:




           '''simple docstring'''
           _UpperCAmelCase  :  Tuple				       =self.tokenizer(__a)
           _UpperCAmelCase  :  List[str]				       =tokenized['input_ids'].to_tensor()

           _UpperCAmelCase  :  Any				       =tf.cast(input_ids_dense > 0    ,				tf.intaa)
           # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

           _UpperCAmelCase  :  Union[str, Any]				       =self.model(input_ids=__a    ,				attention_mask=__a)['logits']

           return outputs






@require_tf
@require_keras_nlp
class    __magic_name__				(						unittest.TestCase							):






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




          '''simple docstring'''
          super().setUp()

          _UpperCAmelCase  :  Tuple				       =[GPTaTokenizer.from_pretrained(__a) for checkpoint in (TOKENIZER_CHECKPOINTS)]
          _UpperCAmelCase  :  str				       =[TFGPTaTokenizer.from_pretrained(__a) for checkpoint in TOKENIZER_CHECKPOINTS]
          assert len(self.tokenizers) == len(self.tf_tokenizers)

          _UpperCAmelCase  :  Any				       =[
              'This is a straightforward English test sentence.',
              'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
              'Now we\'re going to add some Chinese: 一 二 三 一二三',
              'And some much more rare Chinese: 齉 堃 齉堃',
              'Je vais aussi écrire en français pour tester les accents',
              'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
          ]
          _UpperCAmelCase  :  Optional[Any]				       =list(zip(self.test_sentences    ,				self.test_sentences[::-1]))






       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          for tokenizer, tf_tokenizer in zip(self.tokenizers    ,				self.tf_tokenizers):
             for test_inputs in self.test_sentences:
                _UpperCAmelCase  :  List[Any]				       =tokenizer([test_inputs]    ,				return_tensors='tf')
                _UpperCAmelCase  :  str				       =tf_tokenizer([test_inputs])

                for key in python_outputs.keys():
                   # convert them to numpy to avoid messing with ragged tensors
                   _UpperCAmelCase  :  Union[str, Any]				       =python_outputs[key].numpy()
                   _UpperCAmelCase  :  Tuple				       =tf_outputs[key].numpy()

                   self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
                   self.assertTrue(tf.reduce_all(tf.cast(__a    ,				tf.intaa) == tf_outputs_values))






       @slow
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          for tf_tokenizer in self.tf_tokenizers:
             _UpperCAmelCase  :  Dict				       =tf.function(__a)
             for test_inputs in self.test_sentences:
                _UpperCAmelCase  :  Dict				       =tf.constant(__a)
                _UpperCAmelCase  :  Union[str, Any]				       =compiled_tokenizer(__a)
                _UpperCAmelCase  :  Dict				       =tf_tokenizer(__a)

                for key in eager_outputs.keys():
                   self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))






       @slow
       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          for tf_tokenizer in self.tf_tokenizers:
             _UpperCAmelCase  :  str				       =ModelToSave(tokenizer=__a)
             _UpperCAmelCase  :  List[str]				       =tf.convert_to_tensor([self.test_sentences[0]])
             _UpperCAmelCase  :  Tuple				       =model.serving(__a)  # Build model with some sample inputs
             with TemporaryDirectory() as tempdir:
                _UpperCAmelCase  :  List[str]				       =Path(__a) / 'saved.model'
                tf.saved_model.save(__a    ,				__a    ,				signatures={'serving_default': model.serving})
                _UpperCAmelCase  :  List[Any]				       =tf.saved_model.load(__a)
             _UpperCAmelCase  :  Union[str, Any]				       =loaded_model.signatures['serving_default'](__a)['output_0']
             # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
             self.assertTrue(tf.reduce_all(out == loaded_output))






       @slow
       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          for tf_tokenizer in self.tf_tokenizers:
             _UpperCAmelCase  :  Dict				       =tf.convert_to_tensor([self.test_sentences[0]])
             _UpperCAmelCase  :  List[Any]				       =tf_tokenizer(__a)  # Build model with some sample inputs

             _UpperCAmelCase  :  Union[str, Any]				       =tf_tokenizer.get_config()
             _UpperCAmelCase  :  List[Any]				       =TFGPTaTokenizer.from_config(__a)
             _UpperCAmelCase  :  Optional[int]				       =model_from_config(__a)

             for key in from_config_output.keys():
                self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))






       @slow
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          for tf_tokenizer in self.tf_tokenizers:
             # for the test to run
             _UpperCAmelCase  :  List[Any]				       =1_2_3_1_2_3

             for max_length in [3, 5, 1_0_2_4]:
                _UpperCAmelCase  :  int				       =tf.convert_to_tensor([self.test_sentences[0]])
                _UpperCAmelCase  :  Dict				       =tf_tokenizer(__a    ,				max_length=__a)

                _UpperCAmelCase  :  Any				       =out['input_ids'].numpy().shape[1]

                assert out_length == max_length



"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = (DDPMScheduler,)



		def 		UpperCAmelCase				(   self							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''num_train_timesteps''': 10_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**__a)
							return config



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for timesteps in [1, 5, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,						[0.002, 0.02, 0.2, 2]):
												self.check_over_configs(beta_start=__a							,						beta_end=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=__a)



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






							'''simple docstring'''
							self.check_over_configs(thresholding=__a)
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=__a							,						prediction_type=__a							,						sample_max_value=__a							,						)



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






							'''simple docstring'''
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=__a)



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






							'''simple docstring'''
							for t in [0, 5_00, 9_99]:
												self.check_over_forward(time_step=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter
							_UpperCamelCase									=    torch.manual_seed(0)

							for t in reversed(range(__a)):
												# 1. predict noise residual
												_UpperCamelCase									=    model(__a							,						__a)

												# 2. predict previous mean of sample x_t-1
												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a							,						generator=__a).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 258.9606) < 1e-2
							assert abs(result_mean.item() - 0.3372) < 1e-3



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config(prediction_type='''v_prediction''')
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter
							_UpperCamelCase									=    torch.manual_seed(0)

							for t in reversed(range(__a)):
												# 1. predict noise residual
												_UpperCamelCase									=    model(__a							,						__a)

												# 2. predict previous mean of sample x_t-1
												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a							,						generator=__a).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 202.0296) < 1e-2
							assert abs(result_mean.item() - 0.2631) < 1e-3



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=__a)

							_UpperCamelCase									=    scheduler.timesteps

							for i, timestep in enumerate(__a):
												if i == len(__a) - 1:
																	_UpperCamelCase									=    -1
												else:
																	_UpperCamelCase									=    timesteps[i + 1]

												_UpperCamelCase									=    scheduler.previous_timestep(__a)
												_UpperCamelCase									=    prev_t.item()

												self.assertEqual(__a							,						__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 51, 0]

							with self.assertRaises(__a							,						msg='''`custom_timesteps` must be in descending order.'''):
												scheduler.set_timesteps(timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 1, 0]
							_UpperCamelCase									=    len(__a)

							with self.assertRaises(__a							,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
												scheduler.set_timesteps(num_inference_steps=__a							,						timesteps=__a)




		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    __a							,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''							,						):
												scheduler.set_timesteps(timesteps=__a)






