





"""simple docstring"""





import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class 		SCREAMING_SNAKE_CASE_							:
		"""simple docstring"""




		def __init__(     self  :int						,    __lowercase  :Union[str, Any]						,    __lowercase  :Optional[int]=99						,    __lowercase  :Any=13						,    __lowercase  :int=16						,    __lowercase  :Dict=7						,    __lowercase  :List[Any]=True						,    __lowercase  :List[str]=True						,    __lowercase  :List[Any]=True						,    __lowercase  :Optional[int]=False						,    __lowercase  :int=True						,    __lowercase  :Union[str, Any]=2						,    __lowercase  :List[str]=32						,    __lowercase  :Optional[int]=4						,    __lowercase  :Dict=4						,    __lowercase  :Any=30						,    __lowercase  :List[Any]=0						,    __lowercase  :Any=1						,    __lowercase  :Optional[Any]=2						,    __lowercase  :Tuple=None						,    ):
									__lowerCamelCase      :      Tuple         =parent
									__lowerCamelCase      :      List[str]         =batch_size
									__lowerCamelCase      :      List[str]         =decoder_seq_length
									# For common tests
									__lowerCamelCase      :      int         =self.decoder_seq_length
									__lowerCamelCase      :      str         =is_training
									__lowerCamelCase      :      int         =use_attention_mask
									__lowerCamelCase      :      Any         =use_labels

									__lowerCamelCase      :      Union[str, Any]         =vocab_size
									__lowerCamelCase      :      Any         =d_model
									__lowerCamelCase      :      List[str]         =d_model
									__lowerCamelCase      :      List[str]         =decoder_layers
									__lowerCamelCase      :      Optional[int]         =decoder_layers
									__lowerCamelCase      :      Dict         =decoder_ffn_dim
									__lowerCamelCase      :      Optional[Any]         =decoder_attention_heads
									__lowerCamelCase      :      List[str]         =decoder_attention_heads
									__lowerCamelCase      :      List[Any]         =eos_token_id
									__lowerCamelCase      :      List[str]         =bos_token_id
									__lowerCamelCase      :      Union[str, Any]         =pad_token_id
									__lowerCamelCase      :      Union[str, Any]         =decoder_start_token_id
									__lowerCamelCase      :      Any         =use_cache
									__lowerCamelCase      :      Dict         =max_position_embeddings

									__lowerCamelCase      :      Tuple         =None
									__lowerCamelCase      :      List[Any]         =decoder_seq_length
									__lowerCamelCase      :      int         =2
									__lowerCamelCase      :      Optional[Any]         =1




		def 					__lowercase  (     self  :str			):
									__lowerCamelCase      :      str         =ids_tensor([self.batch_size, self.decoder_seq_length]						,    self.vocab_size			)

									__lowerCamelCase      :      Dict         =None
									if self.use_attention_mask:
																__lowerCamelCase      :      Optional[Any]         =ids_tensor([self.batch_size, self.decoder_seq_length]						,    vocab_size=2			)

									__lowerCamelCase      :      Any         =None
									if self.use_labels:
																__lowerCamelCase      :      str         =ids_tensor([self.batch_size, self.decoder_seq_length]						,    self.vocab_size			)

									__lowerCamelCase      :      Tuple         =TrOCRConfig(
									    vocab_size=self.vocab_size						,    d_model=self.d_model						,    decoder_layers=self.decoder_layers						,    decoder_ffn_dim=self.decoder_ffn_dim						,    decoder_attention_heads=self.decoder_attention_heads						,    eos_token_id=self.eos_token_id						,    bos_token_id=self.bos_token_id						,    use_cache=self.use_cache						,    pad_token_id=self.pad_token_id						,    decoder_start_token_id=self.decoder_start_token_id						,    max_position_embeddings=self.max_position_embeddings						,    )

									return (config, input_ids, attention_mask, lm_labels)




		def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :int						,    __lowercase  :Any						,    __lowercase  :int						,    __lowercase  :int						,    ):
									__lowerCamelCase      :      List[str]         =True
									__lowerCamelCase      :      Dict         =TrOCRDecoder(config=__lowercase			).to(__lowercase			).eval()
									__lowerCamelCase      :      List[str]         =input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									__lowerCamelCase      :      Optional[int]         =model(__lowercase						,    use_cache=__lowercase			)
									__lowerCamelCase      :      Union[str, Any]         =model(__lowercase			)
									__lowerCamelCase      :      str         =model(__lowercase						,    use_cache=__lowercase			)

									self.parent.assertTrue(len(__lowercase			) == len(__lowercase			)			)
									self.parent.assertTrue(len(__lowercase			) == len(__lowercase			) + 1			)

									__lowerCamelCase      :      Tuple         =outputs['''past_key_values''']

									# create hypothetical next token and extent to next_input_ids
									__lowerCamelCase      :      Optional[int]         =ids_tensor((2, 1)						,    config.vocab_size - 1			) + 1

									# append to next input_ids and
									__lowerCamelCase      :      Dict         =torch.cat([input_ids, next_tokens]						,    dim=-1			)

									__lowerCamelCase      :      str         =model(__lowercase			)['''last_hidden_state''']
									__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    past_key_values=__lowercase			)['''last_hidden_state''']

									# select random slice
									__lowerCamelCase      :      int         =ids_tensor((1,)						,    output_from_past.shape[-1]			).item()
									__lowerCamelCase      :      str         =output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									__lowerCamelCase      :      Dict         =output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(__lowercase						,    __lowercase						,    atol=1e-3			)




		def 					__lowercase  (     self  :Dict			):
									__lowerCamelCase      :      Tuple         =self.prepare_config_and_inputs()
									__lowerCamelCase      :      str         =config_and_inputs

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

@require_torch
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__       ,  snake_case__       ,  unittest.TestCase   ):
		"""simple docstring"""
		__snake_case   :       Tuple	 =		(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
		__snake_case   :       Optional[Any]	 =		(TrOCRForCausalLM,) if is_torch_available() else ()
		__snake_case   :       Optional[int]	 =		{"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
		__snake_case   :       Any	 =		True
		__snake_case   :       List[str]	 =		False




		def 					__lowercase  (     self  :Any			):
									__lowerCamelCase      :      str         =TrOCRStandaloneDecoderModelTester(self						,    is_training=__lowercase			)
									__lowerCamelCase      :      Dict         =ConfigTester(self						,    config_class=__lowercase			)




		def 					__lowercase  (     self  :str			):
									pass




		def 					__lowercase  (     self  :List[Any]			):
									pass




		def 					__lowercase  (     self  :Optional[Any]			):
									pass




		def 					__lowercase  (     self  :str			):
									self.config_tester.run_common_tests()




		def 					__lowercase  (     self  :Dict			):
									__lowerCamelCase      :      List[Any]         =self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*__lowercase			)




		def 					__lowercase  (     self  :Union[str, Any]			):
									return




		@unittest.skip('''The model doesn\'t support left padding'''			)  # and it's not used enough to be worth fixing :)
		def 					__lowercase  (     self  :List[str]			):
									pass











"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[Any]	 =		(UnCLIPScheduler,)




				def 					__lowercase  (     self  :Any						,    **__lowercase  :str			):
											__lowerCamelCase      :      Optional[int]         ={
											    '''num_train_timesteps''': 1000,
											    '''variance_type''': '''fixed_small_log''',
											    '''clip_sample''': True,
											    '''clip_sample_range''': 1.0,
											    '''prediction_type''': '''epsilon''',
											}

											config.update(**__lowercase			)
											return config




				def 					__lowercase  (     self  :Tuple			):
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=__lowercase			)




				def 					__lowercase  (     self  :Tuple			):
											for variance in ["fixed_small_log", "learned_range"]:
																		self.check_over_configs(variance_type=__lowercase			)




				def 					__lowercase  (     self  :List[str]			):
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=__lowercase			)




				def 					__lowercase  (     self  :List[Any]			):
											for clip_sample_range in [1, 5, 10, 20]:
																		self.check_over_configs(clip_sample_range=__lowercase			)




				def 					__lowercase  (     self  :Any			):
											for prediction_type in ["epsilon", "sample"]:
																		self.check_over_configs(prediction_type=__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											for time_step in [0, 500, 999]:
																		for prev_timestep in [None, 5, 100, 250, 500, 750]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																																continue

																									self.check_over_forward(time_step=__lowercase						,    prev_timestep=__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      List[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      str         =self.get_scheduler_config(variance_type='''fixed_small_log'''			)
											__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)

											assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_0_0_0e-1_0			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0549625			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9994987			)			) < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Tuple         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[Any]         =self.get_scheduler_config(variance_type='''learned_range'''			)
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Optional[int]         =0.5

											assert scheduler._get_variance(1						,    predicted_variance=__lowercase			) - -10.1712790 < 1e-5
											assert scheduler._get_variance(487						,    predicted_variance=__lowercase			) - -5.7998052 < 1e-5
											assert scheduler._get_variance(999						,    predicted_variance=__lowercase			) - -0.0010011 < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Any         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[int]         =self.get_scheduler_config()
											__lowerCamelCase      :      str         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Tuple         =scheduler.timesteps

											__lowerCamelCase      :      Any         =self.dummy_model()
											__lowerCamelCase      :      Any         =self.dummy_sample_deter
											__lowerCamelCase      :      List[Any]         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Tuple         =model(__lowercase						,    __lowercase			)

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      List[Any]         =pred_prev_sample

											__lowerCamelCase      :      Optional[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 252.2682495			) < 1e-2
											assert abs(result_mean.item() - 0.3284743			) < 1e-3




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Optional[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      Any         =self.get_scheduler_config()
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											scheduler.set_timesteps(25			)

											__lowerCamelCase      :      Dict         =scheduler.timesteps

											__lowerCamelCase      :      List[str]         =self.dummy_model()
											__lowerCamelCase      :      List[str]         =self.dummy_sample_deter
											__lowerCamelCase      :      Any         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    __lowercase			)

																		if i + 1 == timesteps.shape[0]:
																									__lowerCamelCase      :      Any         =None
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =timesteps[i + 1]

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[str]         =scheduler.step(
																		    __lowercase						,    __lowercase						,    __lowercase						,    prev_timestep=__lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      int         =pred_prev_sample

											__lowerCamelCase      :      List[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      List[str]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 258.2044983			) < 1e-2
											assert abs(result_mean.item() - 0.3362038			) < 1e-3




				def 					__lowercase  (     self  :List[str]			):
											pass




				def 					__lowercase  (     self  :Tuple			):
											pass




