





"""simple docstring"""






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	(DDPMScheduler,)







					def 			_lowerCamelCase		(    self						,      **_UpperCAmelCase  ):
									__a			:    int      							=	{
									    '''num_train_timesteps''': 1000,
									    '''beta_start''': 0.0_0_0_1,
									    '''beta_end''': 0.0_2,
									    '''beta_schedule''': '''linear''',
									    '''variance_type''': '''fixed_small''',
									    '''clip_sample''': True,
									}

									config.update(**_UpperCAmelCase  )
									return config







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







					def 			_lowerCamelCase		(    self  ):
									for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]						,      [0.0_0_2, 0.0_2, 0.2, 2]  ):
													self.check_over_configs(beta_start=_UpperCAmelCase						,      beta_end=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for variance in ["fixed_small", "fixed_large", "other"]:
													self.check_over_configs(variance_type=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									self.check_over_configs(thresholding=_UpperCAmelCase  )
									for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=_UpperCAmelCase						,      prediction_type=_UpperCAmelCase						,      sample_max_value=_UpperCAmelCase						,      )







					def 			_lowerCamelCase		(    self  ):
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(prediction_type=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for t in [0, 500, 999]:
													self.check_over_forward(time_step=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	self.scheduler_classes[0]
									__a			:    Dict      							=	self.get_scheduler_config()
									__a			:    Dict      							=	scheduler_class(**_UpperCAmelCase  )

									assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0_0_9_7_9  )  ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.0_2  )  ) < 1e-5







					def 			_lowerCamelCase		(    self  ):
									__a			:    int      							=	self.scheduler_classes[0]
									__a			:    int      							=	self.get_scheduler_config()
									__a			:    Optional[Any]      							=	scheduler_class(**_UpperCAmelCase  )

									__a			:    int      							=	len(_UpperCAmelCase  )

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

									for t in reversed(range(_UpperCAmelCase  )  ):
													# 1. predict noise residual
													__a			:    Optional[int]      							=	model(_UpperCAmelCase						,      _UpperCAmelCase  )

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

													# if t > 0:
													#     noise = self.dummy_sample_deter
													#     variance = scheduler.get_variance(t) ** (0.5) * noise
													#
													# sample = pred_prev_sample + variance
													__a			:    List[Any]      							=	pred_prev_sample

									__a			:    int      							=	torch.sum(torch.abs(_UpperCAmelCase  )  )
									__a			:    Union[str, Any]      							=	torch.mean(torch.abs(_UpperCAmelCase  )  )

									assert abs(result_sum.item() - 2_5_8.9_6_0_6  ) < 1e-2
									assert abs(result_mean.item() - 0.3_3_7_2  ) < 1e-3







					def 			_lowerCamelCase		(    self  ):
									__a			:    Dict      							=	self.scheduler_classes[0]
									__a			:    int      							=	self.get_scheduler_config(prediction_type='''v_prediction'''  )
									__a			:    int      							=	scheduler_class(**_UpperCAmelCase  )

									__a			:    Union[str, Any]      							=	len(_UpperCAmelCase  )

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

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

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

													# if t > 0:
													#     noise = self.dummy_sample_deter
													#     variance = scheduler.get_variance(t) ** (0.5) * noise
													#
													# sample = pred_prev_sample + variance
													__a			:    Optional[int]      							=	pred_prev_sample

									__a			:    Optional[int]      							=	torch.sum(torch.abs(_UpperCAmelCase  )  )
									__a			:    int      							=	torch.mean(torch.abs(_UpperCAmelCase  )  )

									assert abs(result_sum.item() - 2_0_2.0_2_9_6  ) < 1e-2
									assert abs(result_mean.item() - 0.2_6_3_1  ) < 1e-3







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	self.scheduler_classes[0]
									__a			:    Any      							=	self.get_scheduler_config()
									__a			:    str      							=	scheduler_class(**_UpperCAmelCase  )

									__a			:    Union[str, Any]      							=	[100, 87, 50, 1, 0]

									scheduler.set_timesteps(timesteps=_UpperCAmelCase  )

									__a			:    List[Any]      							=	scheduler.timesteps

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

													__a			:    Dict      							=	scheduler.previous_timestep(_UpperCAmelCase  )
													__a			:    str      							=	prev_t.item()

													self.assertEqual(_UpperCAmelCase						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Tuple      							=	self.scheduler_classes[0]
									__a			:    Dict      							=	self.get_scheduler_config()
									__a			:    Any      							=	scheduler_class(**_UpperCAmelCase  )

									__a			:    Optional[Any]      							=	[100, 87, 50, 51, 0]

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







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	self.scheduler_classes[0]
									__a			:    Optional[Any]      							=	self.get_scheduler_config()
									__a			:    Any      							=	scheduler_class(**_UpperCAmelCase  )

									__a			:    Union[str, Any]      							=	[100, 87, 50, 1, 0]
									__a			:    Optional[int]      							=	len(_UpperCAmelCase  )

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







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[int]      							=	self.scheduler_classes[0]
									__a			:    Optional[Any]      							=	self.get_scheduler_config()
									__a			:    List[str]      							=	scheduler_class(**_UpperCAmelCase  )

									__a			:    List[Any]      							=	[scheduler.config.num_train_timesteps]

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





'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class 		a							(  unittest.TestCase	):
				snake_case_           =						ViTImageProcessor if is_vision_available() else None


				@property
				def     A_					(						self       :   List[str]   ):
									return self.image_processor_tester.prepare_image_processor_dict()


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							(3, 32, 128)
									snake_case_		     =							tempfile.mkdtemp()

									# fmt: off
									snake_case_		     =							['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
									# fmt: on
									snake_case_		     =							dict(zip(lowercase_			,							range(len(lowercase_   )   )   )   )

									snake_case_		     =							os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES['''vocab_file''']   )
									with open(self.vocab_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														fp.write(json.dumps(lowercase_   ) + '''\n'''   )

									snake_case_		     =							{
									    '''do_normalize''': False,
									    '''do_resize''': True,
									    '''image_processor_type''': '''ViTImageProcessor''',
									    '''resample''': 3,
									    '''size''': {'''height''': 32, '''width''': 128},
									}
									snake_case_		     =							os.path.join(self.tmpdirname			,							lowercase_   )
									with open(self.image_processor_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														json.dump(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any			,							**lowercase_       :   str   ):
									return MgpstrTokenizer.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   List[str]			,							**lowercase_       :   List[Any]   ):
									return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   Union[str, Any]   ):
									shutil.rmtree(self.tmpdirname   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta   )

									snake_case_		     =							Image.fromarray(np.moveaxis(lowercase_			,							0			,							-1   )   )

									return image_input


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )
									snake_case_		     =							MgpstrProcessor.from_pretrained(self.tmpdirname			,							use_fast=lowercase_   )

									self.assertEqual(processor.char_tokenizer.get_vocab()			,							tokenizer.get_vocab()   )
									self.assertIsInstance(processor.char_tokenizer			,							lowercase_   )

									self.assertEqual(processor.image_processor.to_json_string()			,							image_processor.to_json_string()   )
									self.assertIsInstance(processor.image_processor			,							lowercase_   )


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )

									snake_case_		     =							self.get_tokenizer(bos_token='''(BOS)'''			,							eos_token='''(EOS)'''   )
									snake_case_		     =							self.get_image_processor(do_normalize=lowercase_			,							padding_value=1.0   )

									snake_case_		     =							MgpstrProcessor.from_pretrained(
									    self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=lowercase_			,							padding_value=1.0   )

									self.assertEqual(processor.char_tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()   )
									self.assertIsInstance(processor.char_tokenizer			,							lowercase_   )

									self.assertEqual(processor.image_processor.to_json_string()			,							image_processor_add_kwargs.to_json_string()   )
									self.assertIsInstance(processor.image_processor			,							lowercase_   )


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							image_processor(lowercase_			,							return_tensors='''np'''   )
									snake_case_		     =							processor(images=lowercase_			,							return_tensors='''np'''   )

									for key in input_image_proc.keys():
														self.assertAlmostEqual(input_image_proc[key].sum()			,							input_processor[key].sum()			,							delta=1e-2   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''test'''

									snake_case_		     =							processor(text=lowercase_   )

									snake_case_		     =							tokenizer(lowercase_   )
									for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''test'''
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

									self.assertListEqual(list(inputs.keys()   )			,							['''pixel_values''', '''labels''']   )

									# test if it raises when no input is passed
									with pytest.raises(lowercase_   ):
														processor()


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

									snake_case_		     =							processor.char_decode(lowercase_   )
									snake_case_		     =							tokenizer.batch_decode(lowercase_   )
									snake_case_		     =							[seq.replace(''' '''			,							''''''   ) for seq in decoded_tok]

									self.assertListEqual(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							None
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

									self.assertListEqual(list(inputs.keys()   )			,							processor.model_input_names   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							torch.randn(1			,							27			,							38   )
									snake_case_		     =							torch.randn(1			,							27			,							5_0257   )
									snake_case_		     =							torch.randn(1			,							27			,							3_0522   )

									snake_case_		     =							processor.batch_decode([char_input, bpe_input, wp_input]   )

									self.assertListEqual(list(results.keys()   )			,							['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']   )

