






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= (DPMSolverSinglestepScheduler,)
				lowercase__       							= (("""num_inference_steps""", 25),)




				def        lowercase__     (  self		:					List[Any],						**lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    {
								    '''num_train_timesteps''': 1_000,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								    '''prediction_type''': '''epsilon''',
								    '''thresholding''': False,
								    '''sample_max_value''': 1.0,
								    '''algorithm_type''': '''dpmsolver++''',
								    '''solver_type''': '''midpoint''',
								    '''lambda_min_clipped''': -float('''inf'''						),
								    '''variance_type''': None,
								}

								config.update(**lowerCamelCase						)
								return config




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					str=0,						**lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    dict(self.forward_default_kwargs						)
								lowercase__     				=    kwargs.pop('''num_inference_steps''',						lowerCamelCase						)
								lowercase__     				=    self.dummy_sample
								lowercase__     				=    0.1 * sample
								lowercase__     				=    [residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
												lowercase__     				=    self.get_scheduler_config(**lowerCamelCase						)
												lowercase__     				=    scheduler_class(**lowerCamelCase						)
												scheduler.set_timesteps(lowerCamelCase						)
												# copy over dummy past residuals
												lowercase__     				=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase						)
																lowercase__     				=    scheduler_class.from_pretrained(lowerCamelCase						)
																new_scheduler.set_timesteps(lowerCamelCase						)
																# copy over dummy past residuals
																lowercase__     				=    dummy_past_residuals[: new_scheduler.config.solver_order]

												lowercase__				,		lowercase__     				=    sample, sample
												for t in range(lowerCamelCase,						time_step + scheduler.config.solver_order + 1						):
																lowercase__     				=    scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample
																lowercase__     				=    new_scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample

																assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Optional[int]=0,						**lowerCamelCase		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    dict(self.forward_default_kwargs						)
								lowercase__     				=    kwargs.pop('''num_inference_steps''',						lowerCamelCase						)
								lowercase__     				=    self.dummy_sample
								lowercase__     				=    0.1 * sample
								lowercase__     				=    [residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
												lowercase__     				=    self.get_scheduler_config()
												lowercase__     				=    scheduler_class(**lowerCamelCase						)
												scheduler.set_timesteps(lowerCamelCase						)

												# copy over dummy past residuals (must be after setting timesteps)
												lowercase__     				=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase						)
																lowercase__     				=    scheduler_class.from_pretrained(lowerCamelCase						)
																# copy over dummy past residuals
																new_scheduler.set_timesteps(lowerCamelCase						)

																# copy over dummy past residual (must be after setting timesteps)
																lowercase__     				=    dummy_past_residuals[: new_scheduler.config.solver_order]

												lowercase__     				=    scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample
												lowercase__     				=    new_scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample

												assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					List[str]=None,						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								if scheduler is None:
												lowercase__     				=    self.scheduler_classes[0]
												lowercase__     				=    self.get_scheduler_config(**lowerCamelCase						)
												lowercase__     				=    scheduler_class(**lowerCamelCase						)

								lowercase__     				=    self.scheduler_classes[0]
								lowercase__     				=    self.get_scheduler_config(**lowerCamelCase						)
								lowercase__     				=    scheduler_class(**lowerCamelCase						)

								lowercase__     				=    10
								lowercase__     				=    self.dummy_model()
								lowercase__     				=    self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase						)

								for i, t in enumerate(scheduler.timesteps						):
												lowercase__     				=    model(lowerCamelCase,						lowerCamelCase						)
												lowercase__     				=    scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						).prev_sample

								return sample




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
								lowercase__     				=    50
								lowercase__     				=    self.dummy_model()
								lowercase__     				=    self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase						)

								# make sure that the first t is uneven
								for i, t in enumerate(scheduler.timesteps[3:]						):
												lowercase__     				=    model(lowerCamelCase,						lowerCamelCase						)
												lowercase__     				=    scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						).prev_sample

								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.2574						) < 1E-3




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								for timesteps in [25, 50, 100, 999, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
								lowercase__     				=    self.full_loop(scheduler=lowerCamelCase						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.2791						) < 1E-3

								lowercase__     				=    DEISMultistepScheduler.from_config(scheduler.config						)
								lowercase__     				=    DPMSolverMultistepScheduler.from_config(scheduler.config						)
								lowercase__     				=    UniPCMultistepScheduler.from_config(scheduler.config						)
								lowercase__     				=    DPMSolverSinglestepScheduler.from_config(scheduler.config						)

								lowercase__     				=    self.full_loop(scheduler=lowerCamelCase						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.2791						) < 1E-3




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								self.check_over_configs(thresholding=lowerCamelCase						)
								for order in [1, 2, 3]:
												for solver_type in ["midpoint", "heun"]:
																for threshold in [0.5, 1.0, 2.0]:
																				for prediction_type in ["epsilon", "sample"]:
																								self.check_over_configs(
																								    thresholding=lowerCamelCase,						prediction_type=lowerCamelCase,						sample_max_value=lowerCamelCase,						algorithm_type='''dpmsolver++''',						solver_order=lowerCamelCase,						solver_type=lowerCamelCase,						)




				def        lowercase__     (  self		:					Dict						):

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




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								for algorithm_type in ["dpmsolver", "dpmsolver++"]:
												for solver_type in ["midpoint", "heun"]:
																for order in [1, 2, 3]:
																				for prediction_type in ["epsilon", "sample"]:
																								self.check_over_configs(
																								    solver_order=lowerCamelCase,						solver_type=lowerCamelCase,						prediction_type=lowerCamelCase,						algorithm_type=lowerCamelCase,						)
																								lowercase__     				=    self.full_loop(
																								    solver_order=lowerCamelCase,						solver_type=lowerCamelCase,						prediction_type=lowerCamelCase,						algorithm_type=lowerCamelCase,						)
																								assert not torch.isnan(lowerCamelCase						).any(), "Samples have nan numbers"




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								self.check_over_configs(lower_order_final=lowerCamelCase						)
								self.check_over_configs(lower_order_final=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								self.check_over_configs(lambda_min_clipped=-float('''inf'''						)						)
								self.check_over_configs(lambda_min_clipped=-5.1						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								self.check_over_configs(variance_type=lowerCamelCase						)
								self.check_over_configs(variance_type='''learned_range'''						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
												self.check_over_forward(num_inference_steps=lowerCamelCase,						time_step=0						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.full_loop()
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.2791						) < 1E-3




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.full_loop(use_karras_sigmas=lowerCamelCase						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.2248						) < 1E-3




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.full_loop(prediction_type='''v_prediction'''						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.1453						) < 1E-3




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.full_loop(prediction_type='''v_prediction''',						use_karras_sigmas=lowerCamelCase						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.0649						) < 1E-3




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.scheduler_classes[0]
								lowercase__     				=    self.get_scheduler_config(thresholding=lowerCamelCase,						dynamic_thresholding_ratio=0						)
								lowercase__     				=    scheduler_class(**lowerCamelCase						)

								lowercase__     				=    10
								lowercase__     				=    self.dummy_model()
								lowercase__     				=    self.dummy_sample_deter.half()
								scheduler.set_timesteps(lowerCamelCase						)

								for i, t in enumerate(scheduler.timesteps						):
												lowercase__     				=    model(lowerCamelCase,						lowerCamelCase						)
												lowercase__     				=    scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						).prev_sample

								assert sample.dtype == torch.floataa












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    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= ViTImageProcessor if is_vision_available() else None




				@property
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    (3, 32, 128)
								lowercase__     				=    tempfile.mkdtemp()

								# fmt: off
								lowercase__     				=    ['''[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
								lowercase__     				=    dict(zip(lowerCamelCase,						range(len(lowerCamelCase						)						)						)						)

								lowercase__     				=    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(lowerCamelCase						) + '''\n'''						)

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




				def        lowercase__     (  self		:					int,						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								return MgpstrTokenizer.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					str,						**lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								return ViTImageProcessor.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								shutil.rmtree(self.tmpdirname						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    np.random.randint(255,						size=(3, 30, 400),						dtype=np.uinta						)

								lowercase__     				=    Image.fromarray(np.moveaxis(lowerCamelCase,						0,						-1						)						)

								return image_input




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_image_processor()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor.save_pretrained(self.tmpdirname						)
								lowercase__     				=    MgpstrProcessor.from_pretrained(self.tmpdirname,						use_fast=lowerCamelCase						)

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

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




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_image_processor()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor.save_pretrained(self.tmpdirname						)

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

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

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

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




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    image_processor(lowerCamelCase,						return_tensors='''np'''						)
								lowercase__     				=    processor(images=lowerCamelCase,						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        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    '''test'''

								lowercase__     				=    processor(text=lowerCamelCase						)

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




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

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

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

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

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




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

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

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

								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    None
								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

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




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    torch.randn(1,						27,						38						)
								lowercase__     				=    torch.randn(1,						27,						50_257						)
								lowercase__     				=    torch.randn(1,						27,						30_522						)

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

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




