'''simple docstring'''




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class       UpperCAmelCase__  (       _snake_case			):
   A							:       int     =    (DPMSolverSinglestepScheduler,)
   A							:       List[str]     =    (('''num_inference_steps''', 25),)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Optional[Any]:
        lowercase_					:       Optional[Any]     				=					{
            'num_train_timesteps': 1_000,
            'beta_start': 0.00_01,
            '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(**_a				)
        return config
   def 		_lowerCamelCase (self       ,				_a=0       ,				**_a				)    ->     Tuple:
        lowercase_					:       List[str]     				=					dict(self.forward_default_kwargs				)
        lowercase_					:       List[Any]     				=					kwargs.pop('num_inference_steps'       ,				_a				)
        lowercase_					:       Optional[int]     				=					self.dummy_sample
        lowercase_					:       Optional[Any]     				=					0.1 * sample
        lowercase_					:       List[Any]     				=					[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
             lowercase_					:       Dict     				=					self.get_scheduler_config(**_a				)
             lowercase_					:       Tuple     				=					scheduler_class(**_a				)
             scheduler.set_timesteps(_a				)
             # copy over dummy past residuals
             lowercase_					:       Optional[Any]     				=					dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_a				)
                  lowercase_					:       List[Any]     				=					scheduler_class.from_pretrained(_a				)
                  new_scheduler.set_timesteps(_a				)
                  # copy over dummy past residuals
                  lowercase_					:       Optional[int]     				=					dummy_past_residuals[: new_scheduler.config.solver_order]

             lowercase_					:       str     				=					sample, sample
             for t in range(_a       ,				time_step + scheduler.config.solver_order + 1				):
                  lowercase_					:       Dict     				=					scheduler.step(_a       ,				_a       ,				_a       ,				**_a				).prev_sample
                  lowercase_					:       Dict     				=					new_scheduler.step(_a       ,				_a       ,				_a       ,				**_a				).prev_sample

                  assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"
   def 		_lowerCamelCase (self				)    ->     Tuple:
        pass
   def 		_lowerCamelCase (self       ,				_a=0       ,				**_a				)    ->     Tuple:
        lowercase_					:       Optional[int]     				=					dict(self.forward_default_kwargs				)
        lowercase_					:       List[Any]     				=					kwargs.pop('num_inference_steps'       ,				_a				)
        lowercase_					:       int     				=					self.dummy_sample
        lowercase_					:       int     				=					0.1 * sample
        lowercase_					:       Union[str, Any]     				=					[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
             lowercase_					:       Optional[int]     				=					self.get_scheduler_config()
             lowercase_					:       Dict     				=					scheduler_class(**_a				)
             scheduler.set_timesteps(_a				)

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

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

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

             lowercase_					:       Tuple     				=					scheduler.step(_a       ,				_a       ,				_a       ,				**_a				).prev_sample
             lowercase_					:       Optional[int]     				=					new_scheduler.step(_a       ,				_a       ,				_a       ,				**_a				).prev_sample

             assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"
   def 		_lowerCamelCase (self       ,				_a=None       ,				**_a				)    ->     str:
        if scheduler is None:
             lowercase_					:       List[Any]     				=					self.scheduler_classes[0]
             lowercase_					:       Optional[Any]     				=					self.get_scheduler_config(**_a				)
             lowercase_					:       Any     				=					scheduler_class(**_a				)

        lowercase_					:       Optional[Any]     				=					self.scheduler_classes[0]
        lowercase_					:       List[Any]     				=					self.get_scheduler_config(**_a				)
        lowercase_					:       str     				=					scheduler_class(**_a				)

        lowercase_					:       Tuple     				=					10
        lowercase_					:       List[Any]     				=					self.dummy_model()
        lowercase_					:       Any     				=					self.dummy_sample_deter
        scheduler.set_timesteps(_a				)

        for i, t in enumerate(scheduler.timesteps				):
             lowercase_					:       str     				=					model(_a       ,				_a				)
             lowercase_					:       Any     				=					scheduler.step(_a       ,				_a       ,				_a				).prev_sample

        return sample
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Optional[Any]     				=					DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
        lowercase_					:       Tuple     				=					50
        lowercase_					:       int     				=					self.dummy_model()
        lowercase_					:       Optional[int]     				=					self.dummy_sample_deter
        scheduler.set_timesteps(_a				)

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

        lowercase_					:       Tuple     				=					torch.mean(torch.abs(_a				)				)

        assert abs(result_mean.item() - 0.25_74				) < 1e-3
   def 		_lowerCamelCase (self				)    ->     str:
        for timesteps in [25, 50, 100, 999, 1_000]:
             self.check_over_configs(num_train_timesteps=_a				)
   def 		_lowerCamelCase (self				)    ->     str:
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        lowercase_					:       Optional[Any]     				=					DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
        lowercase_					:       List[str]     				=					self.full_loop(scheduler=_a				)
        lowercase_					:       Union[str, Any]     				=					torch.mean(torch.abs(_a				)				)

        assert abs(result_mean.item() - 0.27_91				) < 1e-3

        lowercase_					:       List[str]     				=					DEISMultistepScheduler.from_config(scheduler.config				)
        lowercase_					:       int     				=					DPMSolverMultistepScheduler.from_config(scheduler.config				)
        lowercase_					:       Dict     				=					UniPCMultistepScheduler.from_config(scheduler.config				)
        lowercase_					:       List[str]     				=					DPMSolverSinglestepScheduler.from_config(scheduler.config				)

        lowercase_					:       List[str]     				=					self.full_loop(scheduler=_a				)
        lowercase_					:       List[str]     				=					torch.mean(torch.abs(_a				)				)

        assert abs(result_mean.item() - 0.27_91				) < 1e-3
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        self.check_over_configs(thresholding=_a				)
        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=_a       ,				prediction_type=_a       ,				sample_max_value=_a       ,				algorithm_type='dpmsolver++'       ,				solver_order=_a       ,				solver_type=_a       ,				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_a				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        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=_a       ,				solver_type=_a       ,				prediction_type=_a       ,				algorithm_type=_a       ,				)
                            lowercase_					:       Tuple     				=					self.full_loop(
                                solver_order=_a       ,				solver_type=_a       ,				prediction_type=_a       ,				algorithm_type=_a       ,				)
                            assert not torch.isnan(_a				).any(), "Samples have nan numbers"
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        self.check_over_configs(lower_order_final=_a				)
        self.check_over_configs(lower_order_final=_a				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        self.check_over_configs(lambda_min_clipped=-float('inf'				)				)
        self.check_over_configs(lambda_min_clipped=-5.1				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        self.check_over_configs(variance_type=_a				)
        self.check_over_configs(variance_type='learned_range'				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
             self.check_over_forward(num_inference_steps=_a       ,				time_step=0				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Optional[Any]     				=					self.full_loop()
        lowercase_					:       List[str]     				=					torch.mean(torch.abs(_a				)				)

        assert abs(result_mean.item() - 0.27_91				) < 1e-3
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Any     				=					self.full_loop(use_karras_sigmas=_a				)
        lowercase_					:       Dict     				=					torch.mean(torch.abs(_a				)				)

        assert abs(result_mean.item() - 0.22_48				) < 1e-3
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       List[Any]     				=					self.full_loop(prediction_type='v_prediction'				)
        lowercase_					:       Optional[Any]     				=					torch.mean(torch.abs(_a				)				)

        assert abs(result_mean.item() - 0.14_53				) < 1e-3
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Optional[int]     				=					self.full_loop(prediction_type='v_prediction'       ,				use_karras_sigmas=_a				)
        lowercase_					:       Optional[Any]     				=					torch.mean(torch.abs(_a				)				)

        assert abs(result_mean.item() - 0.06_49				) < 1e-3





   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       List[str]     				=					self.scheduler_classes[0]
        lowercase_					:       List[Any]     				=					self.get_scheduler_config(thresholding=_a       ,				dynamic_thresholding_ratio=0				)
        lowercase_					:       Optional[int]     				=					scheduler_class(**_a				)

        lowercase_					:       Tuple     				=					10
        lowercase_					:       List[str]     				=					self.dummy_model()
        lowercase_					:       int     				=					self.dummy_sample_deter.half()
        scheduler.set_timesteps(_a				)

        for i, t in enumerate(scheduler.timesteps				):
             lowercase_					:       List[str]     				=					model(_a       ,				_a				)
             lowercase_					:       Any     				=					scheduler.step(_a       ,				_a       ,				_a				).prev_sample

        assert sample.dtype == torch.floataa





'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					tempfile.mkdtemp()

        lowercase_					:       int     				=					[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        lowercase_					:       Union[str, Any]     				=					os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']				)
        with open(self.vocab_file       ,				'w'       ,				encoding='utf-8'				) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)

        lowercase_					:       Union[str, Any]     				=					{
            'do_resize': True,
            'size': 20,
            'do_center_crop': True,
            'crop_size': 18,
            'do_normalize': True,
            'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        lowercase_					:       List[str]     				=					os.path.join(self.tmpdirname       ,				_a				)
        with open(self.image_processor_file       ,				'w'       ,				encoding='utf-8'				) as fp:
             json.dump(_a       ,				_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizerFast.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     int:
        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        shutil.rmtree(self.tmpdirname				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       List[str]     				=					[np.random.randint(255       ,				size=(3, 30, 400)       ,				dtype=np.uinta				)]
        lowercase_					:       Optional[Any]     				=					[Image.fromarray(np.moveaxis(_a       ,				0       ,				-1				)				) for x in image_inputs]
        return image_inputs
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       str     				=					self.get_tokenizer()
        lowercase_					:       Tuple     				=					self.get_rust_tokenizer()
        lowercase_					:       int     				=					self.get_image_processor()

        lowercase_					:       int     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_slow.save_pretrained(self.tmpdirname				)
        lowercase_					:       List[str]     				=					AlignProcessor.from_pretrained(self.tmpdirname       ,				use_fast=_a				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_fast.save_pretrained(self.tmpdirname				)
        lowercase_					:       Any     				=					AlignProcessor.from_pretrained(self.tmpdirname				)

        self.assertEqual(processor_slow.tokenizer.get_vocab()       ,				tokenizer_slow.get_vocab()				)
        self.assertEqual(processor_fast.tokenizer.get_vocab()       ,				tokenizer_fast.get_vocab()				)
        self.assertEqual(tokenizer_slow.get_vocab()       ,				tokenizer_fast.get_vocab()				)
        self.assertIsInstance(processor_slow.tokenizer       ,				_a				)
        self.assertIsInstance(processor_fast.tokenizer       ,				_a				)

        self.assertEqual(processor_slow.image_processor.to_json_string()       ,				image_processor.to_json_string()				)
        self.assertEqual(processor_fast.image_processor.to_json_string()       ,				image_processor.to_json_string()				)
        self.assertIsInstance(processor_slow.image_processor       ,				_a				)
        self.assertIsInstance(processor_fast.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       str     				=					AlignProcessor(tokenizer=self.get_tokenizer()       ,				image_processor=self.get_image_processor()				)
        processor.save_pretrained(self.tmpdirname				)

        lowercase_					:       Tuple     				=					self.get_tokenizer(bos_token='(BOS)'       ,				eos_token='(EOS)'				)
        lowercase_					:       str     				=					self.get_image_processor(do_normalize=_a       ,				padding_value=1.0				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor.from_pretrained(
            self.tmpdirname       ,				bos_token='(BOS)'       ,				eos_token='(EOS)'       ,				do_normalize=_a       ,				padding_value=1.0				)

        self.assertEqual(processor.tokenizer.get_vocab()       ,				tokenizer_add_kwargs.get_vocab()				)
        self.assertIsInstance(processor.tokenizer       ,				_a				)

        self.assertEqual(processor.image_processor.to_json_string()       ,				image_processor_add_kwargs.to_json_string()				)
        self.assertIsInstance(processor.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Union[str, Any]     				=					self.get_image_processor()
        lowercase_					:       Union[str, Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[int]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					image_processor(_a       ,				return_tensors='np'				)
        lowercase_					:       Union[str, Any]     				=					processor(images=_a       ,				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 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       List[Any]     				=					self.get_image_processor()
        lowercase_					:       int     				=					self.get_tokenizer()

        lowercase_					:       Any     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Tuple     				=					'lower newer'

        lowercase_					:       Optional[Any]     				=					processor(text=_a				)

        lowercase_					:       List[Any]     				=					tokenizer(_a       ,				padding='max_length'       ,				max_length=64				)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]       ,				encoded_processor[key]				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					self.get_image_processor()
        lowercase_					:       List[Any]     				=					self.get_tokenizer()

        lowercase_					:       List[str]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[Any]     				=					'lower newer'
        lowercase_					:       List[Any]     				=					self.prepare_image_inputs()

        lowercase_					:       List[str]     				=					processor(text=_a       ,				images=_a				)

        self.assertListEqual(list(inputs.keys()				)       ,				['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']				)

        # test if it raises when no input is passed
        with pytest.raises(_a				):
             processor()
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Dict     				=					self.get_image_processor()
        lowercase_					:       Optional[Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase_					:       str     				=					processor.batch_decode(_a				)
        lowercase_					:       Dict     				=					tokenizer.batch_decode(_a				)

        self.assertListEqual(_a       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       str     				=					self.get_image_processor()
        lowercase_					:       str     				=					self.get_tokenizer()

        lowercase_					:       str     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[int]     				=					'lower newer'
        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					processor(text=_a       ,				images=_a				)

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




