


"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Any				      =(DEISMultistepScheduler,)
							__UpperCAmelCase						:    Optional[int]				      =(("""num_inference_steps""", 2_5),)

							def 			snake_case				(     self    ,   **__a	):
										__lowerCAmelCase				    =			{
										    "num_train_timesteps": 10_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										}

										config.update(**__a	)
										return config

							def 			snake_case				(     self    ,   __a=0    ,   **__a	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)
										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   __a	)
										__lowerCAmelCase				    =			self.dummy_sample
										__lowerCAmelCase				    =			0.1 * sample
										__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

													__lowerCAmelCase    ,	__lowerCAmelCase				    =			sample, sample
													for t in range(__a    ,   time_step + scheduler.config.solver_order + 1	):
																__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample
																__lowerCAmelCase				    =			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 			snake_case				(     self	):
										pass

							def 			snake_case				(     self    ,   __a=0    ,   **__a	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)
										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   __a	)
										__lowerCAmelCase				    =			self.dummy_sample
										__lowerCAmelCase				    =			0.1 * sample
										__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase				    =			self.get_scheduler_config()
													__lowerCAmelCase				    =			scheduler_class(**__a	)
													scheduler.set_timesteps(__a	)

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__a	)
																__lowerCAmelCase				    =			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)
																__lowerCAmelCase				    =			dummy_past_residuals[: new_scheduler.config.solver_order]

													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample
													__lowerCAmelCase				    =			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 			snake_case				(     self    ,   __a=None    ,   **__a	):
										if scheduler is None:
													__lowerCAmelCase				    =			self.scheduler_classes[0]
													__lowerCAmelCase				    =			self.get_scheduler_config(**__a	)
													__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config(**__a	)
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			10
										__lowerCAmelCase				    =			self.dummy_model()
										__lowerCAmelCase				    =			self.dummy_sample_deter
										scheduler.set_timesteps(__a	)

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

										return sample

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)

										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   __a	)

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase				    =			self.get_scheduler_config()
													__lowerCAmelCase				    =			scheduler_class(**__a	)

													__lowerCAmelCase				    =			self.dummy_sample
													__lowerCAmelCase				    =			0.1 * sample

													if num_inference_steps is not None and hasattr(__a    ,   "set_timesteps"	):
																scheduler.set_timesteps(__a	)
													elif num_inference_steps is not None and not hasattr(__a    ,   "set_timesteps"	):
																__lowerCAmelCase				    =			num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
													__lowerCAmelCase				    =			dummy_past_residuals[: scheduler.config.solver_order]

													__lowerCAmelCase				    =			scheduler.timesteps[5]
													__lowerCAmelCase				    =			scheduler.timesteps[6]

													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample
													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample

													self.assertEqual(output_a.shape    ,   sample.shape	)
													self.assertEqual(output_a.shape    ,   output_a.shape	)

							def 			snake_case				(     self	):
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										__lowerCAmelCase				    =			DEISMultistepScheduler(**self.get_scheduler_config()	)
										__lowerCAmelCase				    =			self.full_loop(scheduler=__a	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1e-3

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

										__lowerCAmelCase				    =			self.full_loop(scheduler=__a	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1e-3

							def 			snake_case				(     self	):
										for timesteps in [25, 50, 1_00, 9_99, 10_00]:
													self.check_over_configs(num_train_timesteps=__a	)

							def 			snake_case				(     self	):
										self.check_over_configs(thresholding=__a	)
										for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																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="deis"    ,   solver_order=__a    ,   solver_type=__a    ,   )

							def 			snake_case				(     self	):
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=__a	)

							def 			snake_case				(     self	):
										for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																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    ,   )
																						__lowerCAmelCase				    =			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 			snake_case				(     self	):
										self.check_over_configs(lower_order_final=__a	)
										self.check_over_configs(lower_order_final=__a	)

							def 			snake_case				(     self	):
										for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
													self.check_over_forward(num_inference_steps=__a    ,   time_step=0	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.full_loop()
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1e-3

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.full_loop(prediction_type="v_prediction"	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.0_9_1	) < 1e-3



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config(thresholding=__a    ,   dynamic_thresholding_ratio=0	)
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			10
										__lowerCAmelCase				    =			self.dummy_model()
										__lowerCAmelCase				    =			self.dummy_sample_deter.half()
										scheduler.set_timesteps(__a	)

										for i, t in enumerate(scheduler.timesteps	):
													__lowerCAmelCase				    =			model(__a    ,   __a	)
													__lowerCAmelCase				    =			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 FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			tempfile.mkdtemp()

										__lowerCAmelCase				    =			[
										    "[UNK]",
										    "[CLS]",
										    "[SEP]",
										    "[PAD]",
										    "[MASK]",
										    "的",
										    "价",
										    "格",
										    "是",
										    "15",
										    "便",
										    "alex",
										    "##andra",
										    "，",
										    "。",
										    "-",
										    "t",
										    "shirt",
										]
										__lowerCAmelCase				    =			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]	)	)

										__lowerCAmelCase				    =			{
										    "do_resize": True,
										    "size": {"height": 2_24, "width": 2_24},
										    "do_center_crop": True,
										    "crop_size": {"height": 18, "width": 18},
										    "do_normalize": True,
										    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
										    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
										    "do_convert_rgb": True,
										}
										__lowerCAmelCase				    =			os.path.join(self.tmpdirname    ,   __a	)
										with open(self.image_processor_file    ,   "w"    ,   encoding="utf-8"	) as fp:
													json.dump(__a    ,   __a	)

							def 			snake_case				(     self    ,   **__a	):
										return BertTokenizer.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self    ,   **__a	):
										return BertTokenizerFast.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self    ,   **__a	):
										return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self	):
										shutil.rmtree(self.tmpdirname	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			[np.random.randint(2_55    ,   size=(3, 30, 4_00)    ,   dtype=np.uinta	)]

										__lowerCAmelCase				    =			[Image.fromarray(np.moveaxis(__a    ,   0    ,   -1	)	) for x in image_inputs]

										return image_inputs

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_tokenizer()
										__lowerCAmelCase				    =			self.get_rust_tokenizer()
										__lowerCAmelCase				    =			self.get_image_processor()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)
										processor_slow.save_pretrained(self.tmpdirname	)
										__lowerCAmelCase				    =			ChineseCLIPProcessor.from_pretrained(self.tmpdirname    ,   use_fast=__a	)

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)
										processor_fast.save_pretrained(self.tmpdirname	)
										__lowerCAmelCase				    =			ChineseCLIPProcessor.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 			snake_case				(     self	):
										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=self.get_tokenizer()    ,   image_processor=self.get_image_processor()	)
										processor.save_pretrained(self.tmpdirname	)

										__lowerCAmelCase				    =			self.get_tokenizer(cls_token="(CLS)"    ,   sep_token="(SEP)"	)
										__lowerCAmelCase				    =			self.get_image_processor(do_normalize=__a	)

										__lowerCAmelCase				    =			ChineseCLIPProcessor.from_pretrained(
										    self.tmpdirname    ,   cls_token="(CLS)"    ,   sep_token="(SEP)"    ,   do_normalize=__a	)

										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 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			self.prepare_image_inputs()

										__lowerCAmelCase				    =			image_processor(__a    ,   return_tensors="np"	)
										__lowerCAmelCase				    =			processor(images=__a    ,   return_tensors="np"	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			"Alexandra，T-shirt的价格是15便士。"

										__lowerCAmelCase				    =			processor(text=__a	)

										__lowerCAmelCase				    =			tokenizer(__a	)

										for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]    ,   encoded_processor[key]	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			"Alexandra，T-shirt的价格是15便士。"
										__lowerCAmelCase				    =			self.prepare_image_inputs()

										__lowerCAmelCase				    =			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 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

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

										__lowerCAmelCase				    =			processor.batch_decode(__a	)
										__lowerCAmelCase				    =			tokenizer.batch_decode(__a	)

										self.assertListEqual(__a    ,   __a	)



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			"Alexandra，T-shirt的价格是15便士。"
										__lowerCAmelCase				    =			self.prepare_image_inputs()

										__lowerCAmelCase				    =			processor(text=__a    ,   images=__a	)

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




