




import json
import os
import random
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 FlavaImageProcessor, FlavaProcessor
		from transformers.models.flava.image_processing_flava import (
		    FLAVA_CODEBOOK_MEAN,
		    FLAVA_CODEBOOK_STD,
		    FLAVA_IMAGE_MEAN,
		    FLAVA_IMAGE_STD,
		)
@require_vision
class 		UpperCAmelCase       (    unittest.TestCase      ):







						'''simple docstring'''





						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					tempfile.mkdtemp()

							# fmt: off
							snake_case_		   =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
							# fmt: on
							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("".join([x + "\n" for x in vocab_tokens]						)						)

							snake_case_		   =					{
							    "image_mean": FLAVA_IMAGE_MEAN,
							    "image_std": FLAVA_IMAGE_STD,
							    "do_normalize": True,
							    "do_resize": True,
							    "size": 2_24,
							    "do_center_crop": True,
							    "crop_size": 2_24,
							    "input_size_patches": 14,
							    "total_mask_patches": 75,
							    "mask_group_max_patches": None,
							    "mask_group_min_patches": 16,
							    "mask_group_min_aspect_ratio": 0.3,
							    "mask_group_max_aspect_ratio": None,
							    "codebook_do_resize": True,
							    "codebook_size": 1_12,
							    "codebook_do_center_crop": True,
							    "codebook_crop_size": 1_12,
							    "codebook_do_map_pixels": True,
							    "codebook_do_normalize": True,
							    "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
							    "codebook_image_std": FLAVA_CODEBOOK_STD,
							}

							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 	snake_case__						(						self							:					Optional[Any]	,     **__lowercase							:					int						):






							"""simple docstring"""






							return BertTokenizer.from_pretrained(self.tmpdirname	,     **__lowercase						)






						def 	snake_case__						(						self							:					Any	,     **__lowercase							:					List[str]						):






							"""simple docstring"""






							return BertTokenizerFast.from_pretrained(self.tmpdirname	,     **__lowercase						)






						def 	snake_case__						(						self							:					List[Any]	,     **__lowercase							:					int						):






							"""simple docstring"""






							return FlavaImageProcessor.from_pretrained(self.tmpdirname	,     **__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							shutil.rmtree(self.tmpdirname						)






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					[np.random.randint(2_55	,     size=(3, 30, 4_00)	,     dtype=np.uinta						)]

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

							return image_inputs






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






							"""simple docstring"""






							snake_case_		   =					self.get_tokenizer()
							snake_case_		   =					self.get_rust_tokenizer()
							snake_case_		   =					self.get_image_processor()

							snake_case_		   =					FlavaProcessor(tokenizer=__lowercase	,     image_processor=__lowercase						)
							processor_slow.save_pretrained(self.tmpdirname						)
							snake_case_		   =					FlavaProcessor.from_pretrained(self.tmpdirname	,     use_fast=__lowercase						)

							snake_case_		   =					FlavaProcessor(tokenizer=__lowercase	,     image_processor=__lowercase						)
							processor_fast.save_pretrained(self.tmpdirname						)
							snake_case_		   =					FlavaProcessor.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	,     __lowercase						)
							self.assertIsInstance(processor_fast.tokenizer	,     __lowercase						)

							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	,     __lowercase						)
							self.assertIsInstance(processor_fast.image_processor	,     __lowercase						)






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					FlavaProcessor(tokenizer=self.get_tokenizer()	,     image_processor=self.get_image_processor()						)
							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_		   =					FlavaProcessor.from_pretrained(
							    self.tmpdirname	,     bos_token="(BOS)"	,     eos_token="(EOS)"	,     do_normalize=__lowercase	,     padding_value=1.0						)

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

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






						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






							snake_case_		   =					self.get_image_processor()
							snake_case_		   =					self.get_tokenizer()

							snake_case_		   =					FlavaProcessor(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_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()	,     input_processor[key].sum()	,     delta=1E-2						)

							# With rest of the args
							random.seed(12_34						)
							snake_case_		   =					image_processor(
							    __lowercase	,     return_image_mask=__lowercase	,     return_codebook_pixels=__lowercase	,     return_tensors="np"						)
							random.seed(12_34						)
							snake_case_		   =					processor(
							    images=__lowercase	,     return_image_mask=__lowercase	,     return_codebook_pixels=__lowercase	,     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							:					str						):






							"""simple docstring"""






							snake_case_		   =					self.get_image_processor()
							snake_case_		   =					self.get_tokenizer()

							snake_case_		   =					FlavaProcessor(tokenizer=__lowercase	,     image_processor=__lowercase						)

							snake_case_		   =					"lower newer"

							snake_case_		   =					processor(text=__lowercase						)

							snake_case_		   =					tokenizer(__lowercase						)

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






						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






							snake_case_		   =					self.get_image_processor()
							snake_case_		   =					self.get_tokenizer()

							snake_case_		   =					FlavaProcessor(tokenizer=__lowercase	,     image_processor=__lowercase						)

							snake_case_		   =					"lower newer"
							snake_case_		   =					self.prepare_image_inputs()

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

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

							# add extra args
							snake_case_		   =					processor(text=__lowercase	,     images=__lowercase	,     return_codebook_pixels=__lowercase	,     return_image_mask=__lowercase						)

							self.assertListEqual(
							    list(inputs.keys()						)	,     [
							        "input_ids",
							        "token_type_ids",
							        "attention_mask",
							        "pixel_values",
							        "codebook_pixel_values",
							        "bool_masked_pos",
							    ]	,     )

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






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							snake_case_		   =					self.get_image_processor()
							snake_case_		   =					self.get_tokenizer()

							snake_case_		   =					FlavaProcessor(tokenizer=__lowercase	,     image_processor=__lowercase						)

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

							snake_case_		   =					processor.batch_decode(__lowercase						)
							snake_case_		   =					tokenizer.batch_decode(__lowercase						)

							self.assertListEqual(__lowercase	,     __lowercase						)






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






							"""simple docstring"""






							snake_case_		   =					self.get_image_processor()
							snake_case_		   =					self.get_tokenizer()

							snake_case_		   =					FlavaProcessor(tokenizer=__lowercase	,     image_processor=__lowercase						)

							snake_case_		   =					"lower newer"
							snake_case_		   =					self.prepare_image_inputs()

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

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






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_							=       10






						def 	snake_case__						(						self							:					Tuple	,     **__lowercase							:					int						):






							"""simple docstring"""






							snake_case_		   =					{
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config






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






							"""simple docstring"""






							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]						):
								self.check_over_configs(beta_start=__lowercase	,     beta_end=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for schedule in ["linear", "scaled_linear"]:
								self.check_over_configs(beta_schedule=__lowercase						)






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






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config(prediction_type="v_prediction"						)
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 108.4439						) < 1E-2
							assert abs(result_mean.item() - 0.1412						) < 1E-3






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps	,     device=__lowercase						)
							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for t in scheduler.timesteps:
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3
