

'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						DDIMPipeline
				lowerCamelCase_							=						UNCONDITIONAL_IMAGE_GENERATION_PARAMS
				lowerCamelCase_							=						PipelineTesterMixin.required_optional_params - {
				    'num_images_per_prompt',
				    'latents',
				    'callback',
				    'callback_steps',
				}
				lowerCamelCase_							=						UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
				lowerCamelCase_							=						False







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										torch.manual_seed(0      )
										lowercase   :       Tuple								=UNetaDModel(
										    block_out_channels=(32, 64)		,				layers_per_block=2		,				sample_size=32		,				in_channels=3		,				out_channels=3		,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,				)
										lowercase   :       Dict								=DDIMScheduler()
										lowercase   :       Tuple								={'''unet''': unet, '''scheduler''': scheduler}
										return components







				def    lowerCamelCase_  (					self    :		int		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Dict=0      ):
										'''simple docstring'''
										if str(UpperCAmelCase__      ).startswith('''mps'''      ):
																lowercase   :       Tuple								=torch.manual_seed(UpperCAmelCase__      )
										else:
																lowercase   :       Optional[Any]								=torch.Generator(device=UpperCAmelCase__      ).manual_seed(UpperCAmelCase__      )
										lowercase   :       Tuple								={
										    '''batch_size''': 1,
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''numpy''',
										}
										return inputs







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Any								='''cpu'''

										lowercase   :       Dict								=self.get_dummy_components()
										lowercase   :       Any								=self.pipeline_class(**UpperCAmelCase__      )
										pipe.to(UpperCAmelCase__      )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       int								=self.get_dummy_inputs(UpperCAmelCase__      )
										lowercase   :       int								=pipe(**UpperCAmelCase__      ).images
										lowercase   :       Dict								=image[0, -3:, -3:, -1]

										self.assertEqual(image.shape		,				(1, 32, 32, 3)      )
										lowercase   :       str								=np.array(
										    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]      )
										lowercase   :       Any								=np.abs(image_slice.flatten() - expected_slice      ).max()
										self.assertLessEqual(UpperCAmelCase__		,				1E-3      )







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										super().test_save_load_local(expected_max_difference=3E-3      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										super().test_save_load_optional_components(expected_max_difference=3E-3      )







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										super().test_inference_batch_single_identical(expected_max_diff=3E-3      )





@slow
@require_torch_gpu
class 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       List[str]								='''google/ddpm-cifar10-32'''

										lowercase   :       Optional[Any]								=UNetaDModel.from_pretrained(UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=DDIMScheduler()

										lowercase   :       List[str]								=DDIMPipeline(unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__      )
										ddim.to(UpperCAmelCase__      )
										ddim.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								=torch.manual_seed(0      )
										lowercase   :       Tuple								=ddim(generator=UpperCAmelCase__		,				eta=0.0		,				output_type='''numpy'''      ).images

										lowercase   :       Tuple								=image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowercase   :       Optional[Any]								=np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								='''google/ddpm-ema-bedroom-256'''

										lowercase   :       List[Any]								=UNetaDModel.from_pretrained(UpperCAmelCase__      )
										lowercase   :       Optional[int]								=DDIMScheduler.from_pretrained(UpperCAmelCase__      )

										lowercase   :       Optional[int]								=DDIMPipeline(unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__      )
										ddpm.to(UpperCAmelCase__      )
										ddpm.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       Dict								=torch.manual_seed(0      )
										lowercase   :       int								=ddpm(generator=UpperCAmelCase__		,				output_type='''numpy'''      ).images

										lowercase   :       Optional[int]								=image[0, -3:, -3:, -1]

										assert image.shape == (1, 256, 256, 3)
										lowercase   :       int								=np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2








'''simple docstring'''





import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						None
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						True
				lowerCamelCase_							=						False
				lowerCamelCase_							=						'tokenizer_file'
				lowerCamelCase_							=						{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										super().setUp()
										lowercase   :       Union[str, Any]								=BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''      )
										tokenizer.save_pretrained(self.tmpdirname      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				**UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map      )
										return BloomTokenizerFast.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       str								=self.get_rust_tokenizer()

										lowercase   :       List[str]								=['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
										lowercase   :       Any								=[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

										lowercase   :       Any								=tokenizer.batch_encode_plus(UpperCAmelCase__      )['''input_ids''']
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       int								=tokenizer.batch_decode(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		Any=6      ):
										'''simple docstring'''
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																						lowercase   :       Optional[int]								=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__		,				**UpperCAmelCase__      )
																						# tokenizer_r.pad_token = None # Hotfixing padding = None
																						# Simple input
																						lowercase   :       Tuple								='''This is a simple input'''
																						lowercase   :       int								=['''This is a simple input 1''', '''This is a simple input 2''']
																						lowercase   :       Optional[Any]								=('''This is a simple input''', '''This is a pair''')
																						lowercase   :       int								=[
																						    ('''This is a simple input 1''', '''This is a simple input 2'''),
																						    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																						]

																						# Simple input tests
																						try:
																												tokenizer_r.encode(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )

																												tokenizer_r.batch_encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.encode(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.batch_encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																						except ValueError:
																												self.fail('''Bloom Tokenizer should be able to deal with padding'''      )

																						lowercase   :       Optional[int]								=None  # Hotfixing padding = None
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Simple input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Simple input
																						self.assertRaises(
																						    UpperCAmelCase__		,				tokenizer_r.batch_encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''		,				)

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Pair input
																						self.assertRaises(
																						    UpperCAmelCase__		,				tokenizer_r.batch_encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''		,				)







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.get_rust_tokenizer()
										lowercase   :       Dict								=load_dataset('''xnli'''		,				'''all_languages'''		,				split='''test'''		,				streaming=UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								=next(iter(UpperCAmelCase__      )      )['''premise''']  # pick up one data
										lowercase   :       int								=list(sample_data.values()      )

										lowercase   :       Any								=list(map(tokenizer.encode		,				UpperCAmelCase__      )      )
										lowercase   :       List[str]								=[tokenizer.decode(UpperCAmelCase__		,				clean_up_tokenization_spaces=UpperCAmelCase__      ) for x in output_tokens]
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
										# any sequence length constraints. This test of the parent class will fail since it relies on the
										# maximum sequence length of the positoonal embeddings.
										self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map      )		,				1      )
										self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()      )[0]      )		,				1      )





