
"""simple docstring"""


import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
		from transformers.models.rag.configuration_rag import RagConfig
		from transformers.models.rag.tokenization_rag import RagTokenizer







@require_faiss
@require_torch
class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Dict:
							A        =      tempfile.mkdtemp()
							A        =      8

							# DPR tok
							A        =      [
							    '[UNK]',
							    '[CLS]',
							    '[SEP]',
							    '[PAD]',
							    '[MASK]',
							    'want',
							    '##want',
							    '##ed',
							    'wa',
							    'un',
							    'runn',
							    '##ing',
							    ',',
							    'low',
							    'lowest',
							]
							A        =      os.path.join(self.tmpdirname     ,'dpr_tokenizer'					)
							os.makedirs(A_     ,exist_ok=A_					)
							A        =      os.path.join(A_     ,DPR_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]					)					)

							# BART tok
							A        =      [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							A        =      dict(zip(A_     ,range(len(A_					)					)					)					)
							A        =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							A        =      {'unk_token': '<unk>'}

							A        =      os.path.join(self.tmpdirname     ,'bart_tokenizer'					)
							os.makedirs(A_     ,exist_ok=A_					)
							A        =      os.path.join(A_     ,BART_VOCAB_FILES_NAMES['vocab_file']					)
							A        =      os.path.join(A_     ,BART_VOCAB_FILES_NAMES['merges_file']					)
							with open(self.vocab_file     ,'w'     ,encoding='utf-8'					) as fp:
								fp.write(json.dumps(A_					) + '\n'					)
							with open(self.merges_file     ,'w'     ,encoding='utf-8'					) as fp:
								fp.write('\n'.join(A_					)					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      DPRQuestionEncoderTokenizer:
							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,'dpr_tokenizer'					)					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      BartTokenizer:
							return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,'bart_tokenizer'					)					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      Optional[int]:
							shutil.rmtree(self.tmpdirname					)


						@require_tokenizers
						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      List[str]:
							A        =      os.path.join(self.tmpdirname     ,'rag_tokenizer'					)
							A        =      RagConfig(question_encoder=DPRConfig().to_dict()     ,generator=BartConfig().to_dict()					)
							A        =      RagTokenizer(question_encoder=self.get_dpr_tokenizer()     ,generator=self.get_bart_tokenizer()					)
							rag_config.save_pretrained(A_					)
							rag_tokenizer.save_pretrained(A_					)
							A        =      RagTokenizer.from_pretrained(A_     ,config=A_					)
							self.assertIsInstance(new_rag_tokenizer.question_encoder     ,A_					)
							self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()     ,rag_tokenizer.question_encoder.get_vocab()					)
							self.assertIsInstance(new_rag_tokenizer.generator     ,A_					)
							self.assertEqual(new_rag_tokenizer.generator.get_vocab()     ,rag_tokenizer.generator.get_vocab()					)


						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      str:
							A        =      RagTokenizer.from_pretrained('facebook/rag-token-nq'					)
							A        =      [
							    'who got the first nobel prize in physics',
							    'when is the next deadpool movie being released',
							    'which mode is used for short wave broadcast service',
							    'who is the owner of reading football club',
							    'when is the next scandal episode coming out',
							    'when is the last time the philadelphia won the superbowl',
							    'what is the most current adobe flash player version',
							    'how many episodes are there in dragon ball z',
							    'what is the first step in the evolution of the eye',
							    'where is gall bladder situated in human body',
							    'what is the main mineral in lithium batteries',
							    'who is the president of usa right now',
							    'where do the greasers live in the outsiders',
							    'panda is a national animal of which country',
							    'what is the name of manchester united stadium',
							]
							A        =      tokenizer(A_					)
							self.assertIsNotNone(A_					)




						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      Optional[int]:
							A        =      RagTokenizer.from_pretrained('facebook/rag-sequence-nq'					)
							A        =      [
							    'who got the first nobel prize in physics',
							    'when is the next deadpool movie being released',
							    'which mode is used for short wave broadcast service',
							    'who is the owner of reading football club',
							    'when is the next scandal episode coming out',
							    'when is the last time the philadelphia won the superbowl',
							    'what is the most current adobe flash player version',
							    'how many episodes are there in dragon ball z',
							    'what is the first step in the evolution of the eye',
							    'where is gall bladder situated in human body',
							    'what is the main mineral in lithium batteries',
							    'who is the president of usa right now',
							    'where do the greasers live in the outsiders',
							    'panda is a national animal of which country',
							    'what is the name of manchester united stadium',
							]
							A        =      tokenizer(A_					)
							self.assertIsNotNone(A_					)




"""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	(      lowerCAmelCase_		,   unittest.TestCase   ):


					'''simple docstring'''





					_a   			=							DDIMPipeline
					_a   			=							UNCONDITIONAL_IMAGE_GENERATION_PARAMS
					_a   			=							PipelineTesterMixin.required_optional_params - {
					    'num_images_per_prompt',
					    'latents',
					    'callback',
					    'callback_steps',
					}
					_a   			=							UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
					_a   			=							False



					def 		snake_case       (       self							:  str     )->					Optional[Any]:
								torch.manual_seed(0     )
								lowerCamelCase__		:		Union[str, Any]			=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'''),  )
								lowerCamelCase__		:		Optional[Any]			=DDIMScheduler()
								lowerCamelCase__		:		List[Any]			={'''unet''': unet, '''scheduler''': scheduler}
								return components



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Optional[Any]=0     )->					Optional[int]:
								if str(lowerCamelCase     ).startswith('''mps'''     ):
											lowerCamelCase__		:		Dict			=torch.manual_seed(lowerCamelCase     )
								else:
											lowerCamelCase__		:		Optional[int]			=torch.Generator(device=lowerCamelCase     ).manual_seed(lowerCamelCase     )
								lowerCamelCase__		:		Tuple			={
								    '''batch_size''': 1,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''numpy''',
								}
								return inputs



					def 		snake_case       (       self							:  Dict     )->					str:
								lowerCamelCase__		:		Optional[Any]			='''cpu'''

								lowerCamelCase__		:		int			=self.get_dummy_components()
								lowerCamelCase__		:		Optional[int]			=self.pipeline_class(**lowerCamelCase     )
								pipe.to(lowerCamelCase     )
								pipe.set_progress_bar_config(disable=lowerCamelCase     )

								lowerCamelCase__		:		List[str]			=self.get_dummy_inputs(lowerCamelCase     )
								lowerCamelCase__		:		Any			=pipe(**lowerCamelCase     ).images
								lowerCamelCase__		:		Optional[Any]			=image[0, -3:, -3:, -1]

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



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[Any]:
								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					int:
								super().test_save_load_local(expected_max_difference=3E-3     )



					def 		snake_case       (       self							:  List[Any]     )->					List[Any]:
								super().test_save_load_optional_components(expected_max_difference=3E-3     )




					def 		snake_case       (       self							:  Optional[Any]     )->					Optional[int]:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3     )





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


					'''simple docstring'''





					def 		snake_case       (       self							:  Optional[Any]     )->					List[str]:
								lowerCamelCase__		:		Optional[Any]			='''google/ddpm-cifar10-32'''

								lowerCamelCase__		:		Union[str, Any]			=UNetaDModel.from_pretrained(lowerCamelCase     )
								lowerCamelCase__		:		Optional[int]			=DDIMScheduler()

								lowerCamelCase__		:		int			=DDIMPipeline(unet=lowerCamelCase,  scheduler=lowerCamelCase     )
								ddim.to(lowerCamelCase     )
								ddim.set_progress_bar_config(disable=lowerCamelCase     )

								lowerCamelCase__		:		Tuple			=torch.manual_seed(0     )
								lowerCamelCase__		:		int			=ddim(generator=lowerCamelCase,  eta=0.0,  output_type='''numpy'''     ).images

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

								assert image.shape == (1, 32, 32, 3)
								lowerCamelCase__		:		Any			=np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]     )

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




					def 		snake_case       (       self							:  Optional[int]     )->					Any:
								lowerCamelCase__		:		str			='''google/ddpm-ema-bedroom-256'''

								lowerCamelCase__		:		Optional[int]			=UNetaDModel.from_pretrained(lowerCamelCase     )
								lowerCamelCase__		:		Any			=DDIMScheduler.from_pretrained(lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=DDIMPipeline(unet=lowerCamelCase,  scheduler=lowerCamelCase     )
								ddpm.to(lowerCamelCase     )
								ddpm.set_progress_bar_config(disable=lowerCamelCase     )

								lowerCamelCase__		:		List[str]			=torch.manual_seed(0     )
								lowerCamelCase__		:		Optional[Any]			=ddpm(generator=lowerCamelCase,  output_type='''numpy'''     ).images

								lowerCamelCase__		:		Any			=image[0, -3:, -3:, -1]

								assert image.shape == (1, 256, 256, 3)
								lowerCamelCase__		:		Any			=np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]     )

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




