




'''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 						a				(   _lowerCamelCase     ):


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				tempfile.mkdtemp()
									snake_case_									=				8

									# DPR tok
									snake_case_									=				[
									    '''[UNK]''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''[PAD]''',
									    '''[MASK]''',
									    '''want''',
									    '''##want''',
									    '''##ed''',
									    '''wa''',
									    '''un''',
									    '''runn''',
									    '''##ing''',
									    ''',''',
									    '''low''',
									    '''lowest''',
									]
									snake_case_									=				os.path.join(self.tmpdirname       ,				'''dpr_tokenizer'''					)
									os.makedirs(lowercase_       ,				exist_ok=lowercase_					)
									snake_case_									=				os.path.join(lowercase_       ,				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
									snake_case_									=				[
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''<unk>''',
									]
									snake_case_									=				dict(zip(lowercase_       ,				range(len(lowercase_					)					)					)					)
									snake_case_									=				['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									snake_case_									=				{'''unk_token''': '''<unk>'''}

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


				def   A_ (		self    :					Any					):
									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,				'''dpr_tokenizer'''					)					)


				def   A_ (		self    :					Dict					):
									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,				'''bart_tokenizer'''					)					)


				def   A_ (		self    :					Optional[Any]					):
									shutil.rmtree(self.tmpdirname					)


				@require_tokenizers
				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				os.path.join(self.tmpdirname       ,				'''rag_tokenizer'''					)
									snake_case_									=				RagConfig(question_encoder=DPRConfig().to_dict()       ,				generator=BartConfig().to_dict()					)
									snake_case_									=				RagTokenizer(question_encoder=self.get_dpr_tokenizer()       ,				generator=self.get_bart_tokenizer()					)
									rag_config.save_pretrained(lowercase_					)
									rag_tokenizer.save_pretrained(lowercase_					)
									snake_case_									=				RagTokenizer.from_pretrained(lowercase_       ,				config=lowercase_					)
									self.assertIsInstance(new_rag_tokenizer.question_encoder       ,				lowercase_					)
									self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()       ,				rag_tokenizer.question_encoder.get_vocab()					)
									self.assertIsInstance(new_rag_tokenizer.generator       ,				lowercase_					)
									self.assertEqual(new_rag_tokenizer.generator.get_vocab()       ,				rag_tokenizer.generator.get_vocab()					)


				@slow
				def   A_ (		self    :					List[Any]					):
									snake_case_									=				RagTokenizer.from_pretrained('''facebook/rag-token-nq'''					)
									snake_case_									=				[
									    '''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''',
									]
									snake_case_									=				tokenizer(lowercase_					)
									self.assertIsNotNone(lowercase_					)


				@slow
				def   A_ (		self    :					Dict					):
									snake_case_									=				RagTokenizer.from_pretrained('''facebook/rag-sequence-nq'''					)
									snake_case_									=				[
									    '''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''',
									]
									snake_case_									=				tokenizer(lowercase_					)
									self.assertIsNotNone(lowercase_					)









import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  KandinskyVaaPriorPipeline
      __snake_case       =  ['''prompt''']
      __snake_case       =  ['''prompt''', '''negative_prompt''']
      __snake_case       =  [
          '''num_images_per_prompt''',
          '''generator''',
          '''num_inference_steps''',
          '''latents''',
          '''negative_prompt''',
          '''guidance_scale''',
          '''output_type''',
          '''return_dict''',
      ]
      __snake_case       =  False







      @property
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Union[str, Any]:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						Dict  )    ->Any:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						int  )    ->List[str]:




       """simple docstring"""


       return self.time_input_dim







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


       return self.time_input_dim * 4







      @property
      def       __lowerCAmelCase					(      self      :						Any  )    ->List[Any]:




       """simple docstring"""


       return 100







      @property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->str:




       """simple docstring"""


       a		  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )
       return tokenizer







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->str:




       """simple docstring"""


       torch.manual_seed(0  )
       a		  =      CLIPTextConfig(
           bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=self.text_embedder_hidden_size ,					projection_dim=self.text_embedder_hidden_size ,					intermediate_size=37 ,					layer_norm_eps=1e-0_5 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1_000 ,					)
       return CLIPTextModelWithProjection(__UpperCAmelCase  )







      @property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       torch.manual_seed(0  )

       a		  =      {
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 12,
           '''embedding_dim''': self.text_embedder_hidden_size,
           '''num_layers''': 1,
       }

       a		  =      PriorTransformer(**__UpperCAmelCase  )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       a		  =      nn.Parameter(torch.ones(model.clip_std.shape  )  )
       return model







      @property
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[Any]:




       """simple docstring"""


       torch.manual_seed(0  )
       a		  =      CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size ,					image_size=224 ,					projection_dim=self.text_embedder_hidden_size ,					intermediate_size=37 ,					num_attention_heads=4 ,					num_channels=3 ,					num_hidden_layers=5 ,					patch_size=14 ,					)

       a		  =      CLIPVisionModelWithProjection(__UpperCAmelCase  )
       return model







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->int:




       """simple docstring"""


       a		  =      CLIPImageProcessor(
           crop_size=224 ,					do_center_crop=__UpperCAmelCase ,					do_normalize=__UpperCAmelCase ,					do_resize=__UpperCAmelCase ,					image_mean=[0.48145466, 0.4578275, 0.40821073] ,					image_std=[0.26862954, 0.26130258, 0.27577711] ,					resample=3 ,					size=224 ,					)

       return image_processor







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      self.dummy_prior
       a		  =      self.dummy_image_encoder
       a		  =      self.dummy_text_encoder
       a		  =      self.dummy_tokenizer
       a		  =      self.dummy_image_processor

       a		  =      UnCLIPScheduler(
           variance_type='''fixed_small_log''' ,					prediction_type='''sample''' ,					num_train_timesteps=1_000 ,					clip_sample=__UpperCAmelCase ,					clip_sample_range=10.0 ,					)

       a		  =      {
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''scheduler''': scheduler,
           '''image_processor''': image_processor,
       }

       return components







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						str=0  )    ->int:




       """simple docstring"""


       if str(__UpperCAmelCase  ).startswith('''mps'''  ):
        a		  =      torch.manual_seed(__UpperCAmelCase  )
       else:
        a		  =      torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
       a		  =      {
           '''prompt''': '''horse''',
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs







      def       __lowerCAmelCase					(      self      :						str  )    ->Tuple:




       """simple docstring"""


       a		  =      '''cpu'''

       a		  =      self.get_dummy_components()

       a		  =      self.pipeline_class(**__UpperCAmelCase  )
       a		  =      pipe.to(__UpperCAmelCase  )

       pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

       a		  =      pipe(**self.get_dummy_inputs(__UpperCAmelCase  )  )
       a		  =      output.image_embeds

       a		  =      pipe(
           **self.get_dummy_inputs(__UpperCAmelCase  ) ,					return_dict=__UpperCAmelCase ,					)[0]

       a		  =      image[0, -10:]
       a		  =      image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       a		  =      np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]  )

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







      @skip_mps
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      torch_device == '''cpu'''
       a		  =      True
       a		  =      False

       self._test_inference_batch_single_identical(
           test_max_difference=__UpperCAmelCase ,					relax_max_difference=__UpperCAmelCase ,					test_mean_pixel_difference=__UpperCAmelCase ,					)







      @skip_mps
      def       __lowerCAmelCase					(      self      :						List[str]  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      torch_device == '''cpu'''
       a		  =      False

       self._test_attention_slicing_forward_pass(
           test_max_difference=__UpperCAmelCase ,					test_mean_pixel_difference=__UpperCAmelCase ,					)


