'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class lowerCamelCase							(	lowerCamelCase     ,		lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	StableDiffusionSAGPipeline
  lowerCAmelCase__										=	TEXT_TO_IMAGE_PARAMS
  lowerCAmelCase__										=	TEXT_TO_IMAGE_BATCH_PARAMS
  lowerCAmelCase__										=	TEXT_TO_IMAGE_IMAGE_PARAMS
  lowerCAmelCase__										=	TEXT_TO_IMAGE_IMAGE_PARAMS
  lowerCAmelCase__										=	False






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->List[Any]:
        torch.manual_seed(0  )
        UpperCAmelCase_            =  UNetaDConditionModel(
            block_out_channels=(32, 64)  ,							layers_per_block=2  ,							sample_size=32  ,							in_channels=4  ,							out_channels=4  ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,							cross_attention_dim=32  ,							)
        UpperCAmelCase_            =  DDIMScheduler(
            beta_start=0.0_0085  ,							beta_end=0.012  ,							beta_schedule='''scaled_linear'''  ,							clip_sample=UpperCAmelCase__  ,							set_alpha_to_one=UpperCAmelCase__  ,							)
        torch.manual_seed(0  )
        UpperCAmelCase_            =  AutoencoderKL(
            block_out_channels=[32, 64]  ,							in_channels=3  ,							out_channels=3  ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,							latent_channels=4  ,							)
        torch.manual_seed(0  )
        UpperCAmelCase_            =  CLIPTextConfig(
            bos_token_id=0  ,							eos_token_id=2  ,							hidden_size=32  ,							intermediate_size=37  ,							layer_norm_eps=1e-05  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							pad_token_id=1  ,							vocab_size=1000  ,							)
        UpperCAmelCase_            =  CLIPTextModel(UpperCAmelCase__  )
        UpperCAmelCase_            =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

        UpperCAmelCase_            =  {
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Tuple=0  )				->int:
        if str(UpperCAmelCase__  ).startswith('''mps'''  ):
              UpperCAmelCase_            =  torch.manual_seed(UpperCAmelCase__  )
        else:
              UpperCAmelCase_            =  torch.Generator(device=UpperCAmelCase__  ).manual_seed(UpperCAmelCase__  )
        UpperCAmelCase_            =  {
            '''prompt''': '''.''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 1.0,
            '''sag_scale''': 1.0,
            '''output_type''': '''numpy''',
        }
        return inputs






  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[Any]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







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




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Tuple  )				->List[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






  def  lowerCAmelCase__ (      self			:       int  )				->Optional[int]:
        UpperCAmelCase_            =  StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''  )
        UpperCAmelCase_            =  sag_pipe.to(UpperCAmelCase__  )
        sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        UpperCAmelCase_            =  '''.'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sag_pipe(
            [prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=7.5  ,							sag_scale=1.0  ,							num_inference_steps=20  ,							output_type='''np'''  )

        UpperCAmelCase_            =  output.images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Any:
        UpperCAmelCase_            =  StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
        UpperCAmelCase_            =  sag_pipe.to(UpperCAmelCase__  )
        sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        UpperCAmelCase_            =  '''.'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sag_pipe(
            [prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=7.5  ,							sag_scale=1.0  ,							num_inference_steps=20  ,							output_type='''np'''  )

        UpperCAmelCase_            =  output.images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2






  def  lowerCAmelCase__ (      self			:       int  )				->List[str]:
        UpperCAmelCase_            =  StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
        UpperCAmelCase_            =  sag_pipe.to(UpperCAmelCase__  )
        sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        UpperCAmelCase_            =  '''.'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sag_pipe(
            [prompt]  ,							width=768  ,							height=512  ,							generator=UpperCAmelCase__  ,							guidance_scale=7.5  ,							sag_scale=1.0  ,							num_inference_steps=20  ,							output_type='''np'''  ,							)

        UpperCAmelCase_            =  output.images

        assert image.shape == (1, 512, 768, 3)






'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	MvpTokenizer
  lowerCAmelCase__										=	MvpTokenizerFast
  lowerCAmelCase__										=	True
  lowerCAmelCase__										=	filter_roberta_detectors






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        super().setUp()
        UpperCAmelCase_            =  [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        UpperCAmelCase_            =  dict(zip(UpperCAmelCase__  ,							range(len(UpperCAmelCase__  )  )  )  )
        UpperCAmelCase_            =  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        UpperCAmelCase_            =  {'''unk_token''': '''<unk>'''}

        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''vocab_file''']  )
        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''merges_file''']  )
        with open(self.vocab_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write(json.dumps(UpperCAmelCase__  ) + '''\n'''  )
        with open(self.merges_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write('''\n'''.join(UpperCAmelCase__  )  )






  def  lowerCAmelCase__ (      self			:       Tuple  ,							**UpperCAmelCase__			:       List[str]  )				->Dict:
        kwargs.update(self.special_tokens_map  )
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							**UpperCAmelCase__			:       int  )				->Tuple:
        kwargs.update(self.special_tokens_map  )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Optional[Any]  )				->Union[str, Any]:
        return "lower newer", "lower newer"






  @cached_property
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Optional[int]:
        return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''  )






  @cached_property
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Tuple:
        return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Any  )				->Dict:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_            =  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							max_length=len(UpperCAmelCase__  )  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )

              self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
              self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
              UpperCAmelCase_            =  batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase__  ,							UpperCAmelCase__  )
              # Test that special tokens are reset






  @require_torch
  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''  ,							UpperCAmelCase__  )
              self.assertIn('''attention_mask'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''labels'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''decoder_attention_mask'''  ,							UpperCAmelCase__  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[Any]:
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(text_target=UpperCAmelCase__  ,							max_length=32  ,							padding='''max_length'''  ,							return_tensors='''pt'''  )
              self.assertEqual(32  ,							targets['''input_ids'''].shape[1]  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog''']  ,							padding=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )
              self.assertEqual(batch.input_ids.shape  ,							(2, 1024)  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[int]:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''']
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							text_target=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              UpperCAmelCase_            =  inputs['''input_ids''']
              UpperCAmelCase_            =  inputs['''labels''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )






  def  lowerCAmelCase__ (      self			:       str  )				->Optional[Any]:
        pass






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                    UpperCAmelCase_            =  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  self.tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  '''A, <mask> AllenNLP sentence.'''
                    UpperCAmelCase_            =  tokenizer_r.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )
                    UpperCAmelCase_            =  tokenizer_p.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r['''token_type_ids''']  )  ,							sum(tokens_p['''token_type_ids''']  )  )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  )  ,							sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  )  ,							)

                    UpperCAmelCase_            =  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
                    UpperCAmelCase_            =  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
                    self.assertSequenceEqual(tokens_r['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )





