"""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  UpperCamelCase					(				lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      MvpTokenizer
       SCREAMING_SNAKE_CASE_			=      MvpTokenizerFast
       SCREAMING_SNAKE_CASE_			=      True
       SCREAMING_SNAKE_CASE_			=      filter_roberta_detectors







       def 		a_						( self)      ->      List[str]:
            super().setUp()
            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(lowerCAmelCase__,							range(len(lowerCAmelCase__))))
            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,							VOCAB_FILES_NAMES['vocab_file'])
            snake_case_					       =					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(lowerCAmelCase__) + '\n')
            with open(self.merges_file,							'w',							encoding='utf-8') as fp:
                 fp.write('\n'.join(lowerCAmelCase__))







       def 		a_						( self,							**lowerCAmelCase__)      ->      List[Any]:
            kwargs.update(self.special_tokens_map)
            return self.tokenizer_class.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self,							**lowerCAmelCase__)      ->      str:
            kwargs.update(self.special_tokens_map)
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__)      ->      List[str]:
            return "lower newer", "lower newer"







       @cached_property
       def 		a_						( self)      ->      Any:
            return MvpTokenizer.from_pretrained('RUCAIBox/mvp')







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







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

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

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







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







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







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







       @require_torch
       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					['A long paragraph for summarization.']
            snake_case_					       =					[
                'Summary of the text.',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 snake_case_					       =					tokenizer(lowerCAmelCase__,							text_target=lowerCAmelCase__,							return_tensors='pt')
                 snake_case_					       =					inputs['input_ids']
                 snake_case_					       =					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 		a_						( self)      ->      Dict:
            pass







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

                      # 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']),							)

                      snake_case_					       =					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
                      snake_case_					       =					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(
                          lowerCAmelCase__,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
                      self.assertSequenceEqual(
                          lowerCAmelCase__,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])






"""simple docstring"""
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase    :Optional[Any]     =					False
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     pass



@nightly
@require_torch_gpu
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



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





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  Tuple											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  List[str]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

         _UpperCAmelCase							:  Optional[Any]											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  List[Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(A		)
             _UpperCAmelCase							:  int											=    VersatileDiffusionPipeline.from_pretrained(A       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    generator.manual_seed(0		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  List[Any]											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    '''cyberpunk 2077'''
         _UpperCAmelCase							:  Optional[int]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
         _UpperCAmelCase							:  str											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.dual_guided(
             prompt=A       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''       ,	).images

         _UpperCAmelCase							:  Union[str, Any]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[Any]											=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]		)

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

         _UpperCAmelCase							:  Dict											=    '''A painting of a squirrel eating a burger '''
         _UpperCAmelCase							:  Tuple											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.text_to_image(
             prompt=A       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Tuple											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  int											=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]		)

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

         _UpperCAmelCase							:  int											=    pipe.image_variation(A       ,	generator=A       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Optional[int]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[str]											=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]		)

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


