"""simple docstring"""
import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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   __snake_case			(	__lowerCAmelCase			, unittest.TestCase							):
      a__								=						BartTokenizer
      a__								=						BartTokenizerFast
      a__								=						True
      a__								=						filter_roberta_detectors



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        super().setUp()
        a__:	Optional[Any]     				=      [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        a__:	Dict     				=      dict(zip(lowercase       ,     range(len(lowercase))))
        a__:	Optional[Any]     				=      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        a__:	int     				=      {'unk_token': '<unk>'}

        a__:	str     				=      os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['vocab_file'])
        a__:	Dict     				=      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(lowercase) + '\n')
        with open(self.merges_file       ,     'w'       ,     encoding='utf-8') as fp:
          fp.write('\n'.join(lowercase))



      def     lowerCamelCase_  (		self       ,     **lowercase)      ->     Union[str, Any]:





        '''simple docstring'''





        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname       ,     **lowercase)



      def     lowerCamelCase_  (		self       ,     **lowercase)      ->     int:





        '''simple docstring'''





        kwargs.update(self.special_tokens_map)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,     **lowercase)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        return "lower newer", "lower newer"



      @cached_property
      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        return BartTokenizer.from_pretrained('facebook/bart-large')



      @cached_property
      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        return BartTokenizerFast.from_pretrained('facebook/bart-large')



      @require_torch
      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	Any     				=      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        a__:	Union[str, Any]     				=      [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          a__:	Optional[int]     				=      tokenizer(lowercase       ,     max_length=len(lowercase)       ,     padding=lowercase       ,     return_tensors='pt')
          self.assertIsInstance(lowercase       ,     lowercase)

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



      @require_torch
      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Dict     				=      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          a__:	str     				=      tokenizer(lowercase       ,     padding=lowercase       ,     return_tensors='pt')
          # check if input_ids are returned and no labels
          self.assertIn('input_ids'       ,     lowercase)
          self.assertIn('attention_mask'       ,     lowercase)
          self.assertNotIn('labels'       ,     lowercase)
          self.assertNotIn('decoder_attention_mask'       ,     lowercase)



      @require_torch
      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	int     				=      [
            'Summary of the text.',
            'Another summary.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          a__:	int     				=      tokenizer(text_target=lowercase       ,     max_length=32       ,     padding='max_length'       ,     return_tensors='pt')
          self.assertEqual(32       ,     targets['input_ids'].shape[1])



      @require_torch
      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          a__:	Tuple     				=      tokenizer(
              ['I am a small frog' * 10_24, 'I am a small frog']       ,     padding=lowercase       ,     truncation=lowercase       ,     return_tensors='pt')
          self.assertIsInstance(lowercase       ,     lowercase)
          self.assertEqual(batch.input_ids.shape       ,     (2, 10_24))



      @require_torch
      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        a__:	str     				=      ['A long paragraph for summarization.']
        a__:	Union[str, Any]     				=      [
            'Summary of the text.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          a__:	List[str]     				=      tokenizer(lowercase       ,     return_tensors='pt')
          a__:	Optional[int]     				=      tokenizer(text_target=lowercase       ,     return_tensors='pt')
          a__:	List[str]     				=      inputs['input_ids']
          a__:	Optional[int]     				=      targets['input_ids']
          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)      ->     Any:





        '''simple docstring'''





        pass



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





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

            # 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'])       ,     )

            a__:	Any     				=      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
            a__:	int     				=      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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
            self.assertSequenceEqual(tokens_r['input_ids']       ,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

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



'''simple docstring'''
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()






class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     @property
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	1
         __UpperCamelCase						:						Union[str, Any]		     =	3
         __UpperCamelCase						:						Tuple		     =	(3_2, 3_2)

         __UpperCamelCase						:						List[Any]		     =	floats_tensor((batch_size, num_channels) + sizes  ,      rng=random.Random(0     )     ).to(_lowerCamelCase     )
         return image







     @property
     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						Dict		     =	UNetaDModel(
             block_out_channels=(3_2, 6_4)  ,      layers_per_block=2  ,      sample_size=3_2  ,      in_channels=6  ,      out_channels=3  ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,      )
         return model







     @property
     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						List[str]		     =	VQModel(
             block_out_channels=[3_2, 6_4]  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,      latent_channels=3  ,      )
         return model







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Tuple		     =	'cpu'
         __UpperCamelCase						:						Union[str, Any]		     =	self.dummy_uncond_unet
         __UpperCamelCase						:						List[str]		     =	DDIMScheduler()
         __UpperCamelCase						:						Any		     =	self.dummy_vq_model

         __UpperCamelCase						:						Tuple		     =	LDMSuperResolutionPipeline(unet=_lowerCamelCase  ,      vqvae=_lowerCamelCase  ,      scheduler=_lowerCamelCase     )
         ldm.to(_lowerCamelCase     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						int		     =	self.dummy_image.to(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	torch.Generator(device=_lowerCamelCase     ).manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	ldm(image=_lowerCamelCase  ,      generator=_lowerCamelCase  ,      num_inference_steps=2  ,      output_type='numpy'     ).images

         __UpperCamelCase						:						str		     =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         __UpperCamelCase						:						str		     =	np.array([0.8_6_7_8, 0.8_2_4_5, 0.6_3_8_1, 0.6_8_3_0, 0.4_3_8_5, 0.5_5_9_9, 0.4_6_4_1, 0.6_2_0_1, 0.5_1_5_0]     )

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







     @unittest.skipIf(torch_device != 'cuda'  ,      'This test requires a GPU'     )
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	self.dummy_uncond_unet
         __UpperCamelCase						:						Optional[int]		     =	DDIMScheduler()
         __UpperCamelCase						:						Any		     =	self.dummy_vq_model

         # put models in fp16
         __UpperCamelCase						:						str		     =	unet.half()
         __UpperCamelCase						:						Optional[Any]		     =	vqvae.half()

         __UpperCamelCase						:						Optional[int]		     =	LDMSuperResolutionPipeline(unet=_lowerCamelCase  ,      vqvae=_lowerCamelCase  ,      scheduler=_lowerCamelCase     )
         ldm.to(_lowerCamelCase     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	self.dummy_image.to(_lowerCamelCase     )

         __UpperCamelCase						:						Optional[Any]		     =	ldm(_lowerCamelCase  ,      num_inference_steps=2  ,      output_type='numpy'     ).images

         assert image.shape == (1, 6_4, 6_4, 3)






@slow
@require_torch
class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/vq_diffusion/teddy_bear_pool.png'     )
         __UpperCamelCase						:						List[Any]		     =	init_image.resize((6_4, 6_4)  ,      resample=PIL_INTERPOLATION['lanczos']     )

         __UpperCamelCase						:						List[str]		     =	LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'  ,      device_map='auto'     )
         ldm.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	torch.manual_seed(0     )
         __UpperCamelCase						:						Dict		     =	ldm(image=_lowerCamelCase  ,      generator=_lowerCamelCase  ,      num_inference_steps=2_0  ,      output_type='numpy'     ).images

         __UpperCamelCase						:						List[Any]		     =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_5_6, 2_5_6, 3)
         __UpperCamelCase						:						str		     =	np.array([0.7_6_4_4, 0.7_6_7_9, 0.7_6_4_2, 0.7_6_3_3, 0.7_6_6_6, 0.7_5_6_0, 0.7_4_2_5, 0.7_2_5_7, 0.6_9_0_7]     )

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



