




'''simple docstring'''


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

UpperCamelCase			     =					{
    name: (getattr(transformers, name), getattr(transformers, name + '''Fast''')) for name in SLOW_TO_FAST_CONVERTERS
}

UpperCamelCase			     =					datasets.load_dataset('''xnli''', split='''test+validation''')

UpperCamelCase			     =					0
UpperCamelCase			     =					0
UpperCamelCase			     =					0
UpperCamelCase			     =					0
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Any:
     if spm_diff == list(reversed(__lowercase	)	):
          # AAA -> AA+A vs A+AA case.
          return True
     elif len(__lowercase	) == len(__lowercase	) and fast.decode(__lowercase	) == fast.decode(__lowercase	):
          # Second order OK
          # Barrich -> Barr + ich vs Bar + rich
          return True
     A:		Tuple          =		slow.encode(slow.decode(__lowercase	)	)
     A:		str          =		fast.encode(fast.decode(__lowercase	)	)
     if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
          # Type 3 error.
          # Snehagatha ->
          #       Sne, h, aga, th, a
          #       Sne, ha, gat, ha
          # Encoding the wrong with sp does not even recover what spm gave us
          # It fits tokenizer however...
          return True
     return False
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase	)			-> List[str]:
     A:		Optional[int]          =		fast.encode_plus(__lowercase	)[0]
     A:		int          =		enc.offsets
     A     ,    A:		str          =		offsets[idx], offsets[idx - 1]
     if curr is not None and line[curr[0] : curr[1]] == "\u200f":
          return True
     if prev is not None and line[prev[0] : prev[1]] == "\u200f":
          return True
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Dict:
     # Encoding can be the same with same result AAA -> A + AA vs AA + A
     # We can check that we use at least exactly the same number of tokens.
     for i, (spm_id, tok_id) in enumerate(zip(__lowercase		,				__lowercase	)	):
          if spm_id != tok_id:
               break
     A:		List[str]          =		i
     for i, (spm_id, tok_id) in enumerate(zip(reversed(__lowercase	)		,				reversed(__lowercase	)	)	):
          if spm_id != tok_id:
               break
     A:		List[Any]          =		len(__lowercase	) - i

     A:		Union[str, Any]          =		spm_ids[first:last]
     A:		int          =		tok_ids[first:last]

     if check_diff(__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	):
          return True

     if check_LTR_mark(__lowercase		,				__lowercase		,				__lowercase	):
          return True

     if last - first > 5:
          # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
          A:		Tuple          =		Counter(spm_ids[first:last]	)
          A:		Dict          =		Counter(tok_ids[first:last]	)

          A:		Optional[int]          =		{spm_ for (spm_, si) in spms.items() if toks.get(spm_		,				0	) == si}
          A:		List[str]          =		3
          for i in range(last - first - min_width	):
               if all(spm_ids[first + i + j] in removable_tokens for j in range(__lowercase	)	):
                    A:		Dict          =		[
                        k
                        for k in range(last - first - min_width	)
                        if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
                    ]
                    for j in possible_matches:
                         if check_diff(spm_ids[first : first + i]		,				tok_ids[first : first + j]		,				__lowercase		,				__lowercase	) and check_details(
                             __lowercase		,				spm_ids[first + i : last]		,				tok_ids[first + j : last]		,				__lowercase		,				__lowercase		,				):
                              return True

     print(F"""Spm: {[fast.decode([spm_ids[i]]	) for i in range(__lowercase		,				__lowercase	)]}"""	)
     try:
          print(F"""Tok: {[fast.decode([tok_ids[i]]	) for i in range(__lowercase		,				__lowercase	)]}"""	)
     except Exception:
          pass

     A:		Dict          =		fast.decode(spm_ids[:first]	)
     A:		Optional[Any]          =		fast.decode(spm_ids[last:]	)
     A:		Union[str, Any]          =		fast.decode(spm_ids[first:last]	)
     print()
     print(__lowercase	)
     return False
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase	)			-> List[Any]:
     global perfect
     global imperfect
     global wrong
     global total

     A:		int          =		slow.encode(__lowercase	)
     A:		Optional[int]          =		fast.encode(__lowercase	)

     A:		List[Any]          =		False
     total += 1

     if slow_ids != fast_ids:
          if check_details(__lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	):
               A:		Any          =		True
               imperfect += 1
          else:
               wrong += 1
     else:
          perfect += 1

     if total % 1_0_0_0_0 == 0:
          print(F"""({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})"""	)

     if skip_assert:
          return

     assert (
         slow_ids == fast_ids
     ), F"""line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__lowercase	)}\n{fast.tokenize(__lowercase	)}"""
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase	)			-> Optional[Any]:
     global batch_total
     for i in range(len(__lowercase	)	):
          # premise, all languages
          for text in dataset[i]["premise"].values():
               test_string(__lowercase		,				__lowercase		,				__lowercase	)

          # hypothesis, all languages
          for text in dataset[i]["hypothesis"]["translation"]:
               test_string(__lowercase		,				__lowercase		,				__lowercase	)


if __name__ == "__main__":
     for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
          UpperCamelCase			     =					list(slow_class.max_model_input_sizes.keys())
          for checkpoint in checkpoint_names:
               UpperCamelCase			     =					0
               UpperCamelCase			     =					0
               UpperCamelCase			     =					0
               UpperCamelCase			     =					0

               print(f'========================== Checking {name}: {checkpoint} ==========================')
               UpperCamelCase			     =					slow_class.from_pretrained(checkpoint, force_download=True)
               UpperCamelCase			     =					fast_class.from_pretrained(checkpoint, force_download=True)
               test_tokenizer(slow, fast)
               print(f'Accuracy {perfect * 100 / total:.2f}')












'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 						lowerCAmelCase_	(       UpperCAmelCase_       ,					unittest.TestCase							):







  '''simple docstring'''

  UpperCamelCase_			:      Any										=				RePaintPipeline
  UpperCamelCase_			:      int										=				IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
  UpperCamelCase_			:      List[str]										=				PipelineTesterMixin.required_optional_params - {
      """latents""",
      """num_images_per_prompt""",
      """callback""",
      """callback_steps""",
  }
  UpperCamelCase_			:      List[str]										=				IMAGE_INPAINTING_BATCH_PARAMS
  UpperCamelCase_			:      str										=				False



  def 					_snake_case     ( self				:							Dict	)			->		Optional[Any]:







       '''simple docstring'''



       torch.manual_seed(0	)
       torch.manual_seed(0	)
       A:		Optional[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''')					,  )
       A:		Optional[int]          =		RePaintScheduler()
       A:		Optional[int]          =		{'''unet''': unet, '''scheduler''': scheduler}
       return components



  def 					_snake_case     ( self				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							List[Any]					,  SCREAMING_SNAKE_CASE_				:							Tuple=0	)			->		Optional[int]:







       '''simple docstring'''



       if str(SCREAMING_SNAKE_CASE_	).startswith('''mps'''	):
            A:		str          =		torch.manual_seed(SCREAMING_SNAKE_CASE_	)
       else:
            A:		Any          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		str          =		np.random.RandomState(SCREAMING_SNAKE_CASE_	).standard_normal((1, 3, 32, 32)	)
       A:		Tuple          =		torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(device=SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       A:		List[Any]          =		(image > 0).to(device=SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       A:		Tuple          =		{
           '''image''': image,
           '''mask_image''': mask,
           '''generator''': generator,
           '''num_inference_steps''': 5,
           '''eta''': 0.0,
           '''jump_length''': 2,
           '''jump_n_sample''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs



  def 					_snake_case     ( self				:							int	)			->		Tuple:







       '''simple docstring'''



       A:		int          =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       A:		List[str]          =		self.get_dummy_components()
       A:		int          =		RePaintPipeline(**SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
       sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Dict          =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
       A:		List[str]          =		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
       A:		Optional[Any]          =		image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       A:		List[Any]          =		np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3



  @skip_mps
  def 					_snake_case     ( self				:							Union[str, Any]	)			->		Optional[Any]:







       '''simple docstring'''



       return super().test_save_load_local()



  @unittest.skip('''non-deterministic pipeline'''	)
  def 					_snake_case     ( self				:							Tuple	)			->		Optional[Any]:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()



  @skip_mps
  def 					_snake_case     ( self				:							List[Any]	)			->		str:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()



  @skip_mps
  def 					_snake_case     ( self				:							List[Any]	)			->		str:







       '''simple docstring'''



       return super().test_save_load_optional_components()




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







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()






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







  '''simple docstring'''

  def 					_snake_case     ( self				:							Optional[Any]	)			->		Optional[Any]:







       '''simple docstring'''



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




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







       '''simple docstring'''



       A:		str          =		load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256.png'''	)
       A:		Union[str, Any]          =		load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''	)
       A:		Optional[Any]          =		load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256_result.npy'''	)

       A:		Dict          =		'''google/ddpm-ema-celebahq-256'''
       A:		Tuple          =		UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE_	)

       A:		Optional[Any]          =		RePaintPipeline(unet=SCREAMING_SNAKE_CASE_					,  scheduler=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)
       repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
       repaint.enable_attention_slicing()

       A:		str          =		torch.manual_seed(0	)
       A:		str          =		repaint(
           SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  num_inference_steps=2_50					,  eta=0.0					,  jump_length=10					,  jump_n_sample=10					,  generator=SCREAMING_SNAKE_CASE_					,  output_type='''np'''					,  )
       A:		Optional[Any]          =		output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image	).mean() < 1E-2






