


'''simple docstring'''






import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 							a__(      lowerCamelCase__	,				lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					StableDiffusionDiffEditPipeline
    lowercase__				=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
    lowercase__				=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
    lowercase__				=					frozenset(
        []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowercase__				=					frozenset([]			)







    def     lowercase_ (   self				:						Any			):
      torch.manual_seed(0			)
      a		:      Dict       =  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		,    attention_head_dim=(2, 4)		,    use_linear_projection=__snake_case		,    )
      a		:      Optional[Any]       =  DDIMScheduler(
          beta_start=0.00085		,    beta_end=0.012		,    beta_schedule='scaled_linear'		,    clip_sample=__snake_case		,    set_alpha_to_one=__snake_case		,    )
      a		:      Any       =  DDIMInverseScheduler(
          beta_start=0.00085		,    beta_end=0.012		,    beta_schedule='scaled_linear'		,    clip_sample=__snake_case		,    set_alpha_to_zero=__snake_case		,    )
      torch.manual_seed(0			)
      a		:      Any       =  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		,    sample_size=1_28		,    )
      torch.manual_seed(0			)
      a		:      Union[str, Any]       =  CLIPTextConfig(
          bos_token_id=0		,    eos_token_id=2		,    hidden_size=32		,    intermediate_size=37		,    layer_norm_eps=1e-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    pad_token_id=1		,    vocab_size=10_00		,    hidden_act='gelu'		,    projection_dim=5_12		,    )
      a		:      Optional[Any]       =  CLIPTextModel(__snake_case			)
      a		:      str       =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

      a		:      List[str]       =  {
          'unet': unet,
          'scheduler': scheduler,
          'inverse_scheduler': inverse_scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }

      return components







    def     lowercase_ (   self				:						Any		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Union[str, Any]=0			):
      a		:      List[Any]       =  floats_tensor((1, 16, 16)		,    rng=random.Random(__snake_case			)			).to(__snake_case			)
      a		:      Tuple       =  floats_tensor((1, 2, 4, 16, 16)		,    rng=random.Random(__snake_case			)			).to(__snake_case			)
      if str(__snake_case			).startswith('mps'			):
        a		:      Tuple       =  torch.manual_seed(__snake_case			)
      else:
        a		:      Dict       =  torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
      a		:      Optional[Any]       =  {
          'prompt': 'a dog and a newt',
          'mask_image': mask,
          'image_latents': latents,
          'generator': generator,
          'num_inference_steps': 2,
          'inpaint_strength': 1.0,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }

      return inputs







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						List[Any]		,    __snake_case				:						List[str]=0			):
      a		:      Dict       =  floats_tensor((1, 3, 32, 32)		,    rng=random.Random(__snake_case			)			).to(__snake_case			)
      a		:      int       =  image.cpu().permute(0		,    2		,    3		,    1			)[0]
      a		:      int       =  Image.fromarray(np.uinta(__snake_case			)			).convert('RGB'			)
      if str(__snake_case			).startswith('mps'			):
        a		:      Dict       =  torch.manual_seed(__snake_case			)
      else:
        a		:      Dict       =  torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
      a		:      List[str]       =  {
          'image': image,
          'source_prompt': 'a cat and a frog',
          'target_prompt': 'a dog and a newt',
          'generator': generator,
          'num_inference_steps': 2,
          'num_maps_per_mask': 2,
          'mask_encode_strength': 1.0,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }

      return inputs







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						List[Any]		,    __snake_case				:						Tuple=0			):
      a		:      List[Any]       =  floats_tensor((1, 3, 32, 32)		,    rng=random.Random(__snake_case			)			).to(__snake_case			)
      a		:      Any       =  image.cpu().permute(0		,    2		,    3		,    1			)[0]
      a		:      Tuple       =  Image.fromarray(np.uinta(__snake_case			)			).convert('RGB'			)
      if str(__snake_case			).startswith('mps'			):
        a		:      Union[str, Any]       =  torch.manual_seed(__snake_case			)
      else:
        a		:      Optional[Any]       =  torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
      a		:      Union[str, Any]       =  {
          'image': image,
          'prompt': 'a cat and a frog',
          'generator': generator,
          'num_inference_steps': 2,
          'inpaint_strength': 1.0,
          'guidance_scale': 6.0,
          'decode_latents': True,
          'output_type': 'numpy',
      }
      return inputs







    def     lowercase_ (   self				:						Union[str, Any]			):
      if not hasattr(self.pipeline_class		,    '_optional_components'			):
        return

      a		:      Dict       =  self.get_dummy_components()
      a		:      Dict       =  self.pipeline_class(**__snake_case			)
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      # set all optional components to None and update pipeline config accordingly
      for optional_component in pipe._optional_components:
        setattr(__snake_case		,    __snake_case		,    __snake_case			)
      pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}			)

      a		:      Any       =  self.get_dummy_inputs(__snake_case			)
      a		:      Any       =  pipe(**__snake_case			)[0]

      with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(__snake_case			)
        a		:      Dict       =  self.pipeline_class.from_pretrained(__snake_case			)
        pipe_loaded.to(__snake_case			)
        pipe_loaded.set_progress_bar_config(disable=__snake_case			)

      for optional_component in pipe._optional_components:
        self.assertTrue(
            getattr(__snake_case		,    __snake_case			) is None		,    F"""`{optional_component}` did not stay set to None after loading."""		,    )

      a		:      int       =  self.get_dummy_inputs(__snake_case			)
      a		:      List[str]       =  pipe_loaded(**__snake_case			)[0]

      a		:      List[str]       =  np.abs(output - output_loaded			).max()
      self.assertLess(__snake_case		,    1e-4			)







    def     lowercase_ (   self				:						List[str]			):
      a		:      List[Any]       =  'cpu'

      a		:      Union[str, Any]       =  self.get_dummy_components()
      a		:      List[str]       =  self.pipeline_class(**__snake_case			)
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Optional[Any]       =  self.get_dummy_mask_inputs(__snake_case			)
      a		:      Dict       =  pipe.generate_mask(**__snake_case			)
      a		:      Union[str, Any]       =  mask[0, -3:, -3:]

      self.assertEqual(mask.shape		,    (1, 16, 16)			)
      a		:      List[str]       =  np.array([0] * 9			)
      a		:      List[Any]       =  np.abs(mask_slice.flatten() - expected_slice			).max()
      self.assertLessEqual(__snake_case		,    1e-3			)
      self.assertEqual(mask[0, -3, -4]		,    0			)







    def     lowercase_ (   self				:						Tuple			):
      a		:      Any       =  'cpu'

      a		:      Any       =  self.get_dummy_components()
      a		:      Optional[int]       =  self.pipeline_class(**__snake_case			)
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      List[Any]       =  self.get_dummy_inversion_inputs(__snake_case			)
      a		:      str       =  pipe.invert(**__snake_case			).images
      a		:      Tuple       =  image[0, -1, -3:, -3:]

      self.assertEqual(image.shape		,    (2, 32, 32, 3)			)
      a		:      Optional[int]       =  np.array(
          [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]		,    )
      a		:      str       =  np.abs(image_slice.flatten() - expected_slice			).max()
      self.assertLessEqual(__snake_case		,    1e-3			)







    def     lowercase_ (   self				:						Tuple			):
      super().test_inference_batch_single_identical(expected_max_diff=5e-3			)







    def     lowercase_ (   self				:						List[str]			):
      a		:      Tuple       =  'cpu'

      a		:      Union[str, Any]       =  self.get_dummy_components()

      a		:      Tuple       =  {'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
      a		:      int       =  DPMSolverMultistepScheduler(**__snake_case			)
      a		:      int       =  DPMSolverMultistepInverseScheduler(**__snake_case			)

      a		:      Optional[Any]       =  self.pipeline_class(**__snake_case			)
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Any       =  self.get_dummy_inversion_inputs(__snake_case			)
      a		:      Union[str, Any]       =  pipe.invert(**__snake_case			).images
      a		:      List[Any]       =  image[0, -1, -3:, -3:]

      self.assertEqual(image.shape		,    (2, 32, 32, 3)			)
      a		:      List[Any]       =  np.array(
          [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]		,    )
      a		:      Tuple       =  np.abs(image_slice.flatten() - expected_slice			).max()
      self.assertLessEqual(__snake_case		,    1e-3			)

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







    def     lowercase_ (   self				:						Union[str, Any]			):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    @classmethod
    def     lowercase_ (   cls				:						str			):
      a		:      Optional[int]       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'			)

      a		:      int       =  raw_image.convert('RGB'			).resize((7_68, 7_68)			)

      a		:      Tuple       =  raw_image







    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Dict       =  torch.manual_seed(0			)

      a		:      int       =  StableDiffusionDiffEditPipeline.from_pretrained(
          'stabilityai/stable-diffusion-2-1'		,    safety_checker=__snake_case		,    torch_dtype=torch.floataa			)
      a		:      Optional[Any]       =  DDIMScheduler.from_config(pipe.scheduler.config			)
      a		:      Tuple       =  DDIMInverseScheduler.from_config(pipe.scheduler.config			)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      List[str]       =  'a bowl of fruit'
      a		:      Any       =  'a bowl of pears'

      a		:      int       =  pipe.generate_mask(
          image=self.raw_image		,    source_prompt=__snake_case		,    target_prompt=__snake_case		,    generator=__snake_case		,    )

      a		:      List[str]       =  pipe.invert(
          prompt=__snake_case		,    image=self.raw_image		,    inpaint_strength=0.7		,    generator=__snake_case			).latents

      a		:      Optional[Any]       =  pipe(
          prompt=__snake_case		,    mask_image=__snake_case		,    image_latents=__snake_case		,    generator=__snake_case		,    negative_prompt=__snake_case		,    inpaint_strength=0.7		,    output_type='numpy'		,    ).images[0]

      a		:      Optional[Any]       =  (
          np.array(
              load_image(
                  'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                  '/diffedit/pears.png'			).resize((7_68, 7_68)			)			)
          / 2_55
      )
      assert np.abs((expected_image - image).max()			) < 5e-1







    def     lowercase_ (   self				:						str			):
      a		:      Dict       =  torch.manual_seed(0			)

      a		:      Tuple       =  StableDiffusionDiffEditPipeline.from_pretrained(
          'stabilityai/stable-diffusion-2-1'		,    safety_checker=__snake_case		,    torch_dtype=torch.floataa			)
      a		:      List[Any]       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
      a		:      Optional[int]       =  DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config			)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      str       =  'a bowl of fruit'
      a		:      Union[str, Any]       =  'a bowl of pears'

      a		:      List[Any]       =  pipe.generate_mask(
          image=self.raw_image		,    source_prompt=__snake_case		,    target_prompt=__snake_case		,    generator=__snake_case		,    )

      a		:      List[str]       =  pipe.invert(
          prompt=__snake_case		,    image=self.raw_image		,    inpaint_strength=0.7		,    generator=__snake_case		,    num_inference_steps=25		,    ).latents

      a		:      Union[str, Any]       =  pipe(
          prompt=__snake_case		,    mask_image=__snake_case		,    image_latents=__snake_case		,    generator=__snake_case		,    negative_prompt=__snake_case		,    inpaint_strength=0.7		,    num_inference_steps=25		,    output_type='numpy'		,    ).images[0]

      a		:      Tuple       =  (
          np.array(
              load_image(
                  'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                  '/diffedit/pears.png'			).resize((7_68, 7_68)			)			)
          / 2_55
      )
      assert np.abs((expected_image - image).max()			) < 5e-1




'''simple docstring'''






import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCAmelCase:      Tuple		 =		'src/transformers'
lowerCAmelCase:      Union[str, Any]		 =		'docs/source/en'
lowerCAmelCase:      Dict		 =		'.'
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  with open(_A				,    'r'				,    encoding='utf-8'				,    newline='\n'       ) as f:
    a		:      Optional[Any]       =  f.readlines()
  # Find the start prompt.
  a		:      Dict       =  0
  while not lines[start_index].startswith(_A       ):
    start_index += 1
  start_index += 1

  a		:      Optional[Any]       =  start_index
  while not lines[end_index].startswith(_A       ):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]       ) <= 1:
    start_index += 1
  while len(lines[end_index]       ) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]       ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
lowerCAmelCase:      List[str]		 =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase:      Dict		 =		re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase:      Any		 =		re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase:      Dict		 =		re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase:      List[Any]		 =		direct_transformers_import(TRANSFORMERS_PATH)
def    lowerCamelCase__ (		_A       ):
  a		:      Tuple       =  re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'				,    _A       )
  return [m.group(0       ) for m in matches]
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      List[Any]       =  2 if text == '✅' or text == '❌' else len(_A       )
  a		:      Optional[int]       =  (width - text_length) // 2
  a		:      List[Any]       =  width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent
def    lowerCamelCase__ (		):
  a		:      Union[str, Any]       =  transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  a		:      Union[str, Any]       =  {
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  a		:      Tuple       =  {name: config.replace('Config'				,    ''       ) for name, config in model_name_to_config.items()}

  # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  a		:      int       =  collections.defaultdict(_A       )
  a		:      List[Any]       =  collections.defaultdict(_A       )
  a		:      List[Any]       =  collections.defaultdict(_A       )
  a		:      Union[str, Any]       =  collections.defaultdict(_A       )
  a		:      int       =  collections.defaultdict(_A       )

  # Let's lookup through all transformers object (once).
  for attr_name in dir(_A       ):
    a		:      Optional[Any]       =  None
    if attr_name.endswith('Tokenizer'       ):
      a		:      int       =  slow_tokenizers
      a		:      Any       =  attr_name[:-9]
    elif attr_name.endswith('TokenizerFast'       ):
      a		:      str       =  fast_tokenizers
      a		:      List[Any]       =  attr_name[:-13]
    elif _re_tf_models.match(_A       ) is not None:
      a		:      Optional[Any]       =  tf_models
      a		:      Dict       =  _re_tf_models.match(_A       ).groups()[0]
    elif _re_flax_models.match(_A       ) is not None:
      a		:      int       =  flax_models
      a		:      Optional[Any]       =  _re_flax_models.match(_A       ).groups()[0]
    elif _re_pt_models.match(_A       ) is not None:
      a		:      Tuple       =  pt_models
      a		:      Optional[int]       =  _re_pt_models.match(_A       ).groups()[0]

    if lookup_dict is not None:
      while len(_A       ) > 0:
        if attr_name in model_name_to_prefix.values():
          a		:      Tuple       =  True
          break
        # Try again after removing the last word in the name
        a		:      Tuple       =  ''.join(camel_case_split(_A       )[:-1]       )

    # Let's build that table!
  a		:      List[Any]       =  list(model_name_to_config.keys()       )
  model_names.sort(key=str.lower       )
  a		:      Tuple       =  ['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
  # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  a		:      Tuple       =  [len(_A       ) + 2 for c in columns]
  a		:      str       =  max([len(_A       ) for name in model_names]       ) + 2

  # Build the table per se
  a		:      List[Any]       =  '|' + '|'.join([_center_text(_A				,    _A       ) for c, w in zip(_A				,    _A       )]       ) + '|\n'
  # Use ":-----:" format to center-aligned table cell texts
  table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]       ) + "|\n"

  a		:      str       =  {True: '✅', False: '❌'}
  for name in model_names:
    a		:      Any       =  model_name_to_prefix[name]
    a		:      Optional[Any]       =  [
        name,
        check[slow_tokenizers[prefix]],
        check[fast_tokenizers[prefix]],
        check[pt_models[prefix]],
        check[tf_models[prefix]],
        check[flax_models[prefix]],
    ]
    table += "|" + "|".join([_center_text(_A				,    _A       ) for l, w in zip(_A				,    _A       )]       ) + "|\n"
  return table
def    lowerCamelCase__ (		_A=False       ):
  a     ,			a     ,			a     ,			a		:      Tuple       =  _find_text_in_file(
      filename=os.path.join(_A				,    'index.md'       )				,    start_prompt='<!--This table is updated automatically from the auto modules'				,    end_prompt='<!-- End table-->'				,    )
  a		:      Optional[int]       =  get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(_A				,    'index.md'       )				,    'w'				,    encoding='utf-8'				,    newline='\n'       ) as f:
        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]       )
    else:
      raise ValueError(
          'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'       )


if __name__ == "__main__":
     lowerCAmelCase:      Dict		 =		argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     lowerCAmelCase:      Any		 =		parser.parse_args()

     check_model_table(args.fix_and_overwrite)