



'''simple docstring'''





import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


a__    =	False







class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Any)				->				Dict:

    """simple docstring"""







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

   @property
   def      UpperCamelCase_  (					self							:			Tuple)				->				Tuple:

    """simple docstring"""







    return 12

   @property
   def      UpperCamelCase_  (					self							:			str)				->				Any:

    """simple docstring"""







    return 12

   @property
   def      UpperCamelCase_  (					self							:			Dict)				->				int:

    """simple docstring"""







    return 32

   @property
   def      UpperCamelCase_  (					self							:			List[str])				->				Union[str, Any]:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  Any					=					VQModel(
        block_out_channels=[32, 64]				,		in_channels=3				,		out_channels=3				,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,		latent_channels=3				,		num_vq_embeddings=self.num_embed				,		vq_embed_dim=3				,		)
    return model

   @property
   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Tuple:

    """simple docstring"""







    _snake_case    :  int					=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
    return tokenizer

   @property
   def      UpperCamelCase_  (					self							:			Optional[int])				->				List[Any]:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  str					=					CLIPTextConfig(
        bos_token_id=0				,		eos_token_id=2				,		hidden_size=self.text_embedder_hidden_size				,		intermediate_size=37				,		layer_norm_eps=1E-05				,		num_attention_heads=4				,		num_hidden_layers=5				,		pad_token_id=1				,		vocab_size=1000				,		)
    return CLIPTextModel(lowerCAmelCase)

   @property
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				List[Any]:

    """simple docstring"""







    torch.manual_seed(0)

    _snake_case    :  Union[str, Any]					=					12
    _snake_case    :  Dict					=					12

    _snake_case    :  Dict					=					{
        """attention_bias""": True,
        """cross_attention_dim""": 32,
        """attention_head_dim""": height * width,
        """num_attention_heads""": 1,
        """num_vector_embeds""": self.num_embed,
        """num_embeds_ada_norm""": self.num_embeds_ada_norm,
        """norm_num_groups""": 32,
        """sample_size""": width,
        """activation_fn""": """geglu-approximate""",
    }

    _snake_case    :  List[Any]					=					TransformeraDModel(**lowerCAmelCase)
    return model

   def      UpperCamelCase_  (					self							:			List[str])				->				Tuple:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					"""cpu"""

    _snake_case    :  List[Any]					=					self.dummy_vqvae
    _snake_case    :  str					=					self.dummy_text_encoder
    _snake_case    :  Dict					=					self.dummy_tokenizer
    _snake_case    :  Optional[int]					=					self.dummy_transformer
    _snake_case    :  List[Any]					=					VQDiffusionScheduler(self.num_embed)
    _snake_case    :  List[str]					=					LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCAmelCase)

    _snake_case    :  Any					=					VQDiffusionPipeline(
        vqvae=lowerCAmelCase				,		text_encoder=lowerCAmelCase				,		tokenizer=lowerCAmelCase				,		transformer=lowerCAmelCase				,		scheduler=lowerCAmelCase				,		learned_classifier_free_sampling_embeddings=lowerCAmelCase				,		)
    _snake_case    :  Tuple					=					pipe.to(lowerCAmelCase)
    pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  Tuple					=					"""teddy bear playing in the pool"""

    _snake_case    :  Tuple					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  Dict					=					pipe([prompt]				,		generator=lowerCAmelCase				,		num_inference_steps=2				,		output_type="""np""")
    _snake_case    :  Any					=					output.images

    _snake_case    :  List[str]					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  str					=					pipe(
        [prompt]				,		generator=lowerCAmelCase				,		output_type="""np"""				,		return_dict=lowerCAmelCase				,		num_inference_steps=2)[0]

    _snake_case    :  Any					=					image[0, -3:, -3:, -1]
    _snake_case    :  Optional[Any]					=					image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 24, 24, 3)

    _snake_case    :  Tuple					=					np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992])

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


   def      UpperCamelCase_  (					self							:			Tuple)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					"""cpu"""

    _snake_case    :  Union[str, Any]					=					self.dummy_vqvae
    _snake_case    :  Tuple					=					self.dummy_text_encoder
    _snake_case    :  Dict					=					self.dummy_tokenizer
    _snake_case    :  Any					=					self.dummy_transformer
    _snake_case    :  int					=					VQDiffusionScheduler(self.num_embed)
    _snake_case    :  Optional[int]					=					LearnedClassifierFreeSamplingEmbeddings(
        learnable=lowerCAmelCase				,		hidden_size=self.text_embedder_hidden_size				,		length=tokenizer.model_max_length)

    _snake_case    :  List[Any]					=					VQDiffusionPipeline(
        vqvae=lowerCAmelCase				,		text_encoder=lowerCAmelCase				,		tokenizer=lowerCAmelCase				,		transformer=lowerCAmelCase				,		scheduler=lowerCAmelCase				,		learned_classifier_free_sampling_embeddings=lowerCAmelCase				,		)
    _snake_case    :  int					=					pipe.to(lowerCAmelCase)
    pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  int					=					"""teddy bear playing in the pool"""

    _snake_case    :  Tuple					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  Dict					=					pipe([prompt]				,		generator=lowerCAmelCase				,		num_inference_steps=2				,		output_type="""np""")
    _snake_case    :  List[str]					=					output.images

    _snake_case    :  Dict					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  str					=					pipe(
        [prompt]				,		generator=lowerCAmelCase				,		output_type="""np"""				,		return_dict=lowerCAmelCase				,		num_inference_steps=2)[0]

    _snake_case    :  int					=					image[0, -3:, -3:, -1]
    _snake_case    :  Dict					=					image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 24, 24, 3)

    _snake_case    :  Any					=					np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







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



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			List[str])				->				List[Any]:

    """simple docstring"""







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


   def      UpperCamelCase_  (					self							:			Dict)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy""")

    _snake_case    :  List[str]					=					VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq""")
    _snake_case    :  List[Any]					=					pipeline.to(lowerCAmelCase)
    pipeline.set_progress_bar_config(disable=lowerCAmelCase)

    # requires GPU generator for gumbel softmax
    # don't use GPU generator in tests though
    _snake_case    :  Union[str, Any]					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  Tuple					=					pipeline(
        """teddy bear playing in the pool"""				,		num_images_per_prompt=1				,		generator=lowerCAmelCase				,		output_type="""np"""				,		)

    _snake_case    :  List[str]					=					output.images[0]

    assert image.shape == (256, 256, 3)
    assert np.abs(expected_image - image).max() < 2.0





import argparse
import os
import re


a__    =	"""src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a__    =	re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
a__    =	re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	bool = False				)  ->     Optional[Any]:
 with open(SCREAMING_SNAKE_CASE__				,       """r"""				,       encoding="""utf-8"""				) as f:
  _snake_case    :  int					=					f.read()

 _snake_case    :  str					=					content.split("""\n"""				)
 _snake_case    :  List[str]					=					[]
 _snake_case    :  int					=					0
 while line_idx < len(SCREAMING_SNAKE_CASE__				):
  if _re_intro_mapping.search(lines[line_idx]				) is not None:
   _snake_case    :  Union[str, Any]					=					len(re.search(R"""^(\s*)\S"""				,       lines[line_idx]				).groups()[0]				) + 8
   # Start of a new mapping!
   while not lines[line_idx].startswith(""" """ * indent + """("""				):
    new_lines.append(lines[line_idx]				)
    line_idx += 1

   _snake_case    :  int					=					[]
   while lines[line_idx].strip() != "]":
    # Blocks either fit in one line or not
    if lines[line_idx].strip() == "(":
     _snake_case    :  Optional[Any]					=					line_idx
     while not lines[line_idx].startswith(""" """ * indent + """)"""				):
      line_idx += 1
     blocks.append("""\n""".join(lines[start_idx : line_idx + 1]				)				)
    else:
     blocks.append(lines[line_idx]				)
    line_idx += 1

   # Sort blocks by their identifiers
   _snake_case    :  int					=					sorted(SCREAMING_SNAKE_CASE__				,       key=lambda SCREAMING_SNAKE_CASE__				: _re_identifier.search(SCREAMING_SNAKE_CASE__				).groups()[0]				)
   new_lines += blocks
  else:
   new_lines.append(lines[line_idx]				)
   line_idx += 1

 if overwrite:
  with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as f:
   f.write("""\n""".join(SCREAMING_SNAKE_CASE__				)				)
 elif "\n".join(SCREAMING_SNAKE_CASE__				) != content:
  return True



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	bool = False				)  ->     int:
 _snake_case    :  List[Any]					=					[os.path.join(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) for f in os.listdir(SCREAMING_SNAKE_CASE__				) if f.endswith(""".py"""				)]
 _snake_case    :  str					=					[sort_auto_mapping(SCREAMING_SNAKE_CASE__				,       overwrite=SCREAMING_SNAKE_CASE__				) for fname in fnames]

 if not overwrite and any(SCREAMING_SNAKE_CASE__				):
  _snake_case    :  List[Any]					=					[f for f, d in zip(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) if d]
  raise ValueError(
      F'''The following files have auto mappings that need sorting: {', '.join(SCREAMING_SNAKE_CASE__				)}. Run `make style` to fix'''
      """ this."""				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  a__    =	parser.parse_args()

  sort_all_auto_mappings(not args.check_only)
