
import argparse
import os
import re


lowerCamelCase__     :		str        = """src/diffusers"""

# Pattern that looks at the indentation in a line.
lowerCamelCase__     :		str        = re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__     :		Optional[Any]        = re.compile(R"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCamelCase__     :		str        = re.compile(R"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCamelCase__     :		Any        = re.compile(R"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCamelCase__     :		Union[str, Any]        = re.compile(R"""\[([^\]]+)\]""")

def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   _re_indent.search(lowercase_ )
  return "" if search is None else search.groups()[0]

def 		UpperCamelCase						(   lowercase_			,			lowercase_=""			,			lowercase_=None			,			lowercase_=None )		->						Optional[int]:


  '''simple docstring'''






  lowercase__	: int     		=   0
  lowercase__	: List[Any]     		=   code.split("""\n""" )
  if start_prompt is not None:
    while not lines[index].startswith(lowercase_ ):
      index += 1
    lowercase__	: Dict     		=   ["""\n""".join(lines[:index] )]
  else:
    lowercase__	: Dict     		=   []

  # We split into blocks until we get to the `end_prompt` (or the end of the block).
  lowercase__	: str     		=   [lines[index]]
  index += 1
  while index < len(lowercase_ ) and (end_prompt is None or not lines[index].startswith(lowercase_ )):
    if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
      if len(lowercase_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
        current_block.append(lines[index] )
        blocks.append("""\n""".join(lowercase_ ) )
        if index < len(lowercase_ ) - 1:
          lowercase__	: Union[str, Any]     		=   [lines[index + 1]]
          index += 1
        else:
          lowercase__	: Union[str, Any]     		=   []
      else:
        blocks.append("""\n""".join(lowercase_ ) )
        lowercase__	: List[Any]     		=   [lines[index]]
    else:
      current_block.append(lines[index] )
    index += 1

  # Adds current block if it's nonempty.
  if len(lowercase_ ) > 0:
    blocks.append("""\n""".join(lowercase_ ) )

  # Add final block after end_prompt if provided.
  if end_prompt is not None and index < len(lowercase_ ):
    blocks.append("""\n""".join(lines[index:] ) )

  return blocks

def 		UpperCamelCase						(   lowercase_ )		->						List[Any]:


  '''simple docstring'''






  def _inner(lowercase_ ):
    return key(lowercase_ ).lower().replace("""_"""			,			"""""" )

  return _inner

def 		UpperCamelCase						(   lowercase_			,			lowercase_=None )		->						str:


  '''simple docstring'''






  def noop(lowercase_ ):
    return x

  if key is None:
    lowercase__	: str     		=   noop
  # Constants are all uppercase, they go first.
  lowercase__	: List[Any]     		=   [obj for obj in objects if key(lowercase_ ).isupper()]
  # Classes are not all uppercase but start with a capital, they go second.
  lowercase__	: List[str]     		=   [obj for obj in objects if key(lowercase_ )[0].isupper() and not key(lowercase_ ).isupper()]
  # Functions begin with a lowercase, they go last.
  lowercase__	: Tuple     		=   [obj for obj in objects if not key(lowercase_ )[0].isupper()]

  lowercase__	: Optional[Any]     		=   ignore_underscore(lowercase_ )
  return sorted(lowercase_			,			key=lowercase_ ) + sorted(lowercase_			,			key=lowercase_ ) + sorted(lowercase_			,			key=lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Dict:


  '''simple docstring'''






  def _replace(lowercase_ ):
    lowercase__	: int     		=   match.groups()[0]
    if "," not in imports:
      return F'[{imports}]'
    lowercase__	: Tuple     		=   [part.strip().replace("""\""""			,			"""""" ) for part in imports.split(""",""" )]
    # We will have a final empty element if the line finished with a comma.
    if len(keys[-1] ) == 0:
      lowercase__	: str     		=   keys[:-1]
    return "[" + ", ".join([F'"{k}"' for k in sort_objects(lowercase_ )] ) + "]"

  lowercase__	: Optional[int]     		=   import_statement.split("""\n""" )
  if len(lowercase_ ) > 3:
    # Here we have to sort internal imports that are on several lines (one per name):
    # key: [
    #     "object1",
    #     "object2",
    #     ...
    # ]

    # We may have to ignore one or two lines on each side.
    lowercase__	: List[str]     		=   2 if lines[1].strip() == """[""" else 1
    lowercase__	: Union[str, Any]     		=   [(i, _re_strip_line.search(lowercase_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
    lowercase__	: Any     		=   sort_objects(lowercase_			,			key=lambda lowercase_ : x[1] )
    lowercase__	: Optional[Any]     		=   [lines[x[0] + idx] for x in sorted_indices]
    return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
  elif len(lowercase_ ) == 3:
    # Here we have to sort internal imports that are on one separate line:
    # key: [
    #     "object1", "object2", ...
    # ]
    if _re_bracket_content.search(lines[1] ) is not None:
      lowercase__	: Any     		=   _re_bracket_content.sub(_replace			,			lines[1] )
    else:
      lowercase__	: Tuple     		=   [part.strip().replace("""\""""			,			"""""" ) for part in lines[1].split(""",""" )]
      # We will have a final empty element if the line finished with a comma.
      if len(keys[-1] ) == 0:
        lowercase__	: List[str]     		=   keys[:-1]
      lowercase__	: List[str]     		=   get_indent(lines[1] ) + """, """.join([F'"{k}"' for k in sort_objects(lowercase_ )] )
    return "\n".join(lowercase_ )
  else:
    # Finally we have to deal with imports fitting on one line
    lowercase__	: str     		=   _re_bracket_content.sub(_replace			,			lowercase_ )
    return import_statement

def 		UpperCamelCase						(   lowercase_			,			lowercase_=True )		->						int:


  '''simple docstring'''






  with open(lowercase_			,			"""r""" ) as f:
    lowercase__	: List[Any]     		=   f.read()

  if "_import_structure" not in code:
    return

  # Blocks of indent level 0
  lowercase__	: Optional[Any]     		=   split_code_in_indented_blocks(
      lowercase_			,			start_prompt="""_import_structure = {"""			,			end_prompt="""if TYPE_CHECKING:""" )

  # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
  for block_idx in range(1			,			len(lowercase_ ) - 1 ):
    # Check if the block contains some `_import_structure`s thingy to sort.
    lowercase__	: Any     		=   main_blocks[block_idx]
    lowercase__	: Union[str, Any]     		=   block.split("""\n""" )

    # Get to the start of the imports.
    lowercase__	: int     		=   0
    while line_idx < len(lowercase_ ) and "_import_structure" not in block_lines[line_idx]:
      # Skip dummy import blocks
      if "import dummy" in block_lines[line_idx]:
        lowercase__	: List[str]     		=   len(lowercase_ )
      else:
        line_idx += 1
    if line_idx >= len(lowercase_ ):
      continue

    # Ignore beginning and last line: they don't contain anything.
    lowercase__	: List[Any]     		=   """\n""".join(block_lines[line_idx:-1] )
    lowercase__	: Optional[Any]     		=   get_indent(block_lines[1] )
    # Slit the internal block into blocks of indent level 1.
    lowercase__	: Union[str, Any]     		=   split_code_in_indented_blocks(lowercase_			,			indent_level=lowercase_ )
    # We have two categories of import key: list or _import_structure[key].append/extend
    lowercase__	: Tuple     		=   _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
    # Grab the keys, but there is a trap: some lines are empty or just comments.
    lowercase__	: Dict     		=   [(pattern.search(lowercase_ ).groups()[0] if pattern.search(lowercase_ ) is not None else None) for b in internal_blocks]
    # We only sort the lines with a key.
    lowercase__	: Any     		=   [(i, key) for i, key in enumerate(lowercase_ ) if key is not None]
    lowercase__	: List[str]     		=   [x[0] for x in sorted(lowercase_			,			key=lambda lowercase_ : x[1] )]

    # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
    lowercase__	: Any     		=   0
    lowercase__	: Optional[int]     		=   []
    for i in range(len(lowercase_ ) ):
      if keys[i] is None:
        reordered_blocks.append(internal_blocks[i] )
      else:
        lowercase__	: List[str]     		=   sort_objects_in_import(internal_blocks[sorted_indices[count]] )
        reordered_blocks.append(lowercase_ )
        count += 1

        # And we put our main block back together with its first and last line.
    lowercase__	: List[Any]     		=   """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )

  if code != "\n".join(lowercase_ ):
    if check_only:
      return True
    else:
      print(F'Overwriting {file}.' )
      with open(lowercase_			,			"""w""" ) as f:
        f.write("""\n""".join(lowercase_ ) )

def 		UpperCamelCase						(   lowercase_=True )		->						int:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   []
  for root, _, files in os.walk(lowercase_ ):
    if "__init__.py" in files:
      lowercase__	: int     		=   sort_imports(os.path.join(lowercase_			,			"""__init__.py""" )			,			check_only=lowercase_ )
      if result:
        lowercase__	: Dict     		=   [os.path.join(lowercase_			,			"""__init__.py""" )]
  if len(lowercase_ ) > 0:
    raise ValueError(F'Would overwrite {len(lowercase_ )} files, run `make style`.' )


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

     sort_imports_in_all_inits(check_only=args.check_only)














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


UpperCAmelCase__							  =						False






class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Dict    )       ->str:




       """simple docstring"""


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







      @property
      def  __lowerCAmelCase   ( self :		List[str]    )       ->Any:




       """simple docstring"""


       return 12







      @property
      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Dict:




       """simple docstring"""


       return 12







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->Any:




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		int    )       ->List[str]:




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = 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  __lowerCAmelCase   ( self :		str    )       ->str:




       """simple docstring"""


       a						    = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )
       return tokenizer







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




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = CLIPTextConfig(
           bos_token_id=0					,   eos_token_id=2					,   hidden_size=self.text_embedder_hidden_size					,   intermediate_size=37					,   layer_norm_eps=1e-0_5					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1_000					,   )
       return CLIPTextModel(__UpperCAmelCase    )







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->Tuple:




       """simple docstring"""


       torch.manual_seed(0    )

       a						    = 12
       a						    = 12

       a						    = {
           '''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''',
       }

       a						    = TransformeraDModel(**__UpperCAmelCase    )
       return model







      def  __lowerCAmelCase   ( self :		Dict    )       ->Any:




       """simple docstring"""


       a						    = '''cpu'''

       a						    = self.dummy_vqvae
       a						    = self.dummy_text_encoder
       a						    = self.dummy_tokenizer
       a						    = self.dummy_transformer
       a						    = VQDiffusionScheduler(self.num_embed    )
       a						    = LearnedClassifierFreeSamplingEmbeddings(learnable=__UpperCAmelCase    )

       a						    = VQDiffusionPipeline(
           vqvae=__UpperCAmelCase					,   text_encoder=__UpperCAmelCase					,   tokenizer=__UpperCAmelCase					,   transformer=__UpperCAmelCase					,   scheduler=__UpperCAmelCase					,   learned_classifier_free_sampling_embeddings=__UpperCAmelCase					,   )
       a						    = pipe.to(__UpperCAmelCase    )
       pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = '''teddy bear playing in the pool'''

       a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(0    )
       a						    = pipe([prompt]					,   generator=__UpperCAmelCase					,   num_inference_steps=2					,   output_type='''np'''    )
       a						    = output.images

       a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(0    )
       a						    = pipe(
           [prompt]					,   generator=__UpperCAmelCase					,   output_type='''np'''					,   return_dict=__UpperCAmelCase					,   num_inference_steps=2    )[0]

       a						    = image[0, -3:, -3:, -1]
       a						    = image_from_tuple[0, -3:, -3:, -1]

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

       a						    = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]    )

       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  __lowerCAmelCase   ( self :		Tuple    )       ->str:




       """simple docstring"""


       a						    = '''cpu'''

       a						    = self.dummy_vqvae
       a						    = self.dummy_text_encoder
       a						    = self.dummy_tokenizer
       a						    = self.dummy_transformer
       a						    = VQDiffusionScheduler(self.num_embed    )
       a						    = LearnedClassifierFreeSamplingEmbeddings(
           learnable=__UpperCAmelCase					,   hidden_size=self.text_embedder_hidden_size					,   length=tokenizer.model_max_length    )

       a						    = VQDiffusionPipeline(
           vqvae=__UpperCAmelCase					,   text_encoder=__UpperCAmelCase					,   tokenizer=__UpperCAmelCase					,   transformer=__UpperCAmelCase					,   scheduler=__UpperCAmelCase					,   learned_classifier_free_sampling_embeddings=__UpperCAmelCase					,   )
       a						    = pipe.to(__UpperCAmelCase    )
       pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = '''teddy bear playing in the pool'''

       a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(0    )
       a						    = pipe([prompt]					,   generator=__UpperCAmelCase					,   num_inference_steps=2					,   output_type='''np'''    )
       a						    = output.images

       a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(0    )
       a						    = pipe(
           [prompt]					,   generator=__UpperCAmelCase					,   output_type='''np'''					,   return_dict=__UpperCAmelCase					,   num_inference_steps=2    )[0]

       a						    = image[0, -3:, -3:, -1]
       a						    = image_from_tuple[0, -3:, -3:, -1]

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

       a						    = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]    )

       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 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Union[str, Any]:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Dict    )       ->int:




       """simple docstring"""


       a						    = load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''    )

       a						    = VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''    )
       a						    = pipeline.to(__UpperCAmelCase    )
       pipeline.set_progress_bar_config(disable=__UpperCAmelCase    )

       # requires GPU generator for gumbel softmax
       # don't use GPU generator in tests though
       a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(0    )
       a						    = pipeline(
           '''teddy bear playing in the pool'''					,   num_images_per_prompt=1					,   generator=__UpperCAmelCase					,   output_type='''np'''					,   )

       a						    = output.images[0]

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


