


"""simple docstring"""





import collections
import importlib.util
import os
import re
from pathlib import Path


_a														=		"src/transformers"


# Matches is_xxx_available()
_a														=		re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
_a														=		re.compile(R"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_a														=		re.compile(R"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
_a														=		re.compile(R"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
_a														=		re.compile(R"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_a														=		re.compile(R"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma:     "MyModel",
_a														=		re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only:    ["foo", "bar"],
_a														=		re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
_a														=		re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
_a														=		re.compile(R"""^\s*try:""")
# Catches a line with else:
_a														=		re.compile(R"""^\s*else:""")



def 				lowerCamelCase__		(      __snake_case     )							->		List[Any]:


					"""simple docstring"""





					if _re_test_backend.search(lowerCamelCase_     ) is None:
										return None
					_UpperCamelCase										=  [b[0] for b in _re_backend.findall(lowerCamelCase_     )]
					backends.sort()
					return "_and_".join(lowerCamelCase_     )



def 				lowerCamelCase__		(      __snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					with open(lowerCamelCase_,					'''r''',					encoding='''utf-8''',					newline='''\n'''     ) as f:
										_UpperCamelCase										=  f.readlines()

					_UpperCamelCase										=  0
					while line_index < len(lowerCamelCase_     ) and not lines[line_index].startswith('''_import_structure = {'''     ):
										line_index += 1

					# If this is a traditional init, just return.
					if line_index >= len(lowerCamelCase_     ):
										return None

					# First grab the objects without a specific backend in _import_structure
					_UpperCamelCase										=  []
					while not lines[line_index].startswith('''if TYPE_CHECKING'''     ) and find_backend(lines[line_index]     ) is None:
										_UpperCamelCase										=  lines[line_index]
										# If we have everything on a single line, let's deal with it.
										if _re_one_line_import_struct.search(lowerCamelCase_     ):
															_UpperCamelCase										=  _re_one_line_import_struct.search(lowerCamelCase_     ).groups()[0]
															_UpperCamelCase										=  re.findall('''\[([^\]]+)\]''',					lowerCamelCase_     )
															for imp in imports:
																				objects.extend([obj[1:-1] for obj in imp.split(''', '''     )]     )
															line_index += 1
															continue
										_UpperCamelCase										=  _re_import_struct_key_value.search(lowerCamelCase_     )
										if single_line_import_search is not None:
															_UpperCamelCase										=  [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', '''     ) if len(lowerCamelCase_     ) > 0]
															objects.extend(lowerCamelCase_     )
										elif line.startswith(''' ''' * 8 + '''"'''     ):
															objects.append(line[9:-3]     )
										line_index += 1

					_UpperCamelCase										=  {'none': objects}
					# Let's continue with backend-specific objects in _import_structure
					while not lines[line_index].startswith('''if TYPE_CHECKING'''     ):
										# If the line is an if not is_backend_available, we grab all objects associated.
										_UpperCamelCase										=  find_backend(lines[line_index]     )
										# Check if the backend declaration is inside a try block:
										if _re_try.search(lines[line_index - 1]     ) is None:
															_UpperCamelCase										=  None

										if backend is not None:
															line_index += 1

															# Scroll until we hit the else block of try-except-else
															while _re_else.search(lines[line_index]     ) is None:
																				line_index += 1

															line_index += 1

															_UpperCamelCase										=  []
															# Until we unindent, add backend objects to the list
															while len(lines[line_index]     ) <= 1 or lines[line_index].startswith(''' ''' * 4     ):
																				_UpperCamelCase										=  lines[line_index]
																				if _re_import_struct_add_one.search(lowerCamelCase_     ) is not None:
																									objects.append(_re_import_struct_add_one.search(lowerCamelCase_     ).groups()[0]     )
																				elif _re_import_struct_add_many.search(lowerCamelCase_     ) is not None:
																									_UpperCamelCase										=  _re_import_struct_add_many.search(lowerCamelCase_     ).groups()[0].split(''', '''     )
																									_UpperCamelCase										=  [obj[1:-1] for obj in imports if len(lowerCamelCase_     ) > 0]
																									objects.extend(lowerCamelCase_     )
																				elif _re_between_brackets.search(lowerCamelCase_     ) is not None:
																									_UpperCamelCase										=  _re_between_brackets.search(lowerCamelCase_     ).groups()[0].split(''', '''     )
																									_UpperCamelCase										=  [obj[1:-1] for obj in imports if len(lowerCamelCase_     ) > 0]
																									objects.extend(lowerCamelCase_     )
																				elif _re_quote_object.search(lowerCamelCase_     ) is not None:
																									objects.append(_re_quote_object.search(lowerCamelCase_     ).groups()[0]     )
																				elif line.startswith(''' ''' * 8 + '''"'''     ):
																									objects.append(line[9:-3]     )
																				elif line.startswith(''' ''' * 12 + '''"'''     ):
																									objects.append(line[13:-3]     )
																				line_index += 1

															_UpperCamelCase										=  objects
										else:
															line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
					_UpperCamelCase										=  []
					while (
					    line_index < len(lowerCamelCase_     )
					    and find_backend(lines[line_index]     ) is None
					    and not lines[line_index].startswith('''else'''     )
					):
										_UpperCamelCase										=  lines[line_index]
										_UpperCamelCase										=  _re_import.search(lowerCamelCase_     )
										if single_line_import_search is not None:
															objects.extend(single_line_import_search.groups()[0].split(''', '''     )     )
										elif line.startswith(''' ''' * 8     ):
															objects.append(line[8:-2]     )
										line_index += 1

					_UpperCamelCase										=  {'none': objects}
					# Let's continue with backend-specific objects
					while line_index < len(lowerCamelCase_     ):
										# If the line is an if is_backend_available, we grab all objects associated.
										_UpperCamelCase										=  find_backend(lines[line_index]     )
										# Check if the backend declaration is inside a try block:
										if _re_try.search(lines[line_index - 1]     ) is None:
															_UpperCamelCase										=  None

										if backend is not None:
															line_index += 1

															# Scroll until we hit the else block of try-except-else
															while _re_else.search(lines[line_index]     ) is None:
																				line_index += 1

															line_index += 1

															_UpperCamelCase										=  []
															# Until we unindent, add backend objects to the list
															while len(lines[line_index]     ) <= 1 or lines[line_index].startswith(''' ''' * 8     ):
																				_UpperCamelCase										=  lines[line_index]
																				_UpperCamelCase										=  _re_import.search(lowerCamelCase_     )
																				if single_line_import_search is not None:
																									objects.extend(single_line_import_search.groups()[0].split(''', '''     )     )
																				elif line.startswith(''' ''' * 12     ):
																									objects.append(line[12:-2]     )
																				line_index += 1

															_UpperCamelCase										=  objects
										else:
															line_index += 1

					return import_dict_objects, type_hint_objects



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					def find_duplicates(__snake_case     ):
										return [k for k, v in collections.Counter(lowerCamelCase_     ).items() if v > 1]

					if list(import_dict_objects.keys()     ) != list(type_hint_objects.keys()     ):
										return ["Both sides of the init do not have the same backends!"]

					_UpperCamelCase										=  []
					for key in import_dict_objects.keys():
										_UpperCamelCase										=  find_duplicates(import_dict_objects[key]     )
										if duplicate_imports:
															errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}'''     )
										_UpperCamelCase										=  find_duplicates(type_hint_objects[key]     )
										if duplicate_type_hints:
															errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''     )

										if sorted(set(import_dict_objects[key]     )     ) != sorted(set(type_hint_objects[key]     )     ):
															_UpperCamelCase										=  'base imports' if key == 'none' else F'''{key} backend'''
															errors.append(F'''Differences for {name}:'''     )
															for a in type_hint_objects[key]:
																				if a not in import_dict_objects[key]:
																									errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.'''     )
															for a in import_dict_objects[key]:
																				if a not in type_hint_objects[key]:
																									errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.'''     )
					return errors



def 				lowerCamelCase__		(      )							->		str:


					"""simple docstring"""





					_UpperCamelCase										=  []
					for root, _, files in os.walk(lowerCamelCase_     ):
										if "__init__.py" in files:
															_UpperCamelCase										=  os.path.join(lowerCamelCase_,					'''__init__.py'''     )
															_UpperCamelCase										=  parse_init(lowerCamelCase_     )
															if objects is not None:
																				_UpperCamelCase										=  analyze_results(*lowerCamelCase_     )
																				if len(lowerCamelCase_     ) > 0:
																									_UpperCamelCase										=  F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
																									failures.append('''\n'''.join(lowerCamelCase_     )     )
					if len(lowerCamelCase_     ) > 0:
										raise ValueError('''\n\n'''.join(lowerCamelCase_     )     )



def 				lowerCamelCase__		(      )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  []
					for path, directories, files in os.walk(lowerCamelCase_     ):
										for folder in directories:
															# Ignore private modules
															if folder.startswith('''_'''     ):
																				directories.remove(lowerCamelCase_     )
																				continue
															# Ignore leftovers from branches (empty folders apart from pycache)
															if len(list((Path(lowerCamelCase_     ) / folder).glob('''*.py'''     )     )     ) == 0:
																				continue
															_UpperCamelCase										=  str((Path(lowerCamelCase_     ) / folder).relative_to(lowerCamelCase_     )     )
															_UpperCamelCase										=  short_path.replace(os.path.sep,					'''.'''     )
															submodules.append(lowerCamelCase_     )
										for fname in files:
															if fname == "__init__.py":
																				continue
															_UpperCamelCase										=  str((Path(lowerCamelCase_     ) / fname).relative_to(lowerCamelCase_     )     )
															_UpperCamelCase										=  short_path.replace('''.py''',					''''''     ).replace(os.path.sep,					'''.'''     )
															if len(submodule.split('''.'''     )     ) == 1:
																				submodules.append(lowerCamelCase_     )
					return submodules


_a														=		[
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
]



def 				lowerCamelCase__		(      )							->		Any:


					"""simple docstring"""





					_UpperCamelCase										=  importlib.util.spec_from_file_location(
					    '''transformers''',					os.path.join(lowerCamelCase_,					'''__init__.py'''     ),					submodule_search_locations=[PATH_TO_TRANSFORMERS],					)
					_UpperCamelCase										=  spec.loader.load_module()

					_UpperCamelCase										=  [
					    module
					    for module in get_transformers_submodules()
					    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
					]
					if len(lowerCamelCase_     ) > 0:
										_UpperCamelCase										=  '\n'.join(F'''- {module}''' for module in module_not_registered     )
										raise ValueError(
										    '''The following submodules are not properly registered in the main init of Transformers:\n'''
										    F'''{list_of_modules}\n'''
										    '''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'''     )


if __name__ == "__main__":
				check_all_inits()
				check_submodules()








"""simple docstring"""





from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    _UpperCAmelCase(   lowerCamelCase     ):



		def __init__( self    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		)	->	List[str]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(transformer=__a    ,		vae=__a    ,		scheduler=__a)

							# create a imagenet -> id dictionary for easier use
							_UpperCamelCase										=  {}
							if idalabel is not None:
												for key, value in idalabel.items():
																	for label in value.split(''','''):
																						_UpperCamelCase										=  int(__a)
												_UpperCamelCase										=  dict(sorted(self.labels.items()))



		def 						UpperCAmelCase							( self    ,		__a)	->	List[int]:






							'''simple docstring'''

							if not isinstance(__a    ,		__a):
												_UpperCamelCase										=  list(__a)

							for l in label:
												if l not in self.labels:
																	raise ValueError(
																	    F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''')

							return [self.labels[l] for l in label]




		@torch.no_grad()
		def __call__( self    ,		__a    ,		__a = 4.0    ,		__a = None    ,		__a = 50    ,		__a = "pil"    ,		__a = True    ,		)	->	Union[ImagePipelineOutput, Tuple]:






							'''simple docstring'''
							_UpperCamelCase										=  len(__a)
							_UpperCamelCase										=  self.transformer.config.sample_size
							_UpperCamelCase										=  self.transformer.config.in_channels

							_UpperCamelCase										=  randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)    ,		generator=__a    ,		device=self.device    ,		dtype=self.transformer.dtype    ,		)
							_UpperCamelCase										=  torch.cat([latents] * 2) if guidance_scale > 1 else latents

							_UpperCamelCase										=  torch.tensor(__a    ,		device=self.device).reshape(-1)
							_UpperCamelCase										=  torch.tensor([10_00] * batch_size    ,		device=self.device)
							_UpperCamelCase										=  torch.cat([class_labels, class_null]    ,		0) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(__a)

							for t in self.progress_bar(self.scheduler.timesteps):
												if guidance_scale > 1:
																	_UpperCamelCase										=  latent_model_input[: len(__a) // 2]
																	_UpperCamelCase										=  torch.cat([half, half]    ,		dim=0)
												_UpperCamelCase										=  self.scheduler.scale_model_input(__a    ,		__a)

												_UpperCamelCase										=  t
												if not torch.is_tensor(__a):
																	# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																	# This would be a good case for the `match` statement (Python 3.10+)
																	_UpperCamelCase										=  latent_model_input.device.type == '''mps'''
																	if isinstance(__a    ,		__a):
																						_UpperCamelCase										=  torch.floataa if is_mps else torch.floataa
																	else:
																						_UpperCamelCase										=  torch.intaa if is_mps else torch.intaa
																	_UpperCamelCase										=  torch.tensor([timesteps]    ,		dtype=__a    ,		device=latent_model_input.device)
												elif len(timesteps.shape) == 0:
																	_UpperCamelCase										=  timesteps[None].to(latent_model_input.device)
												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												_UpperCamelCase										=  timesteps.expand(latent_model_input.shape[0])
												# predict noise model_output
												_UpperCamelCase										=  self.transformer(
												    __a    ,		timestep=__a    ,		class_labels=__a).sample

												# perform guidance
												if guidance_scale > 1:
																	_UpperCamelCase			,  _UpperCamelCase										=  noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																	_UpperCamelCase			,  _UpperCamelCase										=  torch.split(__a    ,		len(__a) // 2    ,		dim=0)

																	_UpperCamelCase										=  uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																	_UpperCamelCase										=  torch.cat([half_eps, half_eps]    ,		dim=0)

																	_UpperCamelCase										=  torch.cat([eps, rest]    ,		dim=1)

												# learned sigma
												if self.transformer.config.out_channels // 2 == latent_channels:
																	_UpperCamelCase			,  _UpperCamelCase										=  torch.split(__a    ,		__a    ,		dim=1)
												else:
																	_UpperCamelCase										=  noise_pred

												# compute previous image: x_t -> x_t-1
												_UpperCamelCase										=  self.scheduler.step(__a    ,		__a    ,		__a).prev_sample

							if guidance_scale > 1:
												_UpperCamelCase			,  _UpperCamelCase										=  latent_model_input.chunk(2    ,		dim=0)
							else:
												_UpperCamelCase										=  latent_model_input

							_UpperCamelCase										=  1 / self.vae.config.scaling_factor * latents
							_UpperCamelCase										=  self.vae.decode(__a).sample

							_UpperCamelCase										=  (samples / 2 + 0.5).clamp(0    ,		1)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							_UpperCamelCase										=  samples.cpu().permute(0    ,		2    ,		3    ,		1).float().numpy()

							if output_type == "pil":
												_UpperCamelCase										=  self.numpy_to_pil(__a)

							if not return_dict:
												return (samples,)

							return ImagePipelineOutput(images=__a)






