




"""simple docstring"""






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


lowercase__     						=						"""src/transformers"""


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







def      _snake_case			(				lowercase__		):
				if _re_test_backend.search(lowercase__		) is None:
								return None
				_lowerCamelCase     :			Optional[Any]    =    [b[0] for b in _re_backend.findall(lowercase__		)]
				backends.sort()
				return "_and_".join(lowercase__		)







def      _snake_case			(				lowercase__		):
				with open(lowercase__							,    'r'							,    encoding='utf-8'							,    newline='\n'		) as f:
								_lowerCamelCase     :			Dict    =    f.readlines()

				_lowerCamelCase     :			Optional[Any]    =    0
				while line_index < len(lowercase__		) and not lines[line_index].startswith('_import_structure = {'		):
								line_index += 1

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

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

				_lowerCamelCase     :			Optional[int]    =    {'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.
								_lowerCamelCase     :			Optional[int]    =    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:
												_lowerCamelCase     :			Union[str, Any]    =    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

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

												_lowerCamelCase     :			Optional[int]    =    objects
								else:
												line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
				_lowerCamelCase     :			List[str]    =    []
				while (
				    line_index < len(lowercase__		)
				    and find_backend(lines[line_index]		) is None
				    and not lines[line_index].startswith('else'		)
				):
								_lowerCamelCase     :			Tuple    =    lines[line_index]
								_lowerCamelCase     :			Optional[int]    =    _re_import.search(lowercase__		)
								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

				_lowerCamelCase     :			Optional[int]    =    {'none': objects}
				# Let's continue with backend-specific objects
				while line_index < len(lowercase__		):
								# If the line is an if is_backend_available, we grab all objects associated.
								_lowerCamelCase     :			Tuple    =    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:
												_lowerCamelCase     :			List[Any]    =    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

												_lowerCamelCase     :			Tuple    =    []
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]		) <= 1 or lines[line_index].startswith(' ' * 8		):
																_lowerCamelCase     :			List[str]    =    lines[line_index]
																_lowerCamelCase     :			List[Any]    =    _re_import.search(lowercase__		)
																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

												_lowerCamelCase     :			Dict    =    objects
								else:
												line_index += 1

				return import_dict_objects, type_hint_objects







def      _snake_case			(				lowercase__							,    lowercase__		):

				def find_duplicates(lowercase__		):
								return [k for k, v in collections.Counter(lowercase__		).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!"]

				_lowerCamelCase     :			Optional[Any]    =    []
				for key in import_dict_objects.keys():
								_lowerCamelCase     :			int    =    find_duplicates(import_dict_objects[key]		)
								if duplicate_imports:
												errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}'''		)
								_lowerCamelCase     :			Any    =    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]		)		):
												_lowerCamelCase     :			Dict    =    '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      _snake_case			(				):
				_lowerCamelCase     :			int    =    []
				for root, _, files in os.walk(lowercase__		):
								if "__init__.py" in files:
												_lowerCamelCase     :			Dict    =    os.path.join(lowercase__							,    '__init__.py'		)
												_lowerCamelCase     :			Any    =    parse_init(lowercase__		)
												if objects is not None:
																_lowerCamelCase     :			str    =    analyze_results(*lowercase__		)
																if len(lowercase__		) > 0:
																				_lowerCamelCase     :			Tuple    =    f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
																				failures.append('\n'.join(lowercase__		)		)
				if len(lowercase__		) > 0:
								raise ValueError('\n\n'.join(lowercase__		)		)







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


lowercase__     						=						[
    """convert_pytorch_checkpoint_to_tf2""",
    """modeling_flax_pytorch_utils""",
]







def      _snake_case			(				):
				# This is to make sure the transformers module imported is the one in the repo.
				_lowerCamelCase     :			int    =    importlib.util.spec_from_file_location(
				    'transformers'							,    os.path.join(lowercase__							,    '__init__.py'		)							,    submodule_search_locations=[PATH_TO_TRANSFORMERS]							,    )
				_lowerCamelCase     :			List[str]    =    spec.loader.load_module()

				_lowerCamelCase     :			Dict    =    [
				    module
				    for module in get_transformers_submodules()
				    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
				]
				if len(lowercase__		) > 0:
								_lowerCamelCase     :			List[Any]    =    '\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"""






import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			DDIMPipeline
			lowerCamelCase__      =			UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			lowerCamelCase__      =			PipelineTesterMixin.required_optional_params - {
			    """num_images_per_prompt""",
			    """latents""",
			    """callback""",
			    """callback_steps""",
			}
			lowerCamelCase__      =			UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
			lowerCamelCase__      =			False
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			List[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')  ,      )
							_lowerCamelCase     :			List[str]    =    DDIMScheduler()
							_lowerCamelCase     :			Optional[int]    =    {'unet': unet, 'scheduler': scheduler}
							return components
			def 					A_    (  self  ,      lowercase  ,      lowercase=0  ):
							if str(lowercase  ).startswith('mps'  ):
											_lowerCamelCase     :			Dict    =    torch.manual_seed(lowercase  )
							else:
											_lowerCamelCase     :			List[str]    =    torch.Generator(device=lowercase  ).manual_seed(lowercase  )
							_lowerCamelCase     :			Tuple    =    {
							    'batch_size': 1,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    'cpu'

							_lowerCamelCase     :			Tuple    =    self.get_dummy_components()
							_lowerCamelCase     :			Optional[Any]    =    self.pipeline_class(**lowercase  )
							pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			str    =    self.get_dummy_inputs(lowercase  )
							_lowerCamelCase     :			int    =    pipe(**lowercase  ).images
							_lowerCamelCase     :			Any    =    image[0, -3:, -3:, -1]

							self.assertEqual(image.shape  ,      (1, 32, 32, 3)  )
							_lowerCamelCase     :			Tuple    =    np.array(
							    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]  )
							_lowerCamelCase     :			str    =    np.abs(image_slice.flatten() - expected_slice  ).max()
							self.assertLessEqual(lowercase  ,      1E-3  )
			def 					A_    (  self  ):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3  )
			def 					A_    (  self  ):
							super().test_save_load_local(expected_max_difference=3E-3  )
			def 					A_    (  self  ):
							super().test_save_load_optional_components(expected_max_difference=3E-3  )






			def 					A_    (  self  ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3  )






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






			'''simple docstring'''



			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    'google/ddpm-cifar10-32'

							_lowerCamelCase     :			Optional[Any]    =    UNetaDModel.from_pretrained(lowercase  )
							_lowerCamelCase     :			Dict    =    DDIMScheduler()

							_lowerCamelCase     :			Dict    =    DDIMPipeline(unet=lowercase  ,      scheduler=lowercase  )
							ddim.to(lowercase  )
							ddim.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			List[str]    =    torch.manual_seed(0  )
							_lowerCamelCase     :			str    =    ddim(generator=lowercase  ,      eta=0.0  ,      output_type='numpy'  ).images

							_lowerCamelCase     :			Dict    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							_lowerCamelCase     :			List[Any]    =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]  )

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






			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    'google/ddpm-ema-bedroom-256'

							_lowerCamelCase     :			str    =    UNetaDModel.from_pretrained(lowercase  )
							_lowerCamelCase     :			str    =    DDIMScheduler.from_pretrained(lowercase  )

							_lowerCamelCase     :			Optional[int]    =    DDIMPipeline(unet=lowercase  ,      scheduler=lowercase  )
							ddpm.to(lowercase  )
							ddpm.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			Tuple    =    torch.manual_seed(0  )
							_lowerCamelCase     :			int    =    ddpm(generator=lowercase  ,      output_type='numpy'  ).images

							_lowerCamelCase     :			Dict    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							_lowerCamelCase     :			str    =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]  )

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