


"""simple docstring"""


import collections
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(r"^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only:    ["foo", "bar"],
a_       							=    re.compile(r"^\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      a__ (				__lowercase   )	->	List[Any]:
				if _re_test_backend.search(__lowercase   ) is None:
								return None
				_A					  =			[b[0] for b in _re_backend.findall(__lowercase   )]
				backends.sort()
				return "_and_".join(__lowercase   )






def      a__ (				__lowercase   )	->	List[Any]:
				with open(__lowercase     ,			"r"     ,			encoding="utf-8"     ,			newline="\n"   ) as f:
								_A					  =			f.readlines()

				_A					  =			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
				_A					  =			[]
				while not lines[line_index].startswith("if TYPE_CHECKING"   ) and find_backend(lines[line_index]   ) is None:
								_A					  =			lines[line_index]
								# If we have everything on a single line, let's deal with it.
								if _re_one_line_import_struct.search(__lowercase   ):
												_A					  =			_re_one_line_import_struct.search(__lowercase   ).groups()[0]
												_A					  =			re.findall(R"\[([^\]]+)\]"     ,			__lowercase   )
												for imp in imports:
																objects.extend([obj[1:-1] for obj in imp.split(", "   )]   )
												line_index += 1
												continue
								_A					  =			_re_import_struct_key_value.search(__lowercase   )
								if single_line_import_search is not None:
												_A					  =			[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

				_A					  =			{"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.
								_A					  =			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:
												_A					  =			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

												_A					  =			[]
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(" " * 4   ):
																_A					  =			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:
																				_A					  =			_re_import_struct_add_many.search(__lowercase   ).groups()[0].split(", "   )
																				_A					  =			[obj[1:-1] for obj in imports if len(__lowercase   ) > 0]
																				objects.extend(__lowercase   )
																elif _re_between_brackets.search(__lowercase   ) is not None:
																				_A					  =			_re_between_brackets.search(__lowercase   ).groups()[0].split(", "   )
																				_A					  =			[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

												_A					  =			objects
								else:
												line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
				_A					  =			[]
				while (
				    line_index < len(__lowercase   )
				    and find_backend(lines[line_index]   ) is None
				    and not lines[line_index].startswith("else"   )
				):
								_A					  =			lines[line_index]
								_A					  =			_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

				_A					  =			{"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.
								_A					  =			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:
												_A					  =			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

												_A					  =			[]
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(" " * 8   ):
																_A					  =			lines[line_index]
																_A					  =			_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

												_A					  =			objects
								else:
												line_index += 1

				return import_dict_objects, type_hint_objects






def      a__ (				__lowercase     ,			__lowercase   )	->	Dict:

				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!"]

				_A					  =			[]
				for key in import_dict_objects.keys():
								_A					  =			find_duplicates(import_dict_objects[key]   )
								if duplicate_imports:
												errors.append(f"""Duplicate _import_structure definitions for: {duplicate_imports}"""   )
								_A					  =			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]   )   ):
												_A					  =			"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      a__ (				)	->	int:
				_A					  =			[]
				for root, _, files in os.walk(__lowercase   ):
								if "__init__.py" in files:
												_A					  =			os.path.join(__lowercase     ,			"__init__.py"   )
												_A					  =			parse_init(__lowercase   )
												if objects is not None:
																_A					  =			analyze_results(*__lowercase   )
																if len(__lowercase   ) > 0:
																				_A					  =			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      a__ (				)	->	Union[str, Any]:
				_A					  =			[]
				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
												_A					  =			str((Path(__lowercase   ) / folder).relative_to(__lowercase   )   )
												_A					  =			short_path.replace(os.path.sep     ,			"."   )
												submodules.append(__lowercase   )
								for fname in files:
												if fname == "__init__.py":
																continue
												_A					  =			str((Path(__lowercase   ) / fname).relative_to(__lowercase   )   )
												_A					  =			short_path.replace(".py"     ,			""   ).replace(os.path.sep     ,			"."   )
												if len(submodule.split("."   )   ) == 1:
																submodules.append(__lowercase   )
				return submodules


a_       							=    [
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
    "models.esm.openfold_utils",
]






def      a__ (				)	->	Optional[Any]:
				# This is to make sure the transformers module imported is the one in the repo.
				from transformers.utils import direct_transformers_import

				_A					  =			direct_transformers_import(__lowercase   )

				_A					  =			set(transformers._import_structure.keys()   )
				# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
				# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
				# (potentiall re-) add them.
				with open(os.path.join(__lowercase     ,			"__init__.py"   )     ,			"r"   ) as f:
								_A					  =			f.read()
				import_structure_keys.update(set(re.findall(R"import_structure\[\"([^\"]*)\"\]"     ,			__lowercase   )   )   )

				_A					  =			[
				    module
				    for module in get_transformers_submodules()
				    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
				]

				if len(__lowercase   ) > 0:
								_A					  =			"\n".join(f"""- {module}""" for module in module_not_registered   )
								raise ValueError(
								    "The following submodules are not properly registed 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 gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_       							=    False
class snake_case      (  unittest.TestCase):
		pass





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







		def  a_ (					self      :       Optional[int]		)       ->						str:





						'''simple docstring'''







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







		def  a_ (					self      :       Tuple		)       ->						Any:





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt="first prompt"    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"    ,   ).images

						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(a__		)
										_A					  =			VersatileDiffusionPipeline.from_pretrained(a__    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			generator.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt="first prompt"    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"    ,   ).images

						assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"







		def  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			"cyberpunk 2077"
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt=a__    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"    ,   ).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]		)

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

						_A					  =			"A painting of a squirrel eating a burger "
						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.text_to_image(
						    prompt=a__    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"		).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]		)

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

						_A					  =			pipe.image_variation(a__    ,   generator=a__    ,   output_type="numpy"		).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]		)

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