

"""simple docstring"""


import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import BeitImageProcessor





class    snake_case     (    unittest.TestCase  ):

				def __init__(     self							:    Union[str, Any]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    Union[str, Any]=7					,  UpperCamelCase__							:    str=3					,  UpperCamelCase__							:    Optional[int]=1_8					,  UpperCamelCase__							:    Union[str, Any]=3_0					,  UpperCamelCase__							:    str=4_0_0					,  UpperCamelCase__							:    str=True					,  UpperCamelCase__							:    Optional[Any]=None					,  UpperCamelCase__							:    Optional[Any]=True					,  UpperCamelCase__							:    Tuple=None					,  UpperCamelCase__							:    Any=True					,  UpperCamelCase__							:    Any=[0.5, 0.5, 0.5]					,  UpperCamelCase__							:    Union[str, Any]=[0.5, 0.5, 0.5]					,  UpperCamelCase__							:    List[Any]=False					,  )->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      size if size is not None else {'height': 2_0, 'width': 2_0}
						__lowerCAmelCase:	int   				=      crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
						__lowerCAmelCase:	Union[str, Any]   				=      parent
						__lowerCAmelCase:	Any   				=      batch_size
						__lowerCAmelCase:	List[str]   				=      num_channels
						__lowerCAmelCase:	List[str]   				=      image_size
						__lowerCAmelCase:	Optional[Any]   				=      min_resolution
						__lowerCAmelCase:	Optional[int]   				=      max_resolution
						__lowerCAmelCase:	Union[str, Any]   				=      do_resize
						__lowerCAmelCase:	Any   				=      size
						__lowerCAmelCase:	int   				=      do_center_crop
						__lowerCAmelCase:	int   				=      crop_size
						__lowerCAmelCase:	Union[str, Any]   				=      do_normalize
						__lowerCAmelCase:	Optional[int]   				=      image_mean
						__lowerCAmelCase:	int   				=      image_std
						__lowerCAmelCase:	Tuple   				=      do_reduce_labels




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



						'''simple docstring'''


						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_reduce_labels": self.do_reduce_labels,
						}






def    a__   (   )     ->     str:
		__lowerCAmelCase:	Union[str, Any]   				=      load_dataset("hf-internal-testing/fixtures_ade20k" ,			split="test"			)

		__lowerCAmelCase:	Union[str, Any]   				=      Image.open(dataset[0]["file"]			)
		__lowerCAmelCase:	Any   				=      Image.open(dataset[1]["file"]			)

		return image, map






def    a__   (   )     ->     List[str]:
		__lowerCAmelCase:	int   				=      load_dataset("hf-internal-testing/fixtures_ade20k" ,			split="test"			)

		__lowerCAmelCase:	str   				=      Image.open(ds[0]["file"]			)
		__lowerCAmelCase:	List[str]   				=      Image.open(ds[1]["file"]			)
		__lowerCAmelCase:	int   				=      Image.open(ds[2]["file"]			)
		__lowerCAmelCase:	Dict   				=      Image.open(ds[3]["file"]			)

		return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class    snake_case     (    SCREAMING_SNAKE_CASE_,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				int       				=  BeitImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    Dict)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      BeitImageProcessingTester(self)

				@property
				def 							lowercase_					(     self							:    Tuple)->       str:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(snake_case__					,  "do_resize"))
						self.assertTrue(hasattr(snake_case__					,  "size"))
						self.assertTrue(hasattr(snake_case__					,  "do_center_crop"))
						self.assertTrue(hasattr(snake_case__					,  "center_crop"))
						self.assertTrue(hasattr(snake_case__					,  "do_normalize"))
						self.assertTrue(hasattr(snake_case__					,  "image_mean"))
						self.assertTrue(hasattr(snake_case__					,  "image_std"))

				def 							lowercase_					(     self							:    str)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size					,  {"height": 2_0, "width": 2_0})
						self.assertEqual(image_processor.crop_size					,  {"height": 1_8, "width": 1_8})
						self.assertEqual(image_processor.do_reduce_labels					,  snake_case__)

						__lowerCAmelCase:	List[str]   				=      self.image_processing_class.from_dict(
						    self.image_processor_dict					,  size=4_2					,  crop_size=8_4					,  reduce_labels=snake_case__)
						self.assertEqual(image_processor.size					,  {"height": 4_2, "width": 4_2})
						self.assertEqual(image_processor.crop_size					,  {"height": 8_4, "width": 8_4})
						self.assertEqual(image_processor.do_reduce_labels					,  snake_case__)

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



						'''simple docstring'''


						pass

				def 							lowercase_					(     self							:    Any)->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	int   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=snake_case__)
						for image in image_inputs:
								self.assertIsInstance(snake_case__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	Tuple   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Tuple   				=      image_processing(snake_case__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

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



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase:	Tuple   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=snake_case__					,  numpify=snake_case__)
						for image in image_inputs:
								self.assertIsInstance(snake_case__					,  np.ndarray)

						# Test not batched input
						__lowerCAmelCase:	str   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Union[str, Any]   				=      image_processing(snake_case__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

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



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:	str   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=snake_case__					,  torchify=snake_case__)
						for image in image_inputs:
								self.assertIsInstance(snake_case__					,  torch.Tensor)

						# Test not batched input
						__lowerCAmelCase:	Union[str, Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Union[str, Any]   				=      image_processing(snake_case__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

				def 							lowercase_					(     self							:    int)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:	Tuple   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=snake_case__					,  torchify=snake_case__)
						__lowerCAmelCase:	List[Any]   				=      []
						for image in image_inputs:
								self.assertIsInstance(snake_case__					,  torch.Tensor)
								maps.append(torch.zeros(image.shape[-2:]).long())

						# Test not batched input
						__lowerCAmelCase:	List[Any]   				=      image_processing(image_inputs[0]					,  maps[0]					,  return_tensors="pt")
						self.assertEqual(
						    encoding["pixel_values"].shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )
						self.assertEqual(
						    encoding["labels"].shape					,  (
						        1,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )
						self.assertEqual(encoding["labels"].dtype					,  torch.long)
						self.assertTrue(encoding["labels"].min().item() >= 0)
						self.assertTrue(encoding["labels"].max().item() <= 2_5_5)

						# Test batched
						__lowerCAmelCase:	List[Any]   				=      image_processing(snake_case__					,  snake_case__					,  return_tensors="pt")
						self.assertEqual(
						    encoding["pixel_values"].shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )
						self.assertEqual(
						    encoding["labels"].shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )
						self.assertEqual(encoding["labels"].dtype					,  torch.long)
						self.assertTrue(encoding["labels"].min().item() >= 0)
						self.assertTrue(encoding["labels"].max().item() <= 2_5_5)

						# Test not batched input (PIL images)
						__lowerCAmelCase:	List[Any]   				=      prepare_semantic_single_inputs()

						__lowerCAmelCase:	Any   				=      image_processing(snake_case__					,  snake_case__					,  return_tensors="pt")
						self.assertEqual(
						    encoding["pixel_values"].shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )
						self.assertEqual(
						    encoding["labels"].shape					,  (
						        1,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )
						self.assertEqual(encoding["labels"].dtype					,  torch.long)
						self.assertTrue(encoding["labels"].min().item() >= 0)
						self.assertTrue(encoding["labels"].max().item() <= 2_5_5)

						# Test batched input (PIL images)
						__lowerCAmelCase:	Optional[int]   				=      prepare_semantic_batch_inputs()

						__lowerCAmelCase:	Dict   				=      image_processing(snake_case__					,  snake_case__					,  return_tensors="pt")
						self.assertEqual(
						    encoding["pixel_values"].shape					,  (
						        2,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )
						self.assertEqual(
						    encoding["labels"].shape					,  (
						        2,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )
						self.assertEqual(encoding["labels"].dtype					,  torch.long)
						self.assertTrue(encoding["labels"].min().item() >= 0)
						self.assertTrue(encoding["labels"].max().item() <= 2_5_5)




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



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.image_processing_class(**self.image_processor_dict)

						# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
						__lowerCAmelCase:	Optional[int]   				=      prepare_semantic_single_inputs()
						__lowerCAmelCase:	int   				=      image_processing(snake_case__					,  snake_case__					,  return_tensors="pt")
						self.assertTrue(encoding["labels"].min().item() >= 0)
						self.assertTrue(encoding["labels"].max().item() <= 1_5_0)

						__lowerCAmelCase:	Union[str, Any]   				=      True
						__lowerCAmelCase:	Any   				=      image_processing(snake_case__					,  snake_case__					,  return_tensors="pt")
						self.assertTrue(encoding["labels"].min().item() >= 0)
						self.assertTrue(encoding["labels"].max().item() <= 2_5_5)





"""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    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		if _re_test_backend.search(__SCREAMING_SNAKE_CASE			) is None:
				return None
		__lowerCAmelCase:	Union[str, Any]   				=      [b[0] for b in _re_backend.findall(__SCREAMING_SNAKE_CASE			)]
		backends.sort()
		return "_and_".join(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		with open(__SCREAMING_SNAKE_CASE ,			"r" ,			encoding="utf-8" ,			newline="\n"			) as f:
				__lowerCAmelCase:	Optional[int]   				=      f.readlines()

		__lowerCAmelCase:	Dict   				=      0
		while line_index < len(__SCREAMING_SNAKE_CASE			) and not lines[line_index].startswith("_import_structure = {"			):
				line_index += 1

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

		# First grab the objects without a specific backend in _import_structure
		__lowerCAmelCase:	Optional[Any]   				=      []
		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(__SCREAMING_SNAKE_CASE			):
						__lowerCAmelCase:	List[Any]   				=      _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE			).groups()[0]
						__lowerCAmelCase:	List[Any]   				=      re.findall("\[([^\]]+)\]" ,			__SCREAMING_SNAKE_CASE			)
						for imp in imports:
								objects.extend([obj[1:-1] for obj in imp.split(", "			)]			)
						line_index += 1
						continue
				__lowerCAmelCase:	str   				=      _re_import_struct_key_value.search(__SCREAMING_SNAKE_CASE			)
				if single_line_import_search is not None:
						__lowerCAmelCase:	str   				=      [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", "			) if len(__SCREAMING_SNAKE_CASE			) > 0]
						objects.extend(__SCREAMING_SNAKE_CASE			)
				elif line.startswith(" " * 8 + "\""			):
						objects.append(line[9:-3]			)
				line_index += 1

		__lowerCAmelCase:	Tuple   				=      {"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:	Optional[int]   				=      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:	Optional[int]   				=      []
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]			) <= 1 or lines[line_index].startswith(" " * 4			):
								__lowerCAmelCase:	Optional[Any]   				=      lines[line_index]
								if _re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE			) is not None:
										objects.append(_re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE			).groups()[0]			)
								elif _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE			) is not None:
										__lowerCAmelCase:	Union[str, Any]   				=      _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE			).groups()[0].split(", "			)
										__lowerCAmelCase:	int   				=      [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE			) > 0]
										objects.extend(__SCREAMING_SNAKE_CASE			)
								elif _re_between_brackets.search(__SCREAMING_SNAKE_CASE			) is not None:
										__lowerCAmelCase:	Tuple   				=      _re_between_brackets.search(__SCREAMING_SNAKE_CASE			).groups()[0].split(", "			)
										__lowerCAmelCase:	Tuple   				=      [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE			) > 0]
										objects.extend(__SCREAMING_SNAKE_CASE			)
								elif _re_quote_object.search(__SCREAMING_SNAKE_CASE			) is not None:
										objects.append(_re_quote_object.search(__SCREAMING_SNAKE_CASE			).groups()[0]			)
								elif line.startswith(" " * 8 + "\""			):
										objects.append(line[9:-3]			)
								elif line.startswith(" " * 1_2 + "\""			):
										objects.append(line[1_3:-3]			)
								line_index += 1

						__lowerCAmelCase:	Union[str, Any]   				=      objects
				else:
						line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
		__lowerCAmelCase:	str   				=      []
		while (
		    line_index < len(__SCREAMING_SNAKE_CASE			)
		    and find_backend(lines[line_index]			) is None
		    and not lines[line_index].startswith("else"			)
		):
				__lowerCAmelCase:	List[Any]   				=      lines[line_index]
				__lowerCAmelCase:	Tuple   				=      _re_import.search(__SCREAMING_SNAKE_CASE			)
				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:	Any   				=      {"none": objects}
		# Let's continue with backend-specific objects
		while line_index < len(__SCREAMING_SNAKE_CASE			):
				# If the line is an if is_backend_available, we grab all objects associated.
				__lowerCAmelCase:	Optional[Any]   				=      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:	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:	List[Any]   				=      []
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]			) <= 1 or lines[line_index].startswith(" " * 8			):
								__lowerCAmelCase:	Optional[int]   				=      lines[line_index]
								__lowerCAmelCase:	Any   				=      _re_import.search(__SCREAMING_SNAKE_CASE			)
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "			)			)
								elif line.startswith(" " * 1_2			):
										objects.append(line[1_2:-2]			)
								line_index += 1

						__lowerCAmelCase:	Union[str, Any]   				=      objects
				else:
						line_index += 1

		return import_dict_objects, type_hint_objects






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Tuple:

		def find_duplicates(__SCREAMING_SNAKE_CASE			):
				return [k for k, v in collections.Counter(__SCREAMING_SNAKE_CASE			).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[int]   				=      []
		for key in import_dict_objects.keys():
				__lowerCAmelCase:	Union[str, Any]   				=      find_duplicates(import_dict_objects[key]			)
				if duplicate_imports:
						errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}"			)
				__lowerCAmelCase:	Optional[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:	Union[str, Any]   				=      "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__   (   )     ->     Tuple:
		__lowerCAmelCase:	Optional[Any]   				=      []
		for root, _, files in os.walk(__SCREAMING_SNAKE_CASE			):
				if "__init__.py" in files:
						__lowerCAmelCase:	List[str]   				=      os.path.join(__SCREAMING_SNAKE_CASE ,			"__init__.py"			)
						__lowerCAmelCase:	int   				=      parse_init(__SCREAMING_SNAKE_CASE			)
						if objects is not None:
								__lowerCAmelCase:	Optional[Any]   				=      analyze_results(*__SCREAMING_SNAKE_CASE			)
								if len(__SCREAMING_SNAKE_CASE			) > 0:
										__lowerCAmelCase:	Union[str, Any]   				=      F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
										failures.append("\n".join(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) > 0:
				raise ValueError("\n\n".join(__SCREAMING_SNAKE_CASE			)			)






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


__A						       =      [
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
]






def    a__   (   )     ->     Optional[int]:
		# This is to make sure the transformers module imported is the one in the repo.
		__lowerCAmelCase:	Optional[Any]   				=      importlib.util.spec_from_file_location(
		    "transformers" ,			os.path.join(__SCREAMING_SNAKE_CASE ,			"__init__.py"			) ,			submodule_search_locations=[PATH_TO_TRANSFORMERS] ,			)
		__lowerCAmelCase:	Optional[int]   				=      spec.loader.load_module()

		__lowerCAmelCase:	str   				=      [
		    module
		    for module in get_transformers_submodules()
		    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
		]
		if len(__SCREAMING_SNAKE_CASE			) > 0:
				__lowerCAmelCase:	Optional[int]   				=      "\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()


