





"""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 (     a_  :List[str])						->						List[Any]:
    if _re_test_backend.search(_A) is None:
        return None
    __a	:  Dict         =					[b[0] for b in _re_backend.findall(_A)]
    backends.sort()
    return "_and_".join(_A)

def       __A (     a_  :Optional[int])						->						str:
    with open(_A				,    '''r'''				,    encoding='''utf-8'''				,    newline='''\n''') as f:
        __a	:  Optional[int]         =					f.readlines()

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

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

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

    __a	:  List[str]         =					{'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	:  Dict         =					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	:  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

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

            __a	:  Dict         =					objects
        else:
            line_index += 1

        # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
    __a	:  Optional[Any]         =					[]
    while (
        line_index < len(_A)
        and find_backend(lines[line_index]) is None
        and not lines[line_index].startswith('''else''')
    ):
        __a	:  Any         =					lines[line_index]
        __a	:  Dict         =					_re_import.search(_A)
        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	:  List[str]         =					{'none': objects}
    # Let's continue with backend-specific objects
    while line_index < len(_A):
        # If the line is an if is_backend_available, we grab all objects associated.
        __a	:  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:
            __a	:  Optional[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

            __a	:  Dict         =					[]
            # Until we unindent, add backend objects to the list
            while len(lines[line_index]) <= 1 or lines[line_index].startswith(''' ''' * 8):
                __a	:  List[Any]         =					lines[line_index]
                __a	:  int         =					_re_import.search(_A)
                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	:  Tuple         =					objects
        else:
            line_index += 1

    return import_dict_objects, type_hint_objects

def       __A (     a_  :List[Any]				,    a_  :Tuple)						->						int:

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

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


A	     =					[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]

def       __A (     )						->						str:
    # This is to make sure the transformers module imported is the one in the repo.
    __a	:  Dict         =					importlib.util.spec_from_file_location(
        '''transformers'''				,    os.path.join(_A				,    '''__init__.py''')				,    submodule_search_locations=[PATH_TO_TRANSFORMERS]				,    )
    __a	:  Union[str, Any]         =					spec.loader.load_module()

    __a	:  int         =					[
        module
        for module in get_transformers_submodules()
        if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
    ]
    if len(_A) > 0:
        __a	:  Optional[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

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 ViTImageProcessor




class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=13		,     _UpperCAmelCase=3		,     _UpperCAmelCase=224		,     _UpperCAmelCase=30		,     _UpperCAmelCase=400		,     _UpperCAmelCase=True		,     _UpperCAmelCase=None		,     _UpperCAmelCase=True		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     ):
									__a	:  int         =					size if size is not None else {'''height''': 18, '''width''': 18}
									__a	:  List[Any]         =					parent
									__a	:  Dict         =					batch_size
									__a	:  Dict         =					num_channels
									__a	:  int         =					image_size
									__a	:  Optional[Any]         =					min_resolution
									__a	:  Optional[int]         =					max_resolution
									__a	:  Dict         =					do_resize
									__a	:  List[Any]         =					size
									__a	:  int         =					do_normalize
									__a	:  Optional[Any]         =					image_mean
									__a	:  int         =					image_std







					def 	_lowerCamelCase						(  self       ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}






@require_torch
@require_vision
class 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					ViTImageProcessor if is_vision_available() else None







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					EfficientFormerImageProcessorTester(self       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return self.image_proc_tester.prepare_image_processor_dict()







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					self.image_processing_class(**self.image_processor_dict       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''image_mean'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''image_std'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''do_normalize'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''do_resize'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''size'''       )       )







					def 	_lowerCamelCase						(  self       ):
									pass







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  Any         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PIL images
									__a	:  int         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     Image.Image       )

									# Test not batched input
									__a	:  Dict         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Any         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  Tuple         =					self.image_processing_class(**self.image_processor_dict       )
									# create random numpy tensors
									__a	:  int         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase		,     numpify=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     np.ndarray       )

									# Test not batched input
									__a	:  Optional[int]         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  str         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  int         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PyTorch tensors
									__a	:  List[Any]         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase		,     torchify=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     torch.Tensor       )

									# Test not batched input
									__a	:  str         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Tuple         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )