import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase:	Union[str, Any]       			=		'''src/diffusers'''
_lowercase:	Dict       			=		'''.'''


# This is to make sure the diffusers module imported is the one in the repo.
_lowercase:	str       			=		importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
_lowercase:	Dict       			=		spec.loader.load_module()






def _lowerCamelCase  (		snake_case     , snake_case  ):
							return line.startswith(SCREAMING_SNAKE_CASE_  ) or len(SCREAMING_SNAKE_CASE_  ) <= 1 or re.search(R'^\s*\)(\s*->.*:|:)\s*$'     , SCREAMING_SNAKE_CASE_  ) is not None






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							object_name.split('.'  )
							_lowerCAmelCase							=							0

							# First let's find the module where our object lives.
							_lowerCAmelCase							=							parts[i]
							while i < len(SCREAMING_SNAKE_CASE_  ) and not os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE_     , F'{module}.py'  )  ):
														i += 1
														if i < len(SCREAMING_SNAKE_CASE_  ):
																					_lowerCAmelCase							=							os.path.join(SCREAMING_SNAKE_CASE_     , parts[i]  )
							if i >= len(SCREAMING_SNAKE_CASE_  ):
														raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'  )

							with open(os.path.join(SCREAMING_SNAKE_CASE_     , F'{module}.py'  )     , 'r'     , encoding='utf-8'     , newline='\n'  ) as f:
														_lowerCAmelCase							=							f.readlines()

							# Now let's find the class / func in the code!
							_lowerCAmelCase							=							''
							_lowerCAmelCase							=							0
							for name in parts[i + 1 :]:
														while (
														    line_index < len(SCREAMING_SNAKE_CASE_  ) and re.search(RF'^{indent}(class|def)\s+{name}(\(|\:)'     , lines[line_index]  ) is None
														):
																					line_index += 1
														indent += "    "
														line_index += 1

							if line_index >= len(SCREAMING_SNAKE_CASE_  ):
														raise ValueError(F' {object_name} does not match any function or class in {module}.'  )

							# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
							_lowerCAmelCase							=							line_index
							while line_index < len(SCREAMING_SNAKE_CASE_  ) and _should_continue(lines[line_index]     , SCREAMING_SNAKE_CASE_  ):
														line_index += 1
							# Clean up empty lines at the end (if any).
							while len(lines[line_index - 1]  ) <= 1:
														line_index -= 1

							_lowerCAmelCase							=							lines[start_index:line_index]
							return "".join(SCREAMING_SNAKE_CASE_  )


_lowercase:	str       			=		re.compile(R'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
_lowercase:	Union[str, Any]       			=		re.compile(R'''^\s*(\S+)->(\S+)(\s+.*|$)''')
_lowercase:	str       			=		re.compile(R'''<FILL\s+[^>]*>''')






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							code.split('\n'  )
							_lowerCAmelCase							=							0
							while idx < len(SCREAMING_SNAKE_CASE_  ) and len(lines[idx]  ) == 0:
														idx += 1
							if idx < len(SCREAMING_SNAKE_CASE_  ):
														return re.search(R'^(\s*)\S'     , lines[idx]  ).groups()[0]
							return ""






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							len(get_indent(SCREAMING_SNAKE_CASE_  )  ) > 0
							if has_indent:
														_lowerCAmelCase							=							F'class Bla:\n{code}'
							_lowerCAmelCase							=							black.Mode(target_versions={black.TargetVersion.PYaa}     , line_length=119     , preview=SCREAMING_SNAKE_CASE_  )
							_lowerCAmelCase							=							black.format_str(SCREAMING_SNAKE_CASE_     , mode=SCREAMING_SNAKE_CASE_  )
							_lowerCAmelCase			,		_lowerCAmelCase							=							style_docstrings_in_code(SCREAMING_SNAKE_CASE_  )
							return result[len('class Bla:\n'  ) :] if has_indent else result






def _lowerCamelCase  (		snake_case     , snake_case=False  ):







							with open(SCREAMING_SNAKE_CASE_     , 'r'     , encoding='utf-8'     , newline='\n'  ) as f:
														_lowerCAmelCase							=							f.readlines()
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							0
							# Not a for loop cause `lines` is going to change (if `overwrite=True`).
							while line_index < len(SCREAMING_SNAKE_CASE_  ):
														_lowerCAmelCase							=							_re_copy_warning.search(lines[line_index]  )
														if search is None:
																					line_index += 1
																					continue

														# There is some copied code here, let's retrieve the original.
														_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase							=							search.groups()
														_lowerCAmelCase							=							find_code_in_diffusers(SCREAMING_SNAKE_CASE_  )
														_lowerCAmelCase							=							get_indent(SCREAMING_SNAKE_CASE_  )

														_lowerCAmelCase							=							line_index + 1 if indent == theoretical_indent else line_index + 2
														_lowerCAmelCase							=							theoretical_indent
														_lowerCAmelCase							=							start_index

														# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
														_lowerCAmelCase							=							True
														while line_index < len(SCREAMING_SNAKE_CASE_  ) and should_continue:
																					line_index += 1
																					if line_index >= len(SCREAMING_SNAKE_CASE_  ):
																												break
																					_lowerCAmelCase							=							lines[line_index]
																					_lowerCAmelCase							=							_should_continue(SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_  ) and re.search(F'^{indent}# End copy'     , SCREAMING_SNAKE_CASE_  ) is None
														# Clean up empty lines at the end (if any).
														while len(lines[line_index - 1]  ) <= 1:
																					line_index -= 1

														_lowerCAmelCase							=							lines[start_index:line_index]
														_lowerCAmelCase							=							''.join(SCREAMING_SNAKE_CASE_  )

														# Remove any nested `Copied from` comments to avoid circular copies
														_lowerCAmelCase							=							[line for line in theoretical_code.split('\n'  ) if _re_copy_warning.search(SCREAMING_SNAKE_CASE_  ) is None]
														_lowerCAmelCase							=							'\n'.join(SCREAMING_SNAKE_CASE_  )

														# Before comparing, use the `replace_pattern` on the original code.
														if len(SCREAMING_SNAKE_CASE_  ) > 0:
																					_lowerCAmelCase							=							replace_pattern.replace('with'     , ''  ).split(','  )
																					_lowerCAmelCase							=							[_re_replace_pattern.search(SCREAMING_SNAKE_CASE_  ) for p in patterns]
																					for pattern in patterns:
																												if pattern is None:
																																			continue
																												_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase							=							pattern.groups()
																												_lowerCAmelCase							=							re.sub(SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_  )
																												if option.strip() == "all-casing":
																																			_lowerCAmelCase							=							re.sub(obja.lower()     , obja.lower()     , SCREAMING_SNAKE_CASE_  )
																																			_lowerCAmelCase							=							re.sub(obja.upper()     , obja.upper()     , SCREAMING_SNAKE_CASE_  )

																												# Blackify after replacement. To be able to do that, we need the header (class or function definition)
																												# from the previous line
																					_lowerCAmelCase							=							blackify(lines[start_index - 1] + theoretical_code  )
																					_lowerCAmelCase							=							theoretical_code[len(lines[start_index - 1]  ) :]

														# Test for a diff and act accordingly.
														if observed_code != theoretical_code:
																					diffs.append([object_name, start_index]  )
																					if overwrite:
																												_lowerCAmelCase							=							lines[:start_index] + [theoretical_code] + lines[line_index:]
																												_lowerCAmelCase							=							start_index + 1

							if overwrite and len(SCREAMING_SNAKE_CASE_  ) > 0:
														# Warn the user a file has been modified.
														print(F'Detected changes, rewriting {filename}.'  )
														with open(SCREAMING_SNAKE_CASE_     , 'w'     , encoding='utf-8'     , newline='\n'  ) as f:
																					f.writelines(SCREAMING_SNAKE_CASE_  )
							return diffs






def _lowerCamelCase  (		snake_case = False  ):
							_lowerCAmelCase							=							glob.glob(os.path.join(SCREAMING_SNAKE_CASE_     , '**/*.py'  )     , recursive=SCREAMING_SNAKE_CASE_  )
							_lowerCAmelCase							=							[]
							for filename in all_files:
														_lowerCAmelCase							=							is_copy_consistent(SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_  )
														diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
							if not overwrite and len(SCREAMING_SNAKE_CASE_  ) > 0:
														_lowerCAmelCase							=							'\n'.join(SCREAMING_SNAKE_CASE_  )
														raise Exception(
														    'Found the following copy inconsistencies:\n'
														    + diff
														    + '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'  )


if __name__ == "__main__":
				_lowercase:	Any       			=		argparse.ArgumentParser()
				parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
				_lowercase:	int       			=		parser.parse_args()

				check_copies(args.fix_and_overwrite)













from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  int							  =   ["""pixel_values"""]





     def __init__(     self				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = PILImageResampling.BICUBIC				,							_UpperCamelCase = True				,							_UpperCamelCase = True				,							_UpperCamelCase = 1 / 255				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        super().__init__(**_UpperCamelCase					)
        _UpperCAmelCase           =   size if size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        _UpperCAmelCase           =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							default_to_square=_UpperCamelCase				,							param_name='''crop_size'''					)

        _UpperCAmelCase           =   do_resize
        _UpperCAmelCase           =   do_rescale
        _UpperCAmelCase           =   do_normalize
        _UpperCAmelCase           =   do_center_crop
        _UpperCAmelCase           =   crop_size
        _UpperCAmelCase           =   size
        _UpperCAmelCase           =   resample
        _UpperCAmelCase           =   rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _UpperCAmelCase           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = PILImageResampling.BILINEAR				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if "shortest_edge" in size:
           _UpperCAmelCase           =   get_resize_output_image_size(_UpperCamelCase				,							size=size['''shortest_edge''']				,							default_to_square=_UpperCamelCase					)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
           _UpperCAmelCase           =   (size['''height'''], size['''width'''])
        else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''					)
        return resize(_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
        return center_crop(_UpperCamelCase				,							size=(size['''height'''], size['''width'''])				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase					):
        return rescale(_UpperCamelCase				,							scale=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        return normalize(_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = ChannelDimension.FIRST				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase           =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase           =   do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase           =   do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase           =   crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							param_name='''crop_size'''				,							default_to_square=_UpperCamelCase					)
        _UpperCAmelCase           =   resample if resample is not None else self.resample
        _UpperCAmelCase           =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase           =   image_std if image_std is not None else self.image_std

        _UpperCAmelCase           =   size if size is not None else self.size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if not is_batched(_UpperCamelCase					):
           _UpperCAmelCase           =   [images]

        if not valid_images(_UpperCamelCase					):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

        if do_resize and size is None:
           raise ValueError('''Size must be specified if do_resize is True.'''					)

        if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)

        if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

        # All transformations expect numpy arrays.
        _UpperCAmelCase           =   [to_numpy_array(_UpperCamelCase					) for image in images]

        if do_resize:
           _UpperCAmelCase           =   [self.resize(image=_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase					) for image in images]

        if do_center_crop:
           _UpperCAmelCase           =   [self.center_crop(image=_UpperCamelCase				,							size=_UpperCamelCase					) for image in images]

        if do_rescale:
           _UpperCAmelCase           =   [self.rescale(image=_UpperCamelCase				,							scale=_UpperCamelCase					) for image in images]

        if do_normalize:
           _UpperCAmelCase           =   [self.normalize(image=_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   [to_channel_dimension_format(_UpperCamelCase				,							_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   {'''pixel_values''': images}
        return BatchFeature(data=_UpperCamelCase				,							tensor_type=_UpperCamelCase					)