


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
_lowerCamelCase      			=						'src/diffusers'
_lowerCamelCase      			=						'.'


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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						Optional[Any]				)  ->							Tuple:
    return line.startswith(__UpperCamelCase				) or len(__UpperCamelCase				) <= 1 or re.search(R'''^\s*\)(\s*->.*:|:)\s*$'''	,					__UpperCamelCase				) is not None




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str				)  ->							List[str]:
    UpperCAmelCase_										=					object_name.split('''.'''				)
    UpperCAmelCase_										=					0

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

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

    # Now let's find the class / func in the code!
    UpperCAmelCase_										=					''''''
    UpperCAmelCase_										=					0
    for name in parts[i + 1 :]:
        while (
            line_index < len(__UpperCamelCase				) 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(__UpperCamelCase				):
        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).
    UpperCAmelCase_										=					line_index
    while line_index < len(__UpperCamelCase				) and _should_continue(lines[line_index]	,					__UpperCamelCase				):
        line_index += 1
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]				) <= 1:
        line_index -= 1

    UpperCAmelCase_										=					lines[start_index:line_index]
    return "".join(__UpperCamelCase				)


_lowerCamelCase      			=						re.compile(r'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
_lowerCamelCase      			=						re.compile(r'^\s*(\S+)->(\S+)(\s+.*|$)')
_lowerCamelCase      			=						re.compile(r'<FILL\s+[^>]*>')




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]				)  ->							Optional[int]:
    UpperCAmelCase_										=					code.split('''\n'''				)
    UpperCAmelCase_										=					0
    while idx < len(__UpperCamelCase				) and len(lines[idx]				) == 0:
        idx += 1
    if idx < len(__UpperCamelCase				):
        return re.search(R'''^(\s*)\S'''	,					lines[idx]				).groups()[0]
    return ""




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					len(get_indent(__UpperCamelCase				)				) > 0
    if has_indent:
        UpperCAmelCase_										=					f'class Bla:\n{code}'
    UpperCAmelCase_										=					black.Mode(target_versions={black.TargetVersion.PYaa}	,					line_length=119	,					preview=__UpperCamelCase				)
    UpperCAmelCase_										=					black.format_str(__UpperCamelCase	,					mode=__UpperCamelCase				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					style_docstrings_in_code(__UpperCamelCase				)
    return result[len('''class Bla:\n'''				) :] if has_indent else result




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						List[Any]=False				)  ->							List[str]:
    with open(__UpperCamelCase	,					'''r'''	,					encoding='''utf-8'''	,					newline='''\n'''				) as f:
        UpperCAmelCase_										=					f.readlines()
    UpperCAmelCase_										=					[]
    UpperCAmelCase_										=					0
    # Not a for loop cause `lines` is going to change (if `overwrite=True`).
    while line_index < len(__UpperCamelCase				):
        UpperCAmelCase_										=					_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.
        UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					search.groups()
        UpperCAmelCase_										=					find_code_in_diffusers(__UpperCamelCase				)
        UpperCAmelCase_										=					get_indent(__UpperCamelCase				)

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

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

        UpperCAmelCase_										=					lines[start_index:line_index]
        UpperCAmelCase_										=					''''''.join(__UpperCamelCase				)

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

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

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

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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						bool = False				)  ->							Optional[Any]:
    UpperCAmelCase_										=					glob.glob(os.path.join(__UpperCamelCase	,					'''**/*.py'''				)	,					recursive=__UpperCamelCase				)
    UpperCAmelCase_										=					[]
    for filename in all_files:
        UpperCAmelCase_										=					is_copy_consistent(__UpperCamelCase	,					__UpperCamelCase				)
        diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
    if not overwrite and len(__UpperCamelCase				) > 0:
        UpperCAmelCase_										=					'''\n'''.join(__UpperCamelCase				)
        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__":
 _lowerCamelCase      			=						argparse.ArgumentParser()
 parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
 _lowerCamelCase      			=						parser.parse_args()

 check_copies(args.fix_and_overwrite)











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 MobileNetVaImageProcessor



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      Tuple ,   __snake_case       :      int ,   __snake_case       :      List[Any]=7 ,   __snake_case       :      Any=3 ,   __snake_case       :      Any=18 ,   __snake_case       :      str=30 ,   __snake_case       :      Any=4_00 ,   __snake_case       :      Optional[int]=True ,   __snake_case       :      str=None ,   __snake_case       :      Any=True ,   __snake_case       :      List[Any]=None ,   ):
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 20}
         UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					num_channels
         UpperCAmelCase_										=					image_size
         UpperCAmelCase_										=					min_resolution
         UpperCAmelCase_										=					max_resolution
         UpperCAmelCase_										=					do_resize
         UpperCAmelCase_										=					size
         UpperCAmelCase_										=					do_center_crop
         UpperCAmelCase_										=					crop_size




     def   lowerCamelCase_       (     self       :      Any    ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }



@require_torch
@require_vision
class       a					(       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  MobileNetVaImageProcessor if is_vision_available() else None
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					MobileNetVaImageProcessingTester(self    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         return self.image_processor_tester.prepare_image_processor_dict()
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					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 ,   '''crop_size'''    )    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 20}    )
         self.assertEqual(image_processor.crop_size ,   {'''height''': 18, '''width''': 18}    )

         UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict ,   size=42 ,   crop_size=84    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 42}    )
         self.assertEqual(image_processor.crop_size ,   {'''height''': 84, '''width''': 84}    )
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         pass
     def   lowerCamelCase_       (     self       :      Tuple    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      str    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      int    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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'''],
             ) ,   )





