

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  __lowerCAmelCase     (					unittest.TestCase							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase=7							,   lowerCAmelCase=3							,   lowerCAmelCase=18							,   lowerCAmelCase=30							,   lowerCAmelCase=400							,   lowerCAmelCase=True							,   lowerCAmelCase=None							,   lowerCAmelCase=True							,   lowerCAmelCase=None							,   lowerCAmelCase=True							,   lowerCAmelCase=[0.5, 0.5, 0.5]							,   lowerCAmelCase=[0.5, 0.5, 0.5]							,   lowerCAmelCase=False							,   )  -> Optional[int]:
        '''simple docstring'''






        _lowercase	       =size if size is not None else {'height': 20, 'width': 20}
        _lowercase	       =crop_size if crop_size is not None else {'height': 18, 'width': 18}
        _lowercase	       =parent
        _lowercase	       =batch_size
        _lowercase	       =num_channels
        _lowercase	       =image_size
        _lowercase	       =min_resolution
        _lowercase	       =max_resolution
        _lowercase	       =do_resize
        _lowercase	       =size
        _lowercase	       =do_center_crop
        _lowercase	       =crop_size
        _lowercase	       =do_normalize
        _lowercase	       =image_mean
        _lowercase	       =image_std
        _lowercase	       =do_reduce_labels




  def   A__     (				self					)  -> Optional[Any]:
        '''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      (			)    ->      Tuple:





      """simple docstring"""



      _lowercase	       =load_dataset('hf-internal-testing/fixtures_ade20k'      ,   split='test'     )

      _lowercase	       =Image.open(dataset[0]['file']     )
      _lowercase	       =Image.open(dataset[1]['file']     )

      return image, map
def      a      (			)    ->      List[str]:





      """simple docstring"""



      _lowercase	       =load_dataset('hf-internal-testing/fixtures_ade20k'      ,   split='test'     )

      _lowercase	       =Image.open(ds[0]['file']     )
      _lowercase	       =Image.open(ds[1]['file']     )
      _lowercase	       =Image.open(ds[2]['file']     )
      _lowercase	       =Image.open(ds[3]['file']     )

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







@require_torch
@require_vision
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE			,			unittest.TestCase							):
  _a									=  BeitImageProcessor if is_vision_available() else None




  def   A__     (				self					)  -> str:
        '''simple docstring'''






        _lowercase	       =BeitImageProcessingTester(self					)




  @property
  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()




  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(lowerCAmelCase							,   'do_resize'					)					)
        self.assertTrue(hasattr(lowerCAmelCase							,   'size'					)					)
        self.assertTrue(hasattr(lowerCAmelCase							,   'do_center_crop'					)					)
        self.assertTrue(hasattr(lowerCAmelCase							,   'center_crop'					)					)
        self.assertTrue(hasattr(lowerCAmelCase							,   'do_normalize'					)					)
        self.assertTrue(hasattr(lowerCAmelCase							,   'image_mean'					)					)
        self.assertTrue(hasattr(lowerCAmelCase							,   'image_std'					)					)




  def   A__     (				self					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       =self.image_processing_class.from_dict(self.image_processor_dict					)
        self.assertEqual(image_processor.size							,   {'height': 20, 'width': 20}					)
        self.assertEqual(image_processor.crop_size							,   {'height': 18, 'width': 18}					)
        self.assertEqual(image_processor.do_reduce_labels							,   lowerCAmelCase					)

        _lowercase	       =self.image_processing_class.from_dict(
            self.image_processor_dict							,   size=42							,   crop_size=84							,   reduce_labels=lowerCAmelCase					)
        self.assertEqual(image_processor.size							,   {'height': 42, 'width': 42}					)
        self.assertEqual(image_processor.crop_size							,   {'height': 84, 'width': 84}					)
        self.assertEqual(image_processor.do_reduce_labels							,   lowerCAmelCase					)




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        pass




  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =self.image_processing_class(**self.image_processor_dict					)
        # create random PIL images
        _lowercase	       =prepare_image_inputs(self.image_processor_tester							,   equal_resolution=lowerCAmelCase					)
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,   Image.Image					)

        # Test not batched input
        _lowercase	       =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
        _lowercase	       =image_processing(lowerCAmelCase							,   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   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        _lowercase	       =prepare_image_inputs(self.image_processor_tester							,   equal_resolution=lowerCAmelCase							,   numpify=lowerCAmelCase					)
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,   np.ndarray					)

        # Test not batched input
        _lowercase	       =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
        _lowercase	       =image_processing(lowerCAmelCase							,   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   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






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

        # Test not batched input
        _lowercase	       =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
        _lowercase	       =image_processing(lowerCAmelCase							,   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   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






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

        # Test not batched input
        _lowercase	       =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() <= 255					)

        # Test batched
        _lowercase	       =image_processing(lowerCAmelCase							,   lowerCAmelCase							,   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() <= 255					)

        # Test not batched input (PIL images)
        _lowercase       ,       _lowercase	       =prepare_semantic_single_inputs()

        _lowercase	       =image_processing(lowerCAmelCase							,   lowerCAmelCase							,   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() <= 255					)

        # Test batched input (PIL images)
        _lowercase       ,       _lowercase	       =prepare_semantic_batch_inputs()

        _lowercase	       =image_processing(lowerCAmelCase							,   lowerCAmelCase							,   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() <= 255					)




  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =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
        _lowercase       ,       _lowercase	       =prepare_semantic_single_inputs()
        _lowercase	       =image_processing(lowerCAmelCase							,   lowerCAmelCase							,   return_tensors='pt'					)
        self.assertTrue(encoding['labels'].min().item() >= 0					)
        self.assertTrue(encoding['labels'].max().item() <= 150					)

        _lowercase	       =True
        _lowercase	       =image_processing(lowerCAmelCase							,   lowerCAmelCase							,   return_tensors='pt'					)
        self.assertTrue(encoding['labels'].min().item() >= 0					)
        self.assertTrue(encoding['labels'].max().item() <= 255					)





import argparse
import os
import re


lowercase_									=   'src/transformers'

# Pattern that looks at the indentation in a line.
lowercase_									=   re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase_									=   re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowercase_									=   re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
lowercase_									=   re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowercase_									=   re.compile(R'\[([^\]]+)\]')
def      a      (			A__	:   Dict     )    ->      Optional[Any]:





      """simple docstring"""



      _lowercase	       =_re_indent.search(A__     )
      return "" if search is None else search.groups()[0]
def      a      (			A__	:   Optional[Any]      ,   A__	:   Dict=""      ,   A__	:   Union[str, Any]=None      ,   A__	:   Tuple=None     )    ->      Dict:





      """simple docstring"""



      _lowercase	       =0
      _lowercase	       =code.split('\n'     )
      if start_prompt is not None:
            while not lines[index].startswith(A__     ):
                  index += 1
            _lowercase	       =['\n'.join(lines[:index]     )]
      else:
            _lowercase	       =[]

      # We split into blocks until we get to the `end_prompt` (or the end of the block).
      _lowercase	       =[lines[index]]
      index += 1
      while index < len(A__     ) and (end_prompt is None or not lines[index].startswith(A__     )):
            if len(lines[index]     ) > 0 and get_indent(lines[index]     ) == indent_level:
                  if len(A__     ) > 0 and get_indent(current_block[-1]     ).startswith(indent_level + ' '     ):
                        current_block.append(lines[index]     )
                        blocks.append('\n'.join(A__     )     )
                        if index < len(A__     ) - 1:
                              _lowercase	       =[lines[index + 1]]
                              index += 1
                        else:
                              _lowercase	       =[]
                  else:
                        blocks.append('\n'.join(A__     )     )
                        _lowercase	       =[lines[index]]
            else:
                  current_block.append(lines[index]     )
            index += 1

      # Adds current block if it's nonempty.
      if len(A__     ) > 0:
            blocks.append('\n'.join(A__     )     )

      # Add final block after end_prompt if provided.
      if end_prompt is not None and index < len(A__     ):
            blocks.append('\n'.join(lines[index:]     )     )

      return blocks
def      a      (			A__	:   int     )    ->      Union[str, Any]:





      """simple docstring"""



      def _inner(A__	:   Any     ):
            return key(A__     ).lower().replace('_'      ,   ''     )

      return _inner
def      a      (			A__	:   Any      ,   A__	:   Union[str, Any]=None     )    ->      int:





      """simple docstring"""



      def noop(A__	:   Optional[int]     ):
            return x

      if key is None:
            _lowercase	       =noop
      # Constants are all uppercase, they go first.
      _lowercase	       =[obj for obj in objects if key(A__     ).isupper()]
      # Classes are not all uppercase but start with a capital, they go second.
      _lowercase	       =[obj for obj in objects if key(A__     )[0].isupper() and not key(A__     ).isupper()]
      # Functions begin with a lowercase, they go last.
      _lowercase	       =[obj for obj in objects if not key(A__     )[0].isupper()]

      _lowercase	       =ignore_underscore(A__     )
      return sorted(A__      ,   key=A__     ) + sorted(A__      ,   key=A__     ) + sorted(A__      ,   key=A__     )
def      a      (			A__	:   Union[str, Any]     )    ->      Tuple:





      """simple docstring"""



      def _replace(A__	:   Optional[int]     ):
            _lowercase	       =match.groups()[0]
            if "," not in imports:
                  return F'''[{imports}]'''
            _lowercase	       =[part.strip().replace('"'      ,   ''     ) for part in imports.split(','     )]
            # We will have a final empty element if the line finished with a comma.
            if len(keys[-1]     ) == 0:
                  _lowercase	       =keys[:-1]
            return "[" + ", ".join([F'''"{k}"''' for k in sort_objects(A__     )]     ) + "]"

      _lowercase	       =import_statement.split('\n'     )
      if len(A__     ) > 3:
            # Here we have to sort internal imports that are on several lines (one per name):
            # key: [
            #     "object1",
            #     "object2",
            #     ...
            # ]

            # We may have to ignore one or two lines on each side.
            _lowercase	       =2 if lines[1].strip() == '[' else 1
            _lowercase	       =[(i, _re_strip_line.search(A__     ).groups()[0]) for i, line in enumerate(lines[idx:-idx]     )]
            _lowercase	       =sort_objects(A__      ,   key=lambda A__     : x[1]     )
            _lowercase	       =[lines[x[0] + idx] for x in sorted_indices]
            return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]     )
      elif len(A__     ) == 3:
            # Here we have to sort internal imports that are on one separate line:
            # key: [
            #     "object1", "object2", ...
            # ]
            if _re_bracket_content.search(lines[1]     ) is not None:
                  _lowercase	       =_re_bracket_content.sub(_replace      ,   lines[1]     )
            else:
                  _lowercase	       =[part.strip().replace('"'      ,   ''     ) for part in lines[1].split(','     )]
                  # We will have a final empty element if the line finished with a comma.
                  if len(keys[-1]     ) == 0:
                        _lowercase	       =keys[:-1]
                  _lowercase	       =get_indent(lines[1]     ) + ', '.join([F'''"{k}"''' for k in sort_objects(A__     )]     )
            return "\n".join(A__     )
      else:
            # Finally we have to deal with imports fitting on one line
            _lowercase	       =_re_bracket_content.sub(_replace      ,   A__     )
            return import_statement
def      a      (			A__	:   Dict      ,   A__	:   int=True     )    ->      Optional[Any]:





      """simple docstring"""



      with open(A__      ,   encoding='utf-8'     ) as f:
            _lowercase	       =f.read()

      if "_import_structure" not in code:
            return

      # Blocks of indent level 0
      _lowercase	       =split_code_in_indented_blocks(
          A__      ,   start_prompt='_import_structure = {'      ,   end_prompt='if TYPE_CHECKING:'     )

      # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
      for block_idx in range(1      ,   len(A__     ) - 1     ):
            # Check if the block contains some `_import_structure`s thingy to sort.
            _lowercase	       =main_blocks[block_idx]
            _lowercase	       =block.split('\n'     )

            # Get to the start of the imports.
            _lowercase	       =0
            while line_idx < len(A__     ) and "_import_structure" not in block_lines[line_idx]:
                  # Skip dummy import blocks
                  if "import dummy" in block_lines[line_idx]:
                        _lowercase	       =len(A__     )
                  else:
                        line_idx += 1
            if line_idx >= len(A__     ):
                  continue

            # Ignore beginning and last line: they don't contain anything.
            _lowercase	       ='\n'.join(block_lines[line_idx:-1]     )
            _lowercase	       =get_indent(block_lines[1]     )
            # Slit the internal block into blocks of indent level 1.
            _lowercase	       =split_code_in_indented_blocks(A__      ,   indent_level=A__     )
            # We have two categories of import key: list or _import_structure[key].append/extend
            _lowercase	       =_re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
            # Grab the keys, but there is a trap: some lines are empty or just comments.
            _lowercase	       =[(pattern.search(A__     ).groups()[0] if pattern.search(A__     ) is not None else None) for b in internal_blocks]
            # We only sort the lines with a key.
            _lowercase	       =[(i, key) for i, key in enumerate(A__     ) if key is not None]
            _lowercase	       =[x[0] for x in sorted(A__      ,   key=lambda A__     : x[1]     )]

            # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
            _lowercase	       =0
            _lowercase	       =[]
            for i in range(len(A__     )     ):
                  if keys[i] is None:
                        reorderded_blocks.append(internal_blocks[i]     )
                  else:
                        _lowercase	       =sort_objects_in_import(internal_blocks[sorted_indices[count]]     )
                        reorderded_blocks.append(A__     )
                        count += 1

        # And we put our main block back together with its first and last line.
            _lowercase	       ='\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]     )

      if code != "\n".join(A__     ):
            if check_only:
                  return True
            else:
                  print(F'''Overwriting {file}.'''     )
                  with open(A__      ,   'w'      ,   encoding='utf-8'     ) as f:
                        f.write('\n'.join(A__     )     )
def      a      (			A__	:   List[Any]=True     )    ->      List[str]:





      """simple docstring"""



      _lowercase	       =[]
      for root, _, files in os.walk(A__     ):
            if "__init__.py" in files:
                  _lowercase	       =sort_imports(os.path.join(A__      ,   '__init__.py'     )      ,   check_only=A__     )
                  if result:
                        _lowercase	       =[os.path.join(A__      ,   '__init__.py'     )]
      if len(A__     ) > 0:
            raise ValueError(F'''Would overwrite {len(A__     )} files, run `make style`.'''     )


if __name__ == "__main__":
  lowercase_									=   argparse.ArgumentParser()
  parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
  lowercase_									=   parser.parse_args()

  sort_imports_in_all_inits(check_only=args.check_only)


