





"""simple docstring"""






import argparse
from collections import defaultdict

import yaml


lowerCAmelCase_			=							'''docs/source/en/_toctree.yml'''

def       __lowerCamelCase     (       SCREAMING_SNAKE_CASE							)		->       Optional[int]:






       """simple docstring"""




       _UpperCAmelCase     =     defaultdict(lowerCAmelCase__							)
       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     []
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']}							)
              else:
                     new_doc_list.append(lowerCAmelCase__							)

       _UpperCAmelCase     =     new_doc_list
       _UpperCAmelCase     =     [key for key, value in counts.items() if value > 1]

       _UpperCAmelCase     =     []
       for duplicate_key in duplicates:
              _UpperCAmelCase     =     list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}							)
              if len(lowerCAmelCase__							) > 1:
                     raise ValueError(
                         F"""{duplicate_key} is present several times in the documentation table of content at """
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'							)
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}							)

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]							)
       _UpperCAmelCase     =     sorted(lowerCAmelCase__,key=lambda SCREAMING_SNAKE_CASE							: s["title"].lower()							)

       # "overview" gets special treatment and is always first
       if len(lowerCAmelCase__							) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'							)

       overview_doc.extend(lowerCAmelCase__							)

       # Sort
       return overview_doc

def       __lowerCamelCase     (       SCREAMING_SNAKE_CASE=False							)		->       Optional[Any]:






       """simple docstring"""




       with open(lowerCAmelCase__,encoding='utf-8'							) as f:
              _UpperCAmelCase     =     yaml.safe_load(f.read()							)

       # Get to the API doc
       _UpperCAmelCase     =     0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _UpperCAmelCase     =     content[api_idx]['sections']

       # Then to the model doc
       _UpperCAmelCase     =     0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       _UpperCAmelCase     =     api_doc[scheduler_idx]['sections']
       _UpperCAmelCase     =     clean_doc_toc(lowerCAmelCase__							)

       _UpperCAmelCase     =     False
       if new_scheduler_doc != scheduler_doc:
              _UpperCAmelCase     =     True
              if overwrite:
                     _UpperCAmelCase     =     new_scheduler_doc

       if diff:
              if overwrite:
                     _UpperCAmelCase     =     api_doc
                     with open(lowerCAmelCase__,'w',encoding='utf-8'							) as f:
                            f.write(yaml.dump(lowerCAmelCase__,allow_unicode=lowerCAmelCase__							)							)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)

def       __lowerCamelCase     (       SCREAMING_SNAKE_CASE=False							)		->       Tuple:






       """simple docstring"""




       with open(lowerCAmelCase__,encoding='utf-8'							) as f:
              _UpperCAmelCase     =     yaml.safe_load(f.read()							)

       # Get to the API doc
       _UpperCAmelCase     =     0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _UpperCAmelCase     =     content[api_idx]['sections']

       # Then to the model doc
       _UpperCAmelCase     =     0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       _UpperCAmelCase     =     False
       _UpperCAmelCase     =     api_doc[pipeline_idx]['sections']
       _UpperCAmelCase     =     []

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     _UpperCAmelCase     =     pipeline_doc['section']
                     _UpperCAmelCase     =     clean_doc_toc(lowerCAmelCase__							)
                     if overwrite:
                            _UpperCAmelCase     =     new_sub_pipeline_doc
              new_pipeline_docs.append(lowerCAmelCase__							)

       # sort overall pipeline doc
       _UpperCAmelCase     =     clean_doc_toc(lowerCAmelCase__							)

       if new_pipeline_docs != pipeline_docs:
              _UpperCAmelCase     =     True
              if overwrite:
                     _UpperCAmelCase     =     new_pipeline_docs

       if diff:
              if overwrite:
                     _UpperCAmelCase     =     api_doc
                     with open(lowerCAmelCase__,'w',encoding='utf-8'							) as f:
                            f.write(yaml.dump(lowerCAmelCase__,allow_unicode=lowerCAmelCase__							)							)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)


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_scheduler_doc(args.fix_and_overwrite)
    check_pipeline_doc(args.fix_and_overwrite)













"""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


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ChineseCLIPImageProcessor



class 	lowerCAmelCase					(      unittest.TestCase							):






       def __init__(   self    ,  a__    ,  a__=7    ,  a__=3    ,  a__=18    ,  a__=30    ,  a__=4_00    ,  a__=True    ,  a__=None    ,  a__=True    ,  a__=None    ,  a__=True    ,  a__=[0.48_145_466, 0.4_578_275, 0.40_821_073]    ,  a__=[0.26_862_954, 0.26_130_258, 0.27_577_711]    ,  a__=True    ,  ):
              _UpperCAmelCase     =     size if size is not None else {'height': 2_24, 'width': 2_24}
              _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
              _UpperCAmelCase     =     do_normalize
              _UpperCAmelCase     =     image_mean
              _UpperCAmelCase     =     image_std
              _UpperCAmelCase     =     do_convert_rgb






       def 							__A (   self     ):
              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_convert_rgb": self.do_convert_rgb,
              }






       def 							__A (   self    ,  a__=False    ,  a__=False    ,  a__=False     ):
              assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

              if equal_resolution:
                     _UpperCAmelCase     =     []
                     for i in range(self.batch_size     ):
                            image_inputs.append(
                                np.random.randint(
                                    2_55    ,  size=(self.num_channels, self.max_resolution, self.max_resolution)    ,  dtype=np.uinta     )     )
              else:
                     _UpperCAmelCase     =     []
                     for i in range(self.batch_size     ):
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     np.random.choice(np.arange(self.min_resolution    ,  self.max_resolution     )    ,  2     )
                            image_inputs.append(np.random.randint(2_55    ,  size=(self.num_channels, width, height)    ,  dtype=np.uinta     )     )

              if not numpify and not torchify:
                     # PIL expects the channel dimension as last dimension
                     _UpperCAmelCase     =     [Image.fromarray(np.moveaxis(a__    ,  0    ,  -1     )     ) for x in image_inputs]

              if torchify:
                     _UpperCAmelCase     =     [torch.from_numpy(a__     ) for x in image_inputs]

              return image_inputs



@require_torch
@require_vision
class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  ChineseCLIPImageProcessor if is_vision_available() else None






       def 							__A (   self     ):
              _UpperCAmelCase     =     ChineseCLIPImageProcessingTester(self    ,  do_center_crop=a__     )






       @property
       def 							__A (   self     ):
              return self.image_processor_tester.prepare_image_processor_dict()






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              self.assertTrue(hasattr(a__    ,  'do_resize'     )     )
              self.assertTrue(hasattr(a__    ,  'size'     )     )
              self.assertTrue(hasattr(a__    ,  'do_center_crop'     )     )
              self.assertTrue(hasattr(a__    ,  'center_crop'     )     )
              self.assertTrue(hasattr(a__    ,  'do_normalize'     )     )
              self.assertTrue(hasattr(a__    ,  'image_mean'     )     )
              self.assertTrue(hasattr(a__    ,  'image_std'     )     )
              self.assertTrue(hasattr(a__    ,  'do_convert_rgb'     )     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.image_processing_class.from_dict(self.image_processor_dict     )
              self.assertEqual(image_processor.size    ,  {'height': 2_24, 'width': 2_24}     )
              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 							__A (   self     ):
              pass






       def 							__A (   self     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random PIL images
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  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(a__    ,  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     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random numpy tensors
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__    ,  numpify=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  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(a__    ,  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     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random PyTorch tensors
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__    ,  torchify=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  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(a__    ,  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'],
                  )    ,  )



@require_torch
@require_vision
class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  ChineseCLIPImageProcessor if is_vision_available() else None






       def 							__A (   self     ):
              _UpperCAmelCase     =     ChineseCLIPImageProcessingTester(self    ,  num_channels=4    ,  do_center_crop=a__     )
              _UpperCAmelCase     =     3






       @property
       def 							__A (   self     ):
              return self.image_processor_tester.prepare_image_processor_dict()






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              self.assertTrue(hasattr(a__    ,  'do_resize'     )     )
              self.assertTrue(hasattr(a__    ,  'size'     )     )
              self.assertTrue(hasattr(a__    ,  'do_center_crop'     )     )
              self.assertTrue(hasattr(a__    ,  'center_crop'     )     )
              self.assertTrue(hasattr(a__    ,  'do_normalize'     )     )
              self.assertTrue(hasattr(a__    ,  'image_mean'     )     )
              self.assertTrue(hasattr(a__    ,  'image_std'     )     )
              self.assertTrue(hasattr(a__    ,  'do_convert_rgb'     )     )






       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random PIL images
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  Image.Image     )

              # Test not batched input
              _UpperCAmelCase     =     image_processing(image_inputs[0]    ,  return_tensors='pt'     ).pixel_values
              self.assertEqual(
                  encoded_images.shape    ,  (
                      1,
                      self.expected_encoded_image_num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  )    ,  )

              # Test batched
              _UpperCAmelCase     =     image_processing(a__    ,  return_tensors='pt'     ).pixel_values
              self.assertEqual(
                  encoded_images.shape    ,  (
                      self.image_processor_tester.batch_size,
                      self.expected_encoded_image_num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  )    ,  )






