






"""simple docstring"""





import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
UpperCamelCase					=     """src/diffusers"""
UpperCamelCase					=     """docs/source/en"""
UpperCamelCase					=     """."""






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       with open(snake_case_		,  "r"		,  encoding="utf-8"		,  newline="\n"						) as f:
              lowercase__:							int         =     f.readlines()
       # Find the start prompt.
       lowercase__:							Union[str, Any]         =     0
       while not lines[start_index].startswith(snake_case_						):
              start_index += 1
       start_index += 1

       lowercase__:							Union[str, Any]         =     start_index
       while not lines[end_index].startswith(snake_case_						):
              end_index += 1
       end_index -= 1

       while len(lines[start_index]						) <= 1:
              start_index += 1
       while len(lines[end_index]						) <= 1:
              end_index -= 1
       end_index += 1
       return "".join(lines[start_index:end_index]						), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
UpperCamelCase					=     """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
UpperCamelCase					=     re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
UpperCamelCase					=     re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
UpperCamelCase					=     re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Union[str, Any]         =     re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,  snake_case_						)
       return [m.group(0						) for m in matches]






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							str         =     2 if text == "✅" or text == "❌" else len(snake_case_						)
       lowercase__:							Union[str, Any]         =     (width - text_length) // 2
       lowercase__:							int         =     width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent






def 							A(    ):
       """simple docstring"""





       lowercase__:							List[str]         =     diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       lowercase__:							Optional[Any]         =     {
           name: config_mapping_names[code]
           for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
           if code in config_mapping_names
       }
       lowercase__:							Optional[Any]         =     {name: config.replace("ConfigMixin"		,  ""						) for name, config in model_name_to_config.items()}

       # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
       lowercase__:							List[str]         =     collections.defaultdict(snake_case_						)
       lowercase__:							Union[str, Any]         =     collections.defaultdict(snake_case_						)
       lowercase__:							Optional[int]         =     collections.defaultdict(snake_case_						)
       lowercase__:							int         =     collections.defaultdict(snake_case_						)
       lowercase__:							int         =     collections.defaultdict(snake_case_						)

       # Let's lookup through all diffusers object (once).
       for attr_name in dir(snake_case_						):
              lowercase__:							Union[str, Any]         =     None
              if attr_name.endswith("Tokenizer"						):
                     lowercase__:							str         =     slow_tokenizers
                     lowercase__:							str         =     attr_name[:-9]
              elif attr_name.endswith("TokenizerFast"						):
                     lowercase__:							Union[str, Any]         =     fast_tokenizers
                     lowercase__:							Optional[int]         =     attr_name[:-13]
              elif _re_tf_models.match(snake_case_						) is not None:
                     lowercase__:							List[str]         =     tf_models
                     lowercase__:							List[Any]         =     _re_tf_models.match(snake_case_						).groups()[0]
              elif _re_flax_models.match(snake_case_						) is not None:
                     lowercase__:							Union[str, Any]         =     flax_models
                     lowercase__:							Optional[int]         =     _re_flax_models.match(snake_case_						).groups()[0]
              elif _re_pt_models.match(snake_case_						) is not None:
                     lowercase__:							Any         =     pt_models
                     lowercase__:							Optional[Any]         =     _re_pt_models.match(snake_case_						).groups()[0]

              if lookup_dict is not None:
                     while len(snake_case_						) > 0:
                            if attr_name in model_name_to_prefix.values():
                                   lowercase__:							Dict         =     True
                                   break
                            # Try again after removing the last word in the name
                            lowercase__:							List[str]         =     "".join(camel_case_split(snake_case_						)[:-1]						)

    # Let's build that table!
       lowercase__:							str         =     list(model_name_to_config.keys()						)
       model_names.sort(key=str.lower						)
       lowercase__:							Any         =     ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
       # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
       lowercase__:							List[str]         =     [len(snake_case_						) + 2 for c in columns]
       lowercase__:							Optional[int]         =     max([len(snake_case_						) for name in model_names]						) + 2

       # Build the table per se
       lowercase__:							List[Any]         =     "|" + "|".join([_center_text(snake_case_		,  snake_case_						) for c, w in zip(snake_case_		,  snake_case_						)]						) + "|\n"
       # Use ":-----:" format to center-aligned table cell texts
       table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]						) + "|\n"

       lowercase__:							Dict         =     {True: "✅", False: "❌"}
       for name in model_names:
              lowercase__:							str         =     model_name_to_prefix[name]
              lowercase__:							Union[str, Any]         =     [
                  name,
                  check[slow_tokenizers[prefix]],
                  check[fast_tokenizers[prefix]],
                  check[pt_models[prefix]],
                  check[tf_models[prefix]],
                  check[flax_models[prefix]],
              ]
              table += "|" + "|".join([_center_text(snake_case_		,  snake_case_						) for l, w in zip(snake_case_		,  snake_case_						)]						) + "|\n"
       return table






def 							A(    snake_case_=False						):
       """simple docstring"""





       lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__:							Any         =     _find_text_in_file(
           filename=os.path.join(snake_case_		,  "index.mdx"						)		,  start_prompt="<!--This table is updated automatically from the auto modules"		,  end_prompt="<!-- End table-->"		,  )
       lowercase__:							Any         =     get_model_table_from_auto_modules()

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(snake_case_		,  "index.mdx"						)		,  "w"		,  encoding="utf-8"		,  newline="\n"						) as f:
                            f.writelines(lines[:start_index] + [new_table] + lines[end_index:]						)
              else:
                     raise ValueError(
                         "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."						)


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

  check_model_table(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 TvltImageProcessor
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase=10 ,   UpperCAmelCase=10 ,   UpperCAmelCase=False ,   UpperCAmelCase=False     )						->Union[str, Any]:

   """simple docstring"""



   a_						   =     []
   for i in range(image_processor_tester.num_frames     ):
      video.append(np.random.randint(255 ,   size=(image_processor_tester.num_channels, width, height) ,   dtype=np.uinta     )     )

   if not numpify and not torchify:
      # PIL expects the channel dimension as last dimension
      a_						   =     [Image.fromarray(np.moveaxis(UpperCAmelCase ,   0 ,   -1     )     ) for frame in video]

   if torchify:
      a_						   =     [torch.from_numpy(UpperCAmelCase     ) for frame in video]

   return video
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase=False ,   UpperCAmelCase=False ,   UpperCAmelCase=False     )						->Optional[Any]:

   """simple docstring"""



   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   a_						   =     []
   for i in range(image_processor_tester.batch_size     ):
      if equal_resolution:
         a_						   =     a_						   =     image_processor_tester.max_resolution
      else:
         a_   ,			a_						   =     np.random.choice(
             np.arange(image_processor_tester.min_resolution ,   image_processor_tester.max_resolution     ) ,   2     )
         a_						   =     prepare_video(
             image_processor_tester=UpperCAmelCase ,   width=UpperCAmelCase ,   height=UpperCAmelCase ,   numpify=UpperCAmelCase ,   torchify=UpperCAmelCase ,   )
      video_inputs.append(UpperCAmelCase     )

   return video_inputs





class 							snake_case       (     unittest.TestCase		):







   def __init__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=7       ,							__UpperCAmelCase=3       ,							__UpperCAmelCase=4       ,							__UpperCAmelCase=18       ,							__UpperCAmelCase=30       ,							__UpperCAmelCase=4_00       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							)   ->str:
      a_						   =     size if size is not None else {"shortest_edge": 18}
      a_						   =     crop_size if crop_size is not None else {"height": 18, "width": 18}

      a_						   =     parent
      a_						   =     batch_size
      a_						   =     num_channels
      a_						   =     num_frames
      a_						   =     image_size
      a_						   =     min_resolution
      a_						   =     max_resolution
      a_						   =     do_resize
      a_						   =     size
      a_						   =     do_normalize
      a_						   =     image_mean
      a_						   =     image_std
      a_						   =     do_center_crop
      a_						   =     crop_size







   def    UpperCAmelCase__	( self)   ->Tuple:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "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 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		int     					=     TvltImageProcessor if is_vision_available() else None







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      a_						   =     TvltImageProcessorTester(self)







   @property
   def    UpperCAmelCase__	( self)   ->int:
      return self.image_processor_tester.prepare_image_processor_dict()







   def    UpperCAmelCase__	( self)   ->int:
      a_						   =     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       ,							"do_center_crop"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"size"))







   def    UpperCAmelCase__	( self)   ->Any:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PIL videos
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							Image.Image)

      # Test not batched input
      a_						   =     image_processor(video_inputs[0]       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)

      # Test batched
      a_						   =     image_processor(__UpperCAmelCase       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)







   def    UpperCAmelCase__	( self)   ->int:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							numpify=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							np.ndarray)

      # Test not batched input
      a_						   =     image_processor(video_inputs[0]       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)

      # Test batched
      a_						   =     image_processor(__UpperCAmelCase       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)







   def    UpperCAmelCase__	( self)   ->Tuple:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							torchify=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							torch.Tensor)

      # Test not batched input
      a_						   =     image_processor(video_inputs[0]       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)

      # Test batched
      a_						   =     image_processor(__UpperCAmelCase       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)