"""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
__A         =				"src/diffusers"
__A         =				"docs/source/en"
__A         =				"."



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

   lowercase__:     str      =   start_index
   while not lines[end_index].startswith(__UpperCAmelCase				):
      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 |
__A         =				"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
__A         =				re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__A         =				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.
__A         =				re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Optional[Any]:
   lowercase__:     Union[str, Any]      =   re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' ,					__UpperCAmelCase				)
   return [m.group(0				) for m in matches]



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase				)						->						Dict:
   lowercase__:     int      =   2 if text == '''✅''' or text == '''❌''' else len(__UpperCAmelCase				)
   lowercase__:     Tuple      =   (width - text_length) // 2
   lowercase__:     List[str]      =   width - text_length - left_indent
   return " " * left_indent + text + " " * right_indent



def SCREAMING_SNAKE_CASE__    (     )						->						Optional[int]:
   lowercase__:     str      =   diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   lowercase__:     int      =   {
       name: config_mapping_names[code]
       for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
       if code in config_mapping_names
   }
   lowercase__:     Tuple      =   {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__:     str      =   collections.defaultdict(__UpperCAmelCase				)
   lowercase__:     Tuple      =   collections.defaultdict(__UpperCAmelCase				)
   lowercase__:     Optional[Any]      =   collections.defaultdict(__UpperCAmelCase				)
   lowercase__:     Dict      =   collections.defaultdict(__UpperCAmelCase				)
   lowercase__:     int      =   collections.defaultdict(__UpperCAmelCase				)

   # Let's lookup through all diffusers object (once).
   for attr_name in dir(__UpperCAmelCase				):
      lowercase__:     Any      =   None
      if attr_name.endswith('''Tokenizer'''				):
         lowercase__:     Union[str, Any]      =   slow_tokenizers
         lowercase__:     str      =   attr_name[:-9]
      elif attr_name.endswith('''TokenizerFast'''				):
         lowercase__:     Dict      =   fast_tokenizers
         lowercase__:     Optional[Any]      =   attr_name[:-1_3]
      elif _re_tf_models.match(__UpperCAmelCase				) is not None:
         lowercase__:     List[Any]      =   tf_models
         lowercase__:     str      =   _re_tf_models.match(__UpperCAmelCase				).groups()[0]
      elif _re_flax_models.match(__UpperCAmelCase				) is not None:
         lowercase__:     Union[str, Any]      =   flax_models
         lowercase__:     Tuple      =   _re_flax_models.match(__UpperCAmelCase				).groups()[0]
      elif _re_pt_models.match(__UpperCAmelCase				) is not None:
         lowercase__:     int      =   pt_models
         lowercase__:     Dict      =   _re_pt_models.match(__UpperCAmelCase				).groups()[0]

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

    # Let's build that table!
   lowercase__:     Optional[int]      =   list(model_name_to_config.keys()				)
   model_names.sort(key=str.lower				)
   lowercase__:     Dict      =   ['''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__:     int      =   [len(__UpperCAmelCase				) + 2 for c in columns]
   lowercase__:     Union[str, Any]      =   max([len(__UpperCAmelCase				) for name in model_names]				) + 2

   # Build the table per se
   lowercase__:     int      =   '''|''' + '''|'''.join([_center_text(__UpperCAmelCase ,					__UpperCAmelCase				) for c, w in zip(__UpperCAmelCase ,					__UpperCAmelCase				)]				) + '''|\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__:     Dict      =   model_name_to_prefix[name]
      lowercase__:     int      =   [
          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(__UpperCAmelCase ,					__UpperCAmelCase				) for l, w in zip(__UpperCAmelCase ,					__UpperCAmelCase				)]				) + "|\n"
   return table



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase=False				)						->						Optional[int]:
   lowercase__,				lowercase__,				lowercase__,				lowercase__:     List[str]      =   _find_text_in_file(
       filename=os.path.join(__UpperCAmelCase ,					'''index.mdx'''				) ,					start_prompt='''<!--This table is updated automatically from the auto modules''' ,					end_prompt='''<!-- End table-->''' ,					)
   lowercase__:     Optional[int]      =   get_model_table_from_auto_modules()

   if current_table != new_table:
      if overwrite:
         with open(os.path.join(__UpperCAmelCase ,					'''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__":
       __A         =				argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       __A         =				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 SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase=1_0 ,					__UpperCAmelCase=1_0 ,					__UpperCAmelCase=False ,					__UpperCAmelCase=False				)						->						List[str]:
   lowercase__:     List[Any]      =   []
   for i in range(image_processor_tester.num_frames				):
      video.append(np.random.randint(2_5_5 ,					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
      lowercase__:     Union[str, Any]      =   [Image.fromarray(np.moveaxis(__UpperCAmelCase ,					0 ,					-1				)				) for frame in video]

   if torchify:
      lowercase__:     List[str]      =   [torch.from_numpy(__UpperCAmelCase				) for frame in video]

   return video



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase=False ,					__UpperCAmelCase=False ,					__UpperCAmelCase=False				)						->						Tuple:
   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   lowercase__:     int      =   []
   for i in range(image_processor_tester.batch_size				):
      if equal_resolution:
         lowercase__:     Any      =   image_processor_tester.max_resolution
      else:
         lowercase__,				lowercase__:     Union[str, Any]      =   np.random.choice(
             np.arange(image_processor_tester.min_resolution ,					image_processor_tester.max_resolution				) ,					2				)
         lowercase__:     str      =   prepare_video(
             image_processor_tester=__UpperCAmelCase ,					width=__UpperCAmelCase ,					height=__UpperCAmelCase ,					numpify=__UpperCAmelCase ,					torchify=__UpperCAmelCase ,					)
      video_inputs.append(__UpperCAmelCase				)

   return video_inputs


class 					UpperCAmelCase		(unittest.TestCase						):



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase					,    _UpperCAmelCase=7					,    _UpperCAmelCase=3					,    _UpperCAmelCase=4					,    _UpperCAmelCase=18					,    _UpperCAmelCase=30					,    _UpperCAmelCase=400					,    _UpperCAmelCase=True					,    _UpperCAmelCase=None					,    _UpperCAmelCase=True					,    _UpperCAmelCase=[0.5, 0.5, 0.5]					,    _UpperCAmelCase=[0.5, 0.5, 0.5]					,    _UpperCAmelCase=True					,    _UpperCAmelCase=None					,    ):
       lowercase__:     Any      =   size if size is not None else {'''shortest_edge''': 18}
       lowercase__:     List[Any]      =   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

       lowercase__:     List[Any]      =   parent
       lowercase__:     List[str]      =   batch_size
       lowercase__:     Union[str, Any]      =   num_channels
       lowercase__:     List[Any]      =   num_frames
       lowercase__:     Tuple      =   image_size
       lowercase__:     List[Any]      =   min_resolution
       lowercase__:     Union[str, Any]      =   max_resolution
       lowercase__:     Dict      =   do_resize
       lowercase__:     List[Any]      =   size
       lowercase__:     str      =   do_normalize
       lowercase__:     int      =   image_mean
       lowercase__:     Any      =   image_std
       lowercase__:     List[Any]      =   do_center_crop
       lowercase__:     Optional[int]      =   crop_size






    def _snake_case    (     self							):
       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 					UpperCAmelCase		(_UpperCAmelCase					,unittest.TestCase						):



    """simple docstring"""

    _UpperCAmelCase			:str							=				TvltImageProcessor if is_vision_available() else None






    def _snake_case    (     self							):
       lowercase__:     List[Any]      =   TvltImageProcessorTester(self							)






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






    def _snake_case    (     self							):
       lowercase__:     List[str]      =   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 _snake_case    (     self							):
       # Initialize image_processor
       lowercase__:     List[str]      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PIL videos
       lowercase__:     str      =   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
       lowercase__:     Union[str, Any]      =   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
       lowercase__:     Tuple      =   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 _snake_case    (     self							):
       # Initialize image_processor
       lowercase__:     str      =   self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       lowercase__:     List[str]      =   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
       lowercase__:     Optional[Any]      =   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
       lowercase__:     Tuple      =   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 _snake_case    (     self							):
       # Initialize image_processor
       lowercase__:     List[Any]      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       lowercase__:     Optional[int]      =   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
       lowercase__:     Optional[Any]      =   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
       lowercase__:     List[Any]      =   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'''],
           )					,    )
