



'''simple docstring'''





import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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
lowercase_	 =						"src/transformers"
lowercase_	 =						"docs/source/en"
lowercase_	 =						"."




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     with open(__A						,			'''r'''						,			encoding='''utf-8'''						,			newline='''\n''') as f:
          _a											=						f.readlines()
     # Find the start prompt.
     _a											=						0
     while not lines[start_index].startswith(__A):
          start_index += 1
     start_index += 1

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


# This is to make sure the transformers module imported is the one in the repo.
lowercase_	 =						direct_transformers_import(TRANSFORMERS_PATH)




def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''						,			__A)
     return [m.group(0) for m in matches]




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						2 if text == '''✅''' or text == '''❌''' else len(__A)
     _a											=						(width - text_length) // 2
     _a											=						width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent




def lowerCAmelCase						():



     """simple docstring"""



     _a											=						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     _a											=						{
         name: config_maping_names[code]
         for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
         if code in config_maping_names
     }
     _a											=						{name: config.replace('''Config'''						,			'''''') 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.
     _a											=						collections.defaultdict(__A)
     _a											=						collections.defaultdict(__A)
     _a											=						collections.defaultdict(__A)
     _a											=						collections.defaultdict(__A)
     _a											=						collections.defaultdict(__A)

     # Let's lookup through all transformers object (once).
     for attr_name in dir(__A):
          _a											=						None
          if attr_name.endswith('''Tokenizer'''):
               _a											=						slow_tokenizers
               _a											=						attr_name[:-9]
          elif attr_name.endswith('''TokenizerFast'''):
               _a											=						fast_tokenizers
               _a											=						attr_name[:-13]
          elif _re_tf_models.match(__A) is not None:
               _a											=						tf_models
               _a											=						_re_tf_models.match(__A).groups()[0]
          elif _re_flax_models.match(__A) is not None:
               _a											=						flax_models
               _a											=						_re_flax_models.match(__A).groups()[0]
          elif _re_pt_models.match(__A) is not None:
               _a											=						pt_models
               _a											=						_re_pt_models.match(__A).groups()[0]

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

    # Let's build that table!
     _a											=						list(model_name_to_config.keys())
     model_names.sort(key=str.lower)
     _a											=						['''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).
     _a											=						[len(__A) + 2 for c in columns]
     _a											=						max([len(__A) for name in model_names]) + 2

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

     _a											=						{True: '''✅''', False: '''❌'''}
     for name in model_names:
          _a											=						model_name_to_prefix[name]
          _a											=						[
              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(__A						,			__A) for l, w in zip(__A						,			__A)]) + "|\n"
     return table




def lowerCAmelCase						(__A=False):



     """simple docstring"""



     _a						,					_a						,					_a						,					_a											=						_find_text_in_file(
         filename=os.path.join(__A						,			'''index.md''')						,			start_prompt='''<!--This table is updated automatically from the auto modules'''						,			end_prompt='''<!-- End table-->'''						,			)
     _a											=						get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(__A						,			'''index.md''')						,			'''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.md` has not been updated. Run `make fix-copies` to fix this.''')


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

  check_model_table(args.fix_and_overwrite)








'''simple docstring'''





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = KandinskyVaaPriorPipeline
 __lowerCamelCase					: List[str]    = ['prompt']
 __lowerCamelCase					: str    = ['prompt', 'negative_prompt']
 __lowerCamelCase					: Union[str, Any]    = [
     'num_images_per_prompt',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 __lowerCamelCase					: Union[str, Any]    = False



 @property
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.time_input_dim



 @property
 def       a__						(self )					->  Any:


      """simple docstring"""



      return self.time_input_dim * 4



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return 100



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
      return tokenizer



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      torch.manual_seed(0 )
      _a											=						CLIPTextConfig(
          bos_token_id=0				,     eos_token_id=2				,     hidden_size=self.text_embedder_hidden_size				,     projection_dim=self.text_embedder_hidden_size				,     intermediate_size=37				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_000				,     )
      return CLIPTextModelWithProjection(A )



 @property
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      torch.manual_seed(0 )

      _a											=						{
          '''num_attention_heads''': 2,
          '''attention_head_dim''': 12,
          '''embedding_dim''': self.text_embedder_hidden_size,
          '''num_layers''': 1,
      }

      _a											=						PriorTransformer(**A )
      # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
      _a											=						nn.Parameter(torch.ones(model.clip_std.shape ) )
      return model



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      torch.manual_seed(0 )
      _a											=						CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size				,     image_size=224				,     projection_dim=self.text_embedder_hidden_size				,     intermediate_size=37				,     num_attention_heads=4				,     num_channels=3				,     num_hidden_layers=5				,     patch_size=14				,     )

      _a											=						CLIPVisionModelWithProjection(A )
      return model



 @property
 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						CLIPImageProcessor(
          crop_size=224				,     do_center_crop=A				,     do_normalize=A				,     do_resize=A				,     image_mean=[0.48145466, 0.4578275, 0.40821073]				,     image_std=[0.26862954, 0.26130258, 0.27577711]				,     resample=3				,     size=224				,     )

      return image_processor



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						self.dummy_prior
      _a											=						self.dummy_image_encoder
      _a											=						self.dummy_text_encoder
      _a											=						self.dummy_tokenizer
      _a											=						self.dummy_image_processor

      _a											=						UnCLIPScheduler(
          variance_type='''fixed_small_log'''				,     prediction_type='''sample'''				,     num_train_timesteps=1_000				,     clip_sample=A				,     clip_sample_range=10.0				,     )

      _a											=						{
          '''prior''': prior,
          '''image_encoder''': image_encoder,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''scheduler''': scheduler,
          '''image_processor''': image_processor,
      }

      return components



 def       a__						(self				,     A				,     A=0 )					->  Optional[int]:


      """simple docstring"""



      if str(A ).startswith('''mps''' ):
           _a											=						torch.manual_seed(A )
      else:
           _a											=						torch.Generator(device=A ).manual_seed(A )
      _a											=						{
          '''prompt''': '''horse''',
          '''generator''': generator,
          '''guidance_scale''': 4.0,
          '''num_inference_steps''': 2,
          '''output_type''': '''np''',
      }
      return inputs



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						'''cpu'''

      _a											=						self.get_dummy_components()

      _a											=						self.pipeline_class(**A )
      _a											=						pipe.to(A )

      pipe.set_progress_bar_config(disable=A )

      _a											=						pipe(**self.get_dummy_inputs(A ) )
      _a											=						output.image_embeds

      _a											=						pipe(
          **self.get_dummy_inputs(A )				,     return_dict=A				,     )[0]

      _a											=						image[0, -10:]
      _a											=						image_from_tuple[0, -10:]

      assert image.shape == (1, 32)

      _a											=						np.array(
          [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2



 @skip_mps
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						torch_device == '''cpu'''
      _a											=						True
      _a											=						False

      self._test_inference_batch_single_identical(
          test_max_difference=A				,     relax_max_difference=A				,     test_mean_pixel_difference=A				,     )



 @skip_mps
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						torch_device == '''cpu'''
      _a											=						False

      self._test_attention_slicing_forward_pass(
          test_max_difference=A				,     test_mean_pixel_difference=A				,     )



