


"""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
__snake_case												=							'''src/diffusers'''
__snake_case												=							'''docs/source/en'''
__snake_case												=							'''.'''
def    A_ ( _lowerCAmelCase  :       Any,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Tuple							):






     """simple docstring"""

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

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


# This is to make sure the diffusers module imported is the one in the repo.
__snake_case												=							importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(TRANSFORMERS_PATH, '''__init__.py'''),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__snake_case												=							spec.loader.load_module()
def    A_ ( _lowerCAmelCase  :       List[str]							):






     """simple docstring"""

     _a     						=					re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''',  _lowercase							)
     return [m.group(0							) for m in matches]
def    A_ ( _lowerCAmelCase  :       Optional[Any],  _lowerCAmelCase  :       List[str]							):






     """simple docstring"""

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






     """simple docstring"""

     _a     						=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     _a     						=					{
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     _a     						=					{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.
     _a     						=					collections.defaultdict(_lowercase							)
     _a     						=					collections.defaultdict(_lowercase							)
     _a     						=					collections.defaultdict(_lowercase							)
     _a     						=					collections.defaultdict(_lowercase							)
     _a     						=					collections.defaultdict(_lowercase							)

     # Let's lookup through all diffusers object (once).
     for attr_name in dir(_lowercase							):
          _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(_lowercase							) is not None:
               _a     						=					tf_models
               _a     						=					_re_tf_models.match(_lowercase							).groups()[0]
          elif _re_flax_models.match(_lowercase							) is not None:
               _a     						=					flax_models
               _a     						=					_re_flax_models.match(_lowercase							).groups()[0]
          elif _re_pt_models.match(_lowercase							) is not None:
               _a     						=					pt_models
               _a     						=					_re_pt_models.match(_lowercase							).groups()[0]

          if lookup_dict is not None:
               while len(_lowercase							) > 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(_lowercase							)[:-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(_lowercase							) + 2 for c in columns]
     _a     						=					max([len(_lowercase							) for name in model_names]							) + 2

     # Build the table per se
     _a     						=					'''|''' + '''|'''.join([_center_text(_lowercase,  _lowercase							) for c, w in zip(_lowercase,  _lowercase							)]							) + '''|\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(_lowercase,  _lowercase							) for l, w in zip(_lowercase,  _lowercase							)]							) + "|\n"
     return table
def    A_ ( _lowerCAmelCase  :       Union[str, Any]=False							):






     """simple docstring"""

     _a						,   _a						,   _a						,   _a     						=					_find_text_in_file(
         filename=os.path.join(_lowercase,  '''index.mdx'''							),  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(_lowercase,  '''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__":
   __snake_case												=							argparse.ArgumentParser()
   parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
   __snake_case												=							parser.parse_args()

   check_model_table(args.fix_and_overwrite)






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 	_UpperCAmelCase						( _lowercase			,							unittest.TestCase       ):
					__A        = KandinskyPipeline
					__A        = [
					    "prompt",
					    "image_embeds",
					    "negative_image_embeds",
					]
					__A        = ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
					__A        = [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "negative_prompt",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					__A        = False




					@property
					def 						snake_case_		(    self							)    ->	Any:
							return 32




					@property
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							return 32




					@property
					def 						snake_case_		(    self							)    ->	List[Any]:
							return self.time_input_dim




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return self.time_input_dim * 4




					@property
					def 						snake_case_		(    self							)    ->	Dict:
							return 100




					@property
					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__								=						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
							return tokenizer




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						MCLIPConfig(
							    numDims=self.cross_attention_dim     ,   transformerDimensions=self.text_embedder_hidden_size     ,   hidden_size=self.text_embedder_hidden_size     ,   intermediate_size=37     ,   num_attention_heads=4     ,   num_hidden_layers=5     ,   vocab_size=1_005     ,   )

							UpperCamelCase__								=						MultilingualCLIP(__lowerCAmelCase							)
							UpperCamelCase__								=						text_encoder.eval()

							return text_encoder




					@property
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							torch.manual_seed(0							)

							UpperCamelCase__								=						{
							    """in_channels""": 4,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
							    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
							    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
							    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    """layers_per_block""": 1,
							    """encoder_hid_dim""": self.text_embedder_hidden_size,
							    """encoder_hid_dim_type""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							UpperCamelCase__								=						UNetaDConditionModel(**__lowerCAmelCase							)
							return model




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}




					@property
					def 						snake_case_		(    self							)    ->	Optional[int]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						VQModel(**self.dummy_movq_kwargs							)
							return model




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						self.dummy_text_encoder
							UpperCamelCase__								=						self.dummy_tokenizer
							UpperCamelCase__								=						self.dummy_unet
							UpperCamelCase__								=						self.dummy_movq

							UpperCamelCase__								=						DDIMScheduler(
							    num_train_timesteps=1_000     ,   beta_schedule="""linear"""     ,   beta_start=0.0_0_0_8_5     ,   beta_end=0.0_1_2     ,   clip_sample=__lowerCAmelCase     ,   set_alpha_to_one=__lowerCAmelCase     ,   steps_offset=1     ,   prediction_type="""epsilon"""     ,   thresholding=__lowerCAmelCase     ,   )

							UpperCamelCase__								=						{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}
							return components




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=0							)    ->	str:
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(seed + 1							)							).to(__lowerCAmelCase							)
							if str(__lowerCAmelCase							).startswith("""mps"""							):
									UpperCamelCase__								=						torch.manual_seed(__lowerCAmelCase							)
							else:
									UpperCamelCase__								=						torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
							UpperCamelCase__								=						{
							    """prompt""": """horse""",
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """guidance_scale""": 4.0,
							    """num_inference_steps""": 2,
							    """output_type""": """np""",
							}
							return inputs




					def 						snake_case_		(    self							)    ->	Tuple:
							UpperCamelCase__								=						"""cpu"""

							UpperCamelCase__								=						self.get_dummy_components()

							UpperCamelCase__								=						self.pipeline_class(**__lowerCAmelCase							)
							UpperCamelCase__								=						pipe.to(__lowerCAmelCase							)

							pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

							UpperCamelCase__								=						pipe(**self.get_dummy_inputs(__lowerCAmelCase							)							)
							UpperCamelCase__								=						output.images

							UpperCamelCase__								=						pipe(
							    **self.get_dummy_inputs(__lowerCAmelCase							)     ,   return_dict=__lowerCAmelCase     ,   )[0]

							UpperCamelCase__								=						image[0, -3:, -3:, -1]
							UpperCamelCase__								=						image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							UpperCamelCase__								=						np.array(
							    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]							)

							assert (
							    np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




@slow
@require_torch_gpu
class 	_UpperCAmelCase						( unittest.TestCase       ):




					def 						snake_case_		(    self							)    ->	str:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""							)

							UpperCamelCase__								=						KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""     ,   torch_dtype=torch.floataa							)
							pipe_prior.to(__lowerCAmelCase							)

							UpperCamelCase__								=						KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""     ,   torch_dtype=torch.floataa							)
							UpperCamelCase__								=						pipeline.to(__lowerCAmelCase							)
							pipeline.set_progress_bar_config(disable=__lowerCAmelCase							)

							UpperCamelCase__								=						"""red cat, 4k photo"""

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__	,      UpperCamelCase__								=						pipe_prior(
							    __lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=5     ,   negative_prompt=""""""     ,   ).to_tuple()

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__								=						pipeline(
							    __lowerCAmelCase     ,   image_embeds=__lowerCAmelCase     ,   negative_image_embeds=__lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=100     ,   output_type="""np"""     ,   )

							UpperCamelCase__								=						output.images[0]

							assert image.shape == (512, 512, 3)

							assert_mean_pixel_difference(__lowerCAmelCase     ,   __lowerCAmelCase							)





