'''simple docstring'''

import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class    lowerCamelCase__  (   __snake_case       ,				unittest.TestCase     ):
   __UpperCAmelCase           =						KandinskyVaaPriorEmbaEmbPipeline
   __UpperCAmelCase           =						["""prompt""", """image"""]
   __UpperCAmelCase           =						["""prompt""", """image"""]
   __UpperCAmelCase           =						[
       """num_images_per_prompt""",
       """strength""",
       """generator""",
       """num_inference_steps""",
       """latents""",
       """negative_prompt""",
       """guidance_scale""",
       """output_type""",
       """return_dict""",
   ]
   __UpperCAmelCase           =						False



   @property
   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        return 32



   @property
   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        return 32



   @property
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        return self.time_input_dim



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        return self.time_input_dim * 4



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        return 100



   @property
   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :str       =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
        return tokenizer



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        torch.manual_seed(0     )
        _UpperCamelCase  :List[str]       =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(lowerCAmelCase__     )



   @property
   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        torch.manual_seed(0     )

        _UpperCamelCase  :Optional[int]       ={
            """num_attention_heads""": 2,
            """attention_head_dim""": 12,
            """embedding_dim""": self.text_embedder_hidden_size,
            """num_layers""": 1,
        }

        _UpperCamelCase  :List[str]       =PriorTransformer(**lowerCAmelCase__     )
        # 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
        _UpperCamelCase  :Dict       =nn.Parameter(torch.ones(model.clip_std.shape     )     )
        return model



   @property
   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        torch.manual_seed(0     )
        _UpperCamelCase  :Any       =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					,      )

        _UpperCamelCase  :Union[str, Any]       =CLIPVisionModelWithProjection(lowerCAmelCase__     )
        return model



   @property
   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =CLIPImageProcessor(
            crop_size=224					,      do_center_crop=lowerCAmelCase__					,      do_normalize=lowerCAmelCase__					,      do_resize=lowerCAmelCase__					,      image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]					,      image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]					,      resample=3					,      size=224					,      )

        return image_processor



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =self.dummy_prior
        _UpperCamelCase  :Optional[int]       =self.dummy_image_encoder
        _UpperCamelCase  :Tuple       =self.dummy_text_encoder
        _UpperCamelCase  :Union[str, Any]       =self.dummy_tokenizer
        _UpperCamelCase  :Any       =self.dummy_image_processor

        _UpperCamelCase  :Dict       =UnCLIPScheduler(
            variance_type="""fixed_small_log"""					,      prediction_type="""sample"""					,      num_train_timesteps=1_000					,      clip_sample=lowerCAmelCase__					,      clip_sample_range=10.0					,      )

        _UpperCamelCase  :int       ={
            """prior""": prior,
            """image_encoder""": image_encoder,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """scheduler""": scheduler,
            """image_processor""": image_processor,
        }

        return components



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=0     )				->     Optional[Any]:
        """simple docstring"""





        if str(lowerCAmelCase__     ).startswith("""mps"""     ):
             _UpperCamelCase  :List[Any]       =torch.manual_seed(lowerCAmelCase__     )
        else:
             _UpperCamelCase  :Optional[int]       =torch.Generator(device=lowerCAmelCase__     ).manual_seed(lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       =floats_tensor((1, 3, 64, 64)					,      rng=random.Random(lowerCAmelCase__     )     ).to(lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =image.cpu().permute(0					,      2					,      3					,      1     )[0]
        _UpperCamelCase  :Dict       =Image.fromarray(np.uinta(lowerCAmelCase__     )     ).convert("""RGB"""     ).resize((256, 256)     )

        _UpperCamelCase  :Tuple       ={
            """prompt""": """horse""",
            """image""": init_image,
            """strength""": 0.5,
            """generator""": generator,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       ="""cpu"""

        _UpperCamelCase  :List[Any]       =self.get_dummy_components()

        _UpperCamelCase  :int       =self.pipeline_class(**lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =pipe.to(lowerCAmelCase__     )

        pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =pipe(**self.get_dummy_inputs(lowerCAmelCase__     )     )
        _UpperCamelCase  :Any       =output.image_embeds

        _UpperCamelCase  :Optional[Any]       =pipe(
            **self.get_dummy_inputs(lowerCAmelCase__     )					,      return_dict=lowerCAmelCase__					,      )[0]

        _UpperCamelCase  :int       =image[0, -10:]
        _UpperCamelCase  :Optional[int]       =image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        _UpperCamelCase  :int       =np.array(
            [
                0.107_1284,
                1.333_0271,
                0.6126_0223,
                -0.669_1065,
                -0.384_6852,
                -1.030_3661,
                0.2271_6111,
                0.0334_8901,
                0.3004_0675,
                -0.2480_5029,
            ]     )

        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  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Dict       =torch_device == """cpu"""
        _UpperCamelCase  :List[Any]       =True
        _UpperCamelCase  :Dict       =False

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




   @skip_mps
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =torch_device == """cpu"""
        _UpperCamelCase  :Union[str, Any]       =False

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

"""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__   :						List[str] 							=     """src/diffusers"""
A__   :						List[str] 							=     """docs/source/en"""
A__   :						List[Any] 							=     """."""



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							List[Any]		):






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

     UpperCAmelCase__				:			List[Any]							=    start_index
     while not lines[end_index].startswith(lowerCAmelCase		):
          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__   :						Tuple 							=     """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A__   :						Any 							=     re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
A__   :						Tuple 							=     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__   :						List[Any] 							=     re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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



def 				a__			(					lowerCAmelCase						:							List[str]		):






     '''simple docstring'''
     UpperCAmelCase__				:			Dict							=    re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"							,     lowerCAmelCase		)
     return [m.group(0		) for m in matches]



def 				a__			(					lowerCAmelCase						:							Tuple							,     lowerCAmelCase						:							Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    2 if text == "✅" or text == "❌" else len(lowerCAmelCase		)
     UpperCAmelCase__				:			Any							=    (width - text_length) // 2
     UpperCAmelCase__				:			List[Any]							=    width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__				:			Optional[int]							=    {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     UpperCAmelCase__				:			Optional[int]							=    {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.
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Union[str, Any]							=    collections.defaultdict(lowerCAmelCase		)

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

          if lookup_dict is not None:
               while len(lowerCAmelCase		) > 0:
                    if attr_name in model_name_to_prefix.values():
                         UpperCAmelCase__				:			Optional[Any]							=    True
                         break
                    # Try again after removing the last word in the name
                    UpperCAmelCase__				:			Union[str, Any]							=    "".join(camel_case_split(lowerCAmelCase		)[:-1]		)

    # Let's build that table!
     UpperCAmelCase__				:			Tuple							=    list(model_name_to_config.keys()		)
     model_names.sort(key=str.lower		)
     UpperCAmelCase__				:			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).
     UpperCAmelCase__				:			List[str]							=    [len(lowerCAmelCase		) + 2 for c in columns]
     UpperCAmelCase__				:			Union[str, Any]							=    max([len(lowerCAmelCase		) for name in model_names]		) + 2

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

     UpperCAmelCase__				:			List[Any]							=    {True: "✅", False: "❌"}
     for name in model_names:
          UpperCAmelCase__				:			Optional[Any]							=    model_name_to_prefix[name]
          UpperCAmelCase__				:			List[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(lowerCAmelCase							,     lowerCAmelCase		) for l, w in zip(lowerCAmelCase							,     lowerCAmelCase		)]		) + "|\n"
     return table



def 				a__			(					lowerCAmelCase						:							Union[str, Any]=False		):






     '''simple docstring'''
     UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			int							=    _find_text_in_file(
         filename=os.path.join(lowerCAmelCase							,     "index.mdx"		)							,     start_prompt="<!--This table is updated automatically from the auto modules"							,     end_prompt="<!-- End table-->"							,     )
     UpperCAmelCase__				:			Optional[Any]							=    get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(lowerCAmelCase							,     "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__   :						Union[str, Any] 							=     argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    A__   :						str 							=     parser.parse_args()

    check_model_table(args.fix_and_overwrite)



