

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
SCREAMING_SNAKE_CASE		:List[Any]						=     """src/transformers"""
SCREAMING_SNAKE_CASE		:Tuple						=     """docs/source/en"""
SCREAMING_SNAKE_CASE		:Dict						=     """."""




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






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

    UpperCamelCase_			  =						start_index
    while not lines[end_index].startswith(SCREAMING_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 |
SCREAMING_SNAKE_CASE		:Dict						=     """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE		:Union[str, Any]						=     re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
SCREAMING_SNAKE_CASE		:List[Any]						=     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.
SCREAMING_SNAKE_CASE		:List[str]						=     re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE		:Optional[int]						=     direct_transformers_import(TRANSFORMERS_PATH)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[str]:



    """simple docstring"""






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




def 			lowerCAmelCase(      )->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    UpperCamelCase_			  =						{
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    UpperCamelCase_			  =						{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.
    UpperCamelCase_			  =						collections.defaultdict(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						collections.defaultdict(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						collections.defaultdict(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						collections.defaultdict(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						collections.defaultdict(SCREAMING_SNAKE_CASE_			)

    # Let's lookup through all transformers object (once).
    for attr_name in dir(SCREAMING_SNAKE_CASE_			):
        UpperCamelCase_			  =						None
        if attr_name.endswith("Tokenizer"			):
            UpperCamelCase_			  =						slow_tokenizers
            UpperCamelCase_			  =						attr_name[:-9]
        elif attr_name.endswith("TokenizerFast"			):
            UpperCamelCase_			  =						fast_tokenizers
            UpperCamelCase_			  =						attr_name[:-1_3]
        elif _re_tf_models.match(SCREAMING_SNAKE_CASE_			) is not None:
            UpperCamelCase_			  =						tf_models
            UpperCamelCase_			  =						_re_tf_models.match(SCREAMING_SNAKE_CASE_			).groups()[0]
        elif _re_flax_models.match(SCREAMING_SNAKE_CASE_			) is not None:
            UpperCamelCase_			  =						flax_models
            UpperCamelCase_			  =						_re_flax_models.match(SCREAMING_SNAKE_CASE_			).groups()[0]
        elif _re_pt_models.match(SCREAMING_SNAKE_CASE_			) is not None:
            UpperCamelCase_			  =						pt_models
            UpperCamelCase_			  =						_re_pt_models.match(SCREAMING_SNAKE_CASE_			).groups()[0]

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

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

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

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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_=False			)->						Optional[Any]:



    """simple docstring"""






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

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

 check_model_table(args.fix_and_overwrite)



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      (KDPMaDiscreteScheduler,)
     UpperCamelCase_	:str										=      1_0






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     str:
         UpperCamelCase_			  =						{
             "num_train_timesteps": 1_100,
             "beta_start": 0.0_001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
         }

         config.update(**_lowercase	)
         return config






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     int:
         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,						[0.0_002, 0.002, 0.02]	):
             self.check_over_configs(beta_start=_lowercase		,						beta_end=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     str:
         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						self.scheduler_classes[0]
         UpperCamelCase_			  =						self.get_scheduler_config(prediction_type="v_prediction"	)
         UpperCamelCase_			  =						scheduler_class(**_lowercase	)

         scheduler.set_timesteps(self.num_inference_steps	)

         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCamelCase_			  =						sample.to(_lowercase	)

         for i, t in enumerate(scheduler.timesteps	):
             UpperCamelCase_			  =						scheduler.scale_model_input(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						model(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						scheduler.step(_lowercase		,						_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						output.prev_sample

         UpperCamelCase_			  =						torch.sum(torch.abs(_lowercase	)	)
         UpperCamelCase_			  =						torch.mean(torch.abs(_lowercase	)	)

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_9_3_4e-0_7	) < 1e-2
             assert abs(result_mean.item() - 6.1_1_1_2e-1_0	) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7	) < 1e-2
             assert abs(result_mean.item() - 0.0_002	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Dict:
         if torch_device == "mps":
             return
         UpperCamelCase_			  =						self.scheduler_classes[0]
         UpperCamelCase_			  =						self.get_scheduler_config()
         UpperCamelCase_			  =						scheduler_class(**_lowercase	)

         scheduler.set_timesteps(self.num_inference_steps	)

         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCamelCase_			  =						sample.to(_lowercase	)

         for i, t in enumerate(scheduler.timesteps	):
             UpperCamelCase_			  =						scheduler.scale_model_input(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						model(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						scheduler.step(_lowercase		,						_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						output.prev_sample

         UpperCamelCase_			  =						torch.sum(torch.abs(_lowercase	)	)
         UpperCamelCase_			  =						torch.mean(torch.abs(_lowercase	)	)

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         if torch_device == "mps":
             return
         UpperCamelCase_			  =						self.scheduler_classes[0]
         UpperCamelCase_			  =						self.get_scheduler_config()
         UpperCamelCase_			  =						scheduler_class(**_lowercase	)

         scheduler.set_timesteps(self.num_inference_steps		,						device=_lowercase	)

         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter.to(_lowercase	) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
             UpperCamelCase_			  =						scheduler.scale_model_input(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						model(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						scheduler.step(_lowercase		,						_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						output.prev_sample

         UpperCamelCase_			  =						torch.sum(torch.abs(_lowercase	)	)
         UpperCamelCase_			  =						torch.mean(torch.abs(_lowercase	)	)

         if str(_lowercase	).startswith("cpu"	):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3
