
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[Any]				   =					(UniPCMultistepScheduler,)
						UpperCamelCase      :  Tuple				   =					(('''num_inference_steps''', 25),)





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    **lowerCAmelCase__							:Dict     )  ->List[Any]:
										lowercase         =      {
										    "num_train_timesteps": 1000,
										    "beta_start": 0.00_01,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										    "solver_type": "bh2",
										}

										config.update(**lowerCAmelCase__     )
										return config





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Union[str, Any]=0		,    **lowerCAmelCase__							:Optional[Any]     )  ->List[Any]:
										lowercase         =      dict(self.forward_default_kwargs     )
										lowercase         =      kwargs.pop("num_inference_steps"		,    lowerCAmelCase__     )
										lowercase         =      self.dummy_sample
										lowercase         =      0.1 * sample
										lowercase         =      [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
														lowercase         =      self.get_scheduler_config(**lowerCAmelCase__     )
														lowercase         =      scheduler_class(**lowerCAmelCase__     )
														scheduler.set_timesteps(lowerCAmelCase__     )
														# copy over dummy past residuals
														lowercase         =      dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowerCAmelCase__     )
																		lowercase         =      scheduler_class.from_pretrained(lowerCAmelCase__     )
																		new_scheduler.set_timesteps(lowerCAmelCase__     )
																		# copy over dummy past residuals
																		lowercase         =      dummy_past_residuals[: new_scheduler.config.solver_order]

														lowercase     ,	lowercase         =      sample, sample
														for t in range(lowerCAmelCase__		,    time_step + scheduler.config.solver_order + 1     ):
																		lowercase         =      scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    **lowerCAmelCase__     ).prev_sample
																		lowercase         =      new_scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    **lowerCAmelCase__     ).prev_sample

																		assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:Optional[int]=0		,    **lowerCAmelCase__							:Any     )  ->str:
										lowercase         =      dict(self.forward_default_kwargs     )
										lowercase         =      kwargs.pop("num_inference_steps"		,    lowerCAmelCase__     )
										lowercase         =      self.dummy_sample
										lowercase         =      0.1 * sample
										lowercase         =      [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
														lowercase         =      self.get_scheduler_config()
														lowercase         =      scheduler_class(**lowerCAmelCase__     )
														scheduler.set_timesteps(lowerCAmelCase__     )

														# copy over dummy past residuals (must be after setting timesteps)
														lowercase         =      dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowerCAmelCase__     )
																		lowercase         =      scheduler_class.from_pretrained(lowerCAmelCase__     )
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(lowerCAmelCase__     )

																		# copy over dummy past residual (must be after setting timesteps)
																		lowercase         =      dummy_past_residuals[: new_scheduler.config.solver_order]

														lowercase         =      scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    **lowerCAmelCase__     ).prev_sample
														lowercase         =      new_scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    **lowerCAmelCase__     ).prev_sample

														assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[Any]=None		,    **lowerCAmelCase__							:Optional[int]     )  ->Union[str, Any]:
										if scheduler is None:
														lowercase         =      self.scheduler_classes[0]
														lowercase         =      self.get_scheduler_config(**lowerCAmelCase__     )
														lowercase         =      scheduler_class(**lowerCAmelCase__     )

										lowercase         =      self.scheduler_classes[0]
										lowercase         =      self.get_scheduler_config(**lowerCAmelCase__     )
										lowercase         =      scheduler_class(**lowerCAmelCase__     )

										lowercase         =      10
										lowercase         =      self.dummy_model()
										lowercase         =      self.dummy_sample_deter
										scheduler.set_timesteps(lowerCAmelCase__     )

										for i, t in enumerate(scheduler.timesteps     ):
														lowercase         =      model(lowerCAmelCase__		,    lowerCAmelCase__     )
														lowercase         =      scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     ).prev_sample

										return sample





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Union[str, Any]:
										lowercase         =      dict(self.forward_default_kwargs     )

										lowercase         =      kwargs.pop("num_inference_steps"		,    lowerCAmelCase__     )

										for scheduler_class in self.scheduler_classes:
														lowercase         =      self.get_scheduler_config()
														lowercase         =      scheduler_class(**lowerCAmelCase__     )

														lowercase         =      self.dummy_sample
														lowercase         =      0.1 * sample

														if num_inference_steps is not None and hasattr(lowerCAmelCase__		,    "set_timesteps"     ):
																		scheduler.set_timesteps(lowerCAmelCase__     )
														elif num_inference_steps is not None and not hasattr(lowerCAmelCase__		,    "set_timesteps"     ):
																		lowercase         =      num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														lowercase         =      [residual + 0.2, residual + 0.15, residual + 0.10]
														lowercase         =      dummy_past_residuals[: scheduler.config.solver_order]

														lowercase         =      scheduler.timesteps[5]
														lowercase         =      scheduler.timesteps[6]

														lowercase         =      scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    **lowerCAmelCase__     ).prev_sample
														lowercase         =      scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    **lowerCAmelCase__     ).prev_sample

														self.assertEqual(output_a.shape		,    sample.shape     )
														self.assertEqual(output_a.shape		,    output_a.shape     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Any:
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										lowercase         =      UniPCMultistepScheduler(**self.get_scheduler_config()     )
										lowercase         =      self.full_loop(scheduler=lowerCAmelCase__     )
										lowercase         =      torch.mean(torch.abs(lowerCAmelCase__     )     )

										assert abs(result_mean.item() - 0.24_64     ) < 1E-3

										lowercase         =      DPMSolverSinglestepScheduler.from_config(scheduler.config     )
										lowercase         =      DEISMultistepScheduler.from_config(scheduler.config     )
										lowercase         =      DPMSolverMultistepScheduler.from_config(scheduler.config     )
										lowercase         =      UniPCMultistepScheduler.from_config(scheduler.config     )

										lowercase         =      self.full_loop(scheduler=lowerCAmelCase__     )
										lowercase         =      torch.mean(torch.abs(lowerCAmelCase__     )     )

										assert abs(result_mean.item() - 0.24_64     ) < 1E-3





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Optional[Any]:
										for timesteps in [25, 50, 100, 999, 1000]:
														self.check_over_configs(num_train_timesteps=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Optional[int]:
										self.check_over_configs(thresholding=lowerCAmelCase__     )
										for order in [1, 2, 3]:
														for solver_type in ["bh1", "bh2"]:
																		for threshold in [0.5, 1.0, 2.0]:
																						for prediction_type in ["epsilon", "sample"]:
																										self.check_over_configs(
																										    thresholding=lowerCAmelCase__		,    prediction_type=lowerCAmelCase__		,    sample_max_value=lowerCAmelCase__		,    solver_order=lowerCAmelCase__		,    solver_type=lowerCAmelCase__		,    )





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->str:
										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Dict:
										for solver_type in ["bh1", "bh2"]:
														for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    solver_order=lowerCAmelCase__		,    solver_type=lowerCAmelCase__		,    prediction_type=lowerCAmelCase__		,    )
																						lowercase         =      self.full_loop(
																						    solver_order=lowerCAmelCase__		,    solver_type=lowerCAmelCase__		,    prediction_type=lowerCAmelCase__		,    )
																						assert not torch.isnan(lowerCAmelCase__     ).any(), "Samples have nan numbers"





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->str:
										self.check_over_configs(lower_order_final=lowerCAmelCase__     )
										self.check_over_configs(lower_order_final=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Dict:
										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
														self.check_over_forward(num_inference_steps=lowerCAmelCase__		,    time_step=0     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Optional[int]:
										lowercase         =      self.full_loop()
										lowercase         =      torch.mean(torch.abs(lowerCAmelCase__     )     )

										assert abs(result_mean.item() - 0.24_64     ) < 1E-3





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Union[str, Any]:
										lowercase         =      self.full_loop(prediction_type="v_prediction"     )
										lowercase         =      torch.mean(torch.abs(lowerCAmelCase__     )     )

										assert abs(result_mean.item() - 0.10_14     ) < 1E-3





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Tuple:
										lowercase         =      self.scheduler_classes[0]
										lowercase         =      self.get_scheduler_config(thresholding=lowerCAmelCase__		,    dynamic_thresholding_ratio=0     )
										lowercase         =      scheduler_class(**lowerCAmelCase__     )

										lowercase         =      10
										lowercase         =      self.dummy_model()
										lowercase         =      self.dummy_sample_deter.half()
										scheduler.set_timesteps(lowerCAmelCase__     )

										for i, t in enumerate(scheduler.timesteps     ):
														lowercase         =      model(lowerCAmelCase__		,    lowerCAmelCase__     )
														lowercase         =      scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     ).prev_sample

										assert sample.dtype == torch.floataa





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    **lowerCAmelCase__							:str     )  ->Union[str, Any]:
										for scheduler_class in self.scheduler_classes:
														lowercase         =      self.get_scheduler_config(**lowerCAmelCase__     )
														lowercase         =      scheduler_class(**lowerCAmelCase__     )

														scheduler.set_timesteps(scheduler.config.num_train_timesteps     )
														assert len(scheduler.timesteps.unique()     ) == scheduler.num_inference_steps


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						_snake_case      :							Any     	=    None

_snake_case      :							Union[str, Any]     	=    logging.get_logger(__name__)

_snake_case      :							Optional[Any]     	=    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_snake_case      :							int     	=    {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case      :							str     	=    {
    "moussaKam/mbarthez": 10_24,
    "moussaKam/barthez": 10_24,
    "moussaKam/barthez-orangesum-title": 10_24,
}

_snake_case      :							Optional[int]     	=    "▁"





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					VOCAB_FILES_NAMES
						UpperCamelCase      :  Any				   =					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      :  Union[str, Any]				   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      :  Dict				   =					['''input_ids''', '''attention_mask''']
						UpperCamelCase      :  Optional[Any]				   =					BarthezTokenizer





						def __init__(			self							:str		,    lowerCAmelCase__							:str=None		,    lowerCAmelCase__							:List[str]=None		,    lowerCAmelCase__							:Union[str, Any]="<s>"		,    lowerCAmelCase__							:Optional[Any]="</s>"		,    lowerCAmelCase__							:str="</s>"		,    lowerCAmelCase__							:List[str]="<s>"		,    lowerCAmelCase__							:Dict="<unk>"		,    lowerCAmelCase__							:int="<pad>"		,    lowerCAmelCase__							:Optional[Any]="<mask>"		,    **lowerCAmelCase__							:Optional[int]		,    )  ->int:
										# Mask token behave like a normal word, i.e. include the space before it
										lowercase         =      AddedToken(lowerCAmelCase__		,    lstrip=lowerCAmelCase__		,    rstrip=lowerCAmelCase__     ) if isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) else mask_token

										super().__init__(
										    lowerCAmelCase__		,    tokenizer_file=lowerCAmelCase__		,    bos_token=lowerCAmelCase__		,    eos_token=lowerCAmelCase__		,    unk_token=lowerCAmelCase__		,    sep_token=lowerCAmelCase__		,    cls_token=lowerCAmelCase__		,    pad_token=lowerCAmelCase__		,    mask_token=lowerCAmelCase__		,    **lowerCAmelCase__		,    )

										lowercase         =      vocab_file
										lowercase         =      False if not self.vocab_file else True





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:

										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										lowercase         =      [self.cls_token_id]
										lowercase         =      [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:
										lowercase         =      [self.sep_token_id]
										lowercase         =      [self.cls_token_id]

										if token_ids_a is None:
														return len(cls + token_ids_a + sep     ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[str] = None     )  ->Tuple[str]:
										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
														    "tokenizer."     )

										if not os.path.isdir(lowerCAmelCase__     ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
														return
										lowercase         =      os.path.join(
										    lowerCAmelCase__		,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

										if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowerCAmelCase__     ):
														copyfile(self.vocab_file		,    lowerCAmelCase__     )

										return (out_vocab_file,)
