


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_albert import AlbertTokenizer
else:
				_lowercase							:   List[str]						=						None

_lowercase							:   List[str]						=						logging.get_logger(__name__)
_lowercase							:   List[Any]						=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase							:   str						=						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

_lowercase							:   List[str]						=						{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowercase							:   Dict						=						"▁"






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			VOCAB_FILES_NAMES
				lowerCAmelCase					=			PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase					=			AlbertTokenizer

				def __init__(    self					, a__=None					, a__=None					, a__=True					, a__=True					, a__=False					, a__="[CLS]"					, a__="[SEP]"					, a__="<unk>"					, a__="[SEP]"					, a__="<pad>"					, a__="[CLS]"					, a__="[MASK]"					, **a__					, )						->					Dict:
					# Mask token behave like a normal word, i.e. include the space before it and
					# is included in the raw text, there should be a match in a non-normalized sentence.
					A             =					(
					    AddedToken(a__					, lstrip=a__					, rstrip=a__					, normalized=a__ )
					    if isinstance(a__					, a__ )
					    else mask_token
					)

					super().__init__(
					    a__					, tokenizer_file=a__					, do_lower_case=a__					, remove_space=a__					, keep_accents=a__					, bos_token=a__					, eos_token=a__					, unk_token=a__					, sep_token=a__					, pad_token=a__					, cls_token=a__					, mask_token=a__					, **a__					, )

					A             =					do_lower_case
					A             =					remove_space
					A             =					keep_accents
					A             =					vocab_file
					A             =					False if not self.vocab_file else True

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[int]:
					A             =					[self.sep_token_id]
					A             =					[self.cls_token_id]
					if token_ids_a is None:
						return cls + token_ids_a + sep
					return cls + token_ids_a + sep + token_ids_a + sep

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[int]:
					A             =					[self.sep_token_id]
					A             =					[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 ) * [0] + len(token_ids_a + sep ) * [1]

				def   _UpperCAmelCase       (    self					, a__					, a__ = 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(a__ ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
						return
					A             =					os.path.join(
					    a__					, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

					return (out_vocab_file,)







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			(IPNDMScheduler,)
				lowerCAmelCase					=			(('num_inference_steps', 5_0),)

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[str]:
					A             =					{"""num_train_timesteps""": 1000}
					config.update(**a__ )
					return config

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					Tuple:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config(**a__ )
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )
						# copy over dummy past residuals
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self )						->					Any:
					pass

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					int:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )

						# copy over dummy past residuals (must be after setting timesteps)
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

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

							# copy over dummy past residual (must be after setting timesteps)
							A             =					dummy_past_residuals[:]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[Any]:
					A             =					self.scheduler_classes[0]
					A             =					self.get_scheduler_config(**a__ )
					A             =					scheduler_class(**a__ )

					A             =					10
					A             =					self.dummy_model()
					A             =					self.dummy_sample_deter
					scheduler.set_timesteps(a__ )

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

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

					return sample

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					dict(self.forward_default_kwargs )

					A             =					kwargs.pop("""num_inference_steps"""					, a__ )

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )

						A             =					self.dummy_sample
						A             =					0.1 * sample

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

						# copy over dummy past residuals (must be done after set_timesteps)
						A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
						A             =					dummy_past_residuals[:]

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self )						->					Tuple:
					for timesteps in [100, 1000]:
						self.check_over_configs(num_train_timesteps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					Any:
					for t, num_inference_steps in zip([1, 5, 10]					, [10, 50, 100] ):
						self.check_over_forward(num_inference_steps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					self.full_loop()
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 254_0529 ) < 10



