



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


_lowercase: int      			=		logging.get_logger(__name__)

_lowercase: List[str]      			=		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowercase: int      			=		{
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

_lowercase: Optional[int]      			=		{
    "bert-base-uncased": 512,
    "bert-large-uncased": 512,
    "bert-base-cased": 512,
    "bert-large-cased": 512,
    "bert-base-multilingual-uncased": 512,
    "bert-base-multilingual-cased": 512,
    "bert-base-chinese": 512,
    "bert-base-german-cased": 512,
    "bert-large-uncased-whole-word-masking": 512,
    "bert-large-cased-whole-word-masking": 512,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
    "bert-base-cased-finetuned-mrpc": 512,
    "bert-base-german-dbmdz-cased": 512,
    "bert-base-german-dbmdz-uncased": 512,
    "TurkuNLP/bert-base-finnish-cased-v1": 512,
    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
    "wietsedv/bert-base-dutch-cased": 512,
}

_lowercase: Any      			=		{
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							VOCAB_FILES_NAMES
	__A							=							PRETRAINED_VOCAB_FILES_MAP
	__A							=							PRETRAINED_INIT_CONFIGURATION
	__A							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A							=							BertTokenizer




	def __init__(self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=True		,       lowerCamelCase_="[UNK]"		,       lowerCamelCase_="[SEP]"		,       lowerCamelCase_="[PAD]"		,       lowerCamelCase_="[CLS]"		,       lowerCamelCase_="[MASK]"		,       lowerCamelCase_=True		,       lowerCamelCase_=None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						super().__init__(
						    lowerCamelCase_		,       tokenizer_file=lowerCamelCase_		,       do_lower_case=lowerCamelCase_		,       unk_token=lowerCamelCase_		,       sep_token=lowerCamelCase_		,       pad_token=lowerCamelCase_		,       cls_token=lowerCamelCase_		,       mask_token=lowerCamelCase_		,       tokenize_chinese_chars=lowerCamelCase_		,       strip_accents=lowerCamelCase_		,       **lowerCamelCase_		,       )

						a					     =   json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
						if (
						    normalizer_state.get("lowercase"		,       lowerCamelCase_		) != do_lower_case
						    or normalizer_state.get("strip_accents"		,       lowerCamelCase_		) != strip_accents
						    or normalizer_state.get("handle_chinese_chars"		,       lowerCamelCase_		) != tokenize_chinese_chars
						):
											a					     =   getattr(lowerCamelCase_		,       normalizer_state.pop("type"		)		)
											a					     =   do_lower_case
											a					     =   strip_accents
											a					     =   tokenize_chinese_chars
											a					     =   normalizer_class(**lowerCamelCase_		)

						a					     =   do_lower_case




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

						return output




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						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		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						a					     =   self._tokenizer.model.save(lowerCamelCase_		,       name=lowerCamelCase_		)
						return tuple(lowerCamelCase_		)





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							(DDPMScheduler,)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "variance_type": "fixed_small",
						    "clip_sample": True,
						}

						config.update(**lowerCamelCase_		)
						return config




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,       [0.002, 0.02, 0.2, 2]		):
											self.check_over_configs(beta_start=lowerCamelCase_		,       beta_end=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.check_over_configs(thresholding=lowerCamelCase_		)
						for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowerCamelCase_		,       prediction_type=lowerCamelCase_		,       sample_max_value=lowerCamelCase_		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for t in [0, 500, 999]:
											self.check_over_forward(time_step=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_0979		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

						a					     =   self.dummy_model()
						a					     =   self.dummy_sample_deter
						a					     =   torch.manual_seed(0		)

						for t in reversed(range(lowerCamelCase_		)		):
											# 1. predict noise residual
											a					     =   model(lowerCamelCase_		,       lowerCamelCase_		)

											# 2. predict previous mean of sample x_t-1
											a					     =   scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       generator=lowerCamelCase_		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											a					     =   pred_prev_sample

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

						assert abs(result_sum.item() - 258.9606		) < 1E-2
						assert abs(result_mean.item() - 0.3372		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config(prediction_type="v_prediction"		)
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

						a					     =   self.dummy_model()
						a					     =   self.dummy_sample_deter
						a					     =   torch.manual_seed(0		)

						for t in reversed(range(lowerCamelCase_		)		):
											# 1. predict noise residual
											a					     =   model(lowerCamelCase_		,       lowerCamelCase_		)

											# 2. predict previous mean of sample x_t-1
											a					     =   scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       generator=lowerCamelCase_		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											a					     =   pred_prev_sample

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

						assert abs(result_sum.item() - 202.0296		) < 1E-2
						assert abs(result_mean.item() - 0.2631		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 1, 0]

						scheduler.set_timesteps(timesteps=lowerCamelCase_		)

						a					     =   scheduler.timesteps

						for i, timestep in enumerate(lowerCamelCase_		):
											if i == len(lowerCamelCase_		) - 1:
																a					     =   -1
											else:
																a					     =   timesteps[i + 1]

											a					     =   scheduler.previous_timestep(lowerCamelCase_		)
											a					     =   prev_t.item()

											self.assertEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 51, 0]

						with self.assertRaises(lowerCamelCase_		,       msg="`custom_timesteps` must be in descending order."		):
											scheduler.set_timesteps(timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 1, 0]
						a					     =   len(lowerCamelCase_		)

						with self.assertRaises(lowerCamelCase_		,       msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."		):
											scheduler.set_timesteps(num_inference_steps=lowerCamelCase_		,       timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    lowerCamelCase_		,       msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"		,       ):
											scheduler.set_timesteps(timesteps=lowerCamelCase_		)
