
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase       =					logging.get_logger(__name__)

UpperCAmelCase       =					'''▁'''

UpperCAmelCase       =					{'''vocab_file''': '''prophetnet.tokenizer'''}

UpperCAmelCase       =					{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

UpperCAmelCase       =					{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

UpperCAmelCase       =					{
    '''microsoft/xprophetnet-large-wiki100-cased''': 512,
}






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       collections.OrderedDict()
	with open(__SCREAMING_SNAKE_CASE					,				'r'					,				encoding='utf-8'				) as reader:
		lowercase	    =       reader.readlines()
	for index, token in enumerate(__SCREAMING_SNAKE_CASE				):
		lowercase	    =       token.rstrip('\n'				)
		lowercase	    =       index
	return vocab







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Tuple									=			VOCAB_FILES_NAMES
	_UpperCamelCase	:	Optional[Any]									=			PRETRAINED_VOCAB_FILES_MAP
	_UpperCamelCase	:	Dict									=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCamelCase	:	Tuple									=			["""input_ids""", """attention_mask"""]





	def __init__(			self						,       snake_case						,       snake_case="[SEP]"						,       snake_case="[SEP]"						,       snake_case="[SEP]"						,       snake_case="[UNK]"						,       snake_case="[PAD]"						,       snake_case="[CLS]"						,       snake_case="[MASK]"						,       snake_case = None						,       **snake_case						,       ):
		lowercase	    =       {} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    bos_token=snake_case						,       eos_token=snake_case						,       sep_token=snake_case						,       unk_token=snake_case						,       pad_token=snake_case						,       cls_token=snake_case						,       mask_token=snake_case						,       sp_model_kwargs=self.sp_model_kwargs						,       **snake_case						,       )

		try:
			import sentencepiece as spm
		except ImportError:
			logger.warning(
			    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
			    ' pip install sentencepiece'			)
			raise

		lowercase	    =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
		self.sp_model.Load(str(snake_case			)			)
		lowercase	    =       vocab_file

		# Original fairseq vocab and spm vocab must be "aligned":
		# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
		# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
		# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
		# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

		# put special tokens and [unused] tokens into the vocab
		lowercase	    =       {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

		for i in range(10			):
			lowercase	    =       F'''[unused{i}]'''
			lowercase	    =       5 + i

		# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
		lowercase	    =       12
		lowercase	    =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
		for k in self.fairseq_tokens_to_ids.keys():
			self.unique_no_split_tokens.append(snake_case			)





	def __getstate__(			self			):
		lowercase	    =       self.__dict__.copy()
		lowercase	    =       None
		return state





	def __setstate__(			self						,       snake_case			):
		lowercase	    =       d
		try:
			import sentencepiece as spm
		except ImportError:
			logger.warning(
			    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
			    ' pip install sentencepiece'			)
			raise

		# for backward compatibility
		if not hasattr(self						,       'sp_model_kwargs'			):
			lowercase	    =       {}

		lowercase	    =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
		self.sp_model.Load(self.vocab_file			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None						,       snake_case = False			):

		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=snake_case						,       token_ids_a=snake_case						,       already_has_special_tokens=snake_case			)

		if token_ids_a is None:
			return ([0] * len(snake_case			)) + [1]
		return ([0] * len(snake_case			)) + [1] + ([0] * len(snake_case			)) + [1]





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):
		lowercase	    =       [self.sep_token_id]

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





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return len(self.sp_model			) + self.fairseq_offset





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       {self.convert_ids_to_tokens(snake_case			): i for i in range(self.vocab_size			)}
		vocab.update(self.added_tokens_encoder			)
		return vocab





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		return self.sp_model.encode(snake_case						,       out_type=snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		if token in self.fairseq_tokens_to_ids:
			return self.fairseq_tokens_to_ids[token]
		lowercase	    =       self.sp_model.PieceToId(snake_case			)

		# Need to return unknown token if the SP model returned 0
		return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		if index in self.fairseq_ids_to_tokens:
			return self.fairseq_ids_to_tokens[index]
		return self.sp_model.IdToPiece(index - self.fairseq_offset			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       ''.join(snake_case			).replace(snake_case						,       ' '			).strip()
		return out_string





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):
		if not os.path.isdir(snake_case			):
			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
			return
		lowercase	    =       os.path.join(
		    snake_case						,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

		if os.path.abspath(self.vocab_file			) != os.path.abspath(snake_case			) and os.path.isfile(self.vocab_file			):
			copyfile(self.vocab_file						,       snake_case			)
		elif not os.path.isfile(self.vocab_file			):
			with open(snake_case						,       'wb'			) as fi:
				lowercase	    =       self.sp_model.serialized_model_proto()
				fi.write(snake_case			)

		return (out_vocab_file,)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):

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



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			int						=		(KDPMaDiscreteScheduler,)
						UpperCamelCase_				:			Optional[int]						=		10

						def 			A_					(      self							,				**a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						{
								    """num_train_timesteps""": 11_00,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**a					)
								return config

						def 			A_					(      self					)       ->   List[str]:


								'''simple docstring'''

								for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a					)

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,				[0.0002, 0.002, 0.02]					):
										self.check_over_configs(beta_start=a							,				beta_end=a					)

						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

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

						def 			A_					(      self					)       ->   Optional[int]:


								'''simple docstring'''

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

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

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

								scheduler.set_timesteps(self.num_inference_steps					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter * scheduler.init_noise_sigma
								_UpperCamelCase         =						sample.to(a					)

								for i, t in enumerate(scheduler.timesteps					):
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

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

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 4.6934e-07					) < 1e-2
										assert abs(result_mean.item() - 6.1112e-10					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 4.693_4286_5017_0972e-07					) < 1e-2
										assert abs(result_mean.item() - 0.0002					) < 1e-3

						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								if torch_device == "mps":
										return
								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config()
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter * scheduler.init_noise_sigma
								_UpperCamelCase         =						sample.to(a					)

								for i, t in enumerate(scheduler.timesteps					):
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

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

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3







						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								if torch_device == "mps":
										return
								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config()
								_UpperCamelCase         =						scheduler_class(**a					)

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

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter.to(a					) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

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

								if str(a					).startswith("""cpu"""					):
										# The following sum varies between 148 and 156 on mps. Why?
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3



