
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 	A__   (						UpperCAmelCase__				):
							lowerCAmelCase__		:						int    			=							(PNDMScheduler,)
							lowerCAmelCase__		:						List[Any]    			=							(("num_inference_steps", 50),)





							def 			a__ ( self  :   Any     , **_UpperCAmelCase  :   Optional[Any]				)						->		Union[str, Any]:



											"""simple docstring"""


											__lowercase								=					{
											    '''num_train_timesteps''': 10_00,
											    '''beta_start''': 0.0_001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											}

											config.update(**lowercase_				)
											return config





							def 			a__ ( self  :   Optional[Any]     , _UpperCAmelCase  :   List[str]=0     , **_UpperCAmelCase  :   Union[str, Any]				)						->		Dict:



											"""simple docstring"""


											__lowercase								=					dict(self.forward_default_kwargs				)
											__lowercase								=					kwargs.pop('num_inference_steps'     , lowercase_				)
											__lowercase								=					self.dummy_sample
											__lowercase								=					0.1 * sample
											__lowercase								=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

															__lowercase								=					scheduler.step_prk(lowercase_     , lowercase_     , lowercase_     , **lowercase_				).prev_sample
															__lowercase								=					new_scheduler.step_prk(lowercase_     , lowercase_     , lowercase_     , **lowercase_				).prev_sample

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

															__lowercase								=					scheduler.step_plms(lowercase_     , lowercase_     , lowercase_     , **lowercase_				).prev_sample
															__lowercase								=					new_scheduler.step_plms(lowercase_     , lowercase_     , lowercase_     , **lowercase_				).prev_sample

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





							def 			a__ ( self  :   Any				)						->		Optional[int]:



											"""simple docstring"""


											pass





							def 			a__ ( self  :   List[Any]     , _UpperCAmelCase  :   List[str]=0     , **_UpperCAmelCase  :   List[str]				)						->		Tuple:



											"""simple docstring"""


											__lowercase								=					dict(self.forward_default_kwargs				)
											__lowercase								=					kwargs.pop('num_inference_steps'     , lowercase_				)
											__lowercase								=					self.dummy_sample
											__lowercase								=					0.1 * sample
											__lowercase								=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
															__lowercase								=					self.get_scheduler_config()
															__lowercase								=					scheduler_class(**lowercase_				)
															scheduler.set_timesteps(lowercase_				)

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

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

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

															__lowercase								=					scheduler.step_prk(lowercase_     , lowercase_     , lowercase_     , **lowercase_				).prev_sample
															__lowercase								=					new_scheduler.step_prk(lowercase_     , lowercase_     , lowercase_     , **lowercase_				).prev_sample

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

															__lowercase								=					scheduler.step_plms(lowercase_     , lowercase_     , lowercase_     , **lowercase_				).prev_sample
															__lowercase								=					new_scheduler.step_plms(lowercase_     , lowercase_     , lowercase_     , **lowercase_				).prev_sample

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





							def 			a__ ( self  :   str     , **_UpperCAmelCase  :   str				)						->		Any:



											"""simple docstring"""


											__lowercase								=					self.scheduler_classes[0]
											__lowercase								=					self.get_scheduler_config(**lowercase_				)
											__lowercase								=					scheduler_class(**lowercase_				)

											__lowercase								=					10
											__lowercase								=					self.dummy_model()
											__lowercase								=					self.dummy_sample_deter
											scheduler.set_timesteps(lowercase_				)

											for i, t in enumerate(scheduler.prk_timesteps				):
															__lowercase								=					model(lowercase_     , lowercase_				)
															__lowercase								=					scheduler.step_prk(lowercase_     , lowercase_     , lowercase_				).prev_sample

											for i, t in enumerate(scheduler.plms_timesteps				):
															__lowercase								=					model(lowercase_     , lowercase_				)
															__lowercase								=					scheduler.step_plms(lowercase_     , lowercase_     , lowercase_				).prev_sample

											return sample





							def 			a__ ( self  :   Dict				)						->		str:



											"""simple docstring"""


											__lowercase								=					dict(self.forward_default_kwargs				)

											__lowercase								=					kwargs.pop('num_inference_steps'     , lowercase_				)

											for scheduler_class in self.scheduler_classes:
															__lowercase								=					self.get_scheduler_config()
															__lowercase								=					scheduler_class(**lowercase_				)

															__lowercase								=					self.dummy_sample
															__lowercase								=					0.1 * sample

															if num_inference_steps is not None and hasattr(lowercase_     , 'set_timesteps'				):
																			scheduler.set_timesteps(lowercase_				)
															elif num_inference_steps is not None and not hasattr(lowercase_     , '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 + 0.1, residual + 0.05]
															__lowercase								=					dummy_past_residuals[:]

															__lowercase								=					scheduler.step_prk(lowercase_     , 0     , lowercase_     , **lowercase_				).prev_sample
															__lowercase								=					scheduler.step_prk(lowercase_     , 1     , lowercase_     , **lowercase_				).prev_sample

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

															__lowercase								=					scheduler.step_plms(lowercase_     , 0     , lowercase_     , **lowercase_				).prev_sample
															__lowercase								=					scheduler.step_plms(lowercase_     , 1     , lowercase_     , **lowercase_				).prev_sample

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





							def 			a__ ( self  :   str				)						->		List[Any]:



											"""simple docstring"""


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





							def 			a__ ( self  :   Dict				)						->		Any:



											"""simple docstring"""


											for steps_offset in [0, 1]:
															self.check_over_configs(steps_offset=lowercase_				)

											__lowercase								=					self.scheduler_classes[0]
											__lowercase								=					self.get_scheduler_config(steps_offset=1				)
											__lowercase								=					scheduler_class(**lowercase_				)
											scheduler.set_timesteps(10				)
											assert torch.equal(
											    scheduler.timesteps     , torch.LongTensor(
											        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]				)     , )





							def 			a__ ( self  :   Any				)						->		Optional[int]:



											"""simple docstring"""


											for beta_start, beta_end in zip([0.0_001, 0.001]     , [0.002, 0.02]				):
															self.check_over_configs(beta_start=lowercase_     , beta_end=lowercase_				)





							def 			a__ ( self  :   List[Any]				)						->		int:



											"""simple docstring"""


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





							def 			a__ ( self  :   Any				)						->		Union[str, Any]:



											"""simple docstring"""


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





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



											"""simple docstring"""


											for t in [1, 5, 10]:
															self.check_over_forward(time_step=lowercase_				)





							def 			a__ ( self  :   Optional[Any]				)						->		Any:



											"""simple docstring"""


											for t, num_inference_steps in zip([1, 5, 10]     , [10, 50, 1_00]				):
															self.check_over_forward(num_inference_steps=lowercase_				)





							def 			a__ ( self  :   Any				)						->		List[str]:



											"""simple docstring"""


											__lowercase								=					27

											for scheduler_class in self.scheduler_classes:
															__lowercase								=					self.dummy_sample
															__lowercase								=					0.1 * sample

															__lowercase								=					self.get_scheduler_config()
															__lowercase								=					scheduler_class(**lowercase_				)

															scheduler.set_timesteps(lowercase_				)

															# before power of 3 fix, would error on first step, so we only need to do two
															for i, t in enumerate(scheduler.prk_timesteps[:2]				):
																			__lowercase								=					scheduler.step_prk(lowercase_     , lowercase_     , lowercase_				).prev_sample





							def 			a__ ( self  :   List[Any]				)						->		str:



											"""simple docstring"""


											with self.assertRaises(lowercase_				):
															__lowercase								=					self.scheduler_classes[0]
															__lowercase								=					self.get_scheduler_config()
															__lowercase								=					scheduler_class(**lowercase_				)

															scheduler.step_plms(self.dummy_sample     , 1     , self.dummy_sample				).prev_sample





							def 			a__ ( self  :   Optional[int]				)						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								=					self.full_loop()
											__lowercase								=					torch.sum(torch.abs(lowercase_				)				)
											__lowercase								=					torch.mean(torch.abs(lowercase_				)				)

											assert abs(result_sum.item() - 1_98.13_18				) < 1e-2
											assert abs(result_mean.item() - 0.2_580				) < 1e-3





							def 			a__ ( self  :   Optional[int]				)						->		str:



											"""simple docstring"""


											__lowercase								=					self.full_loop(prediction_type='v_prediction'				)
											__lowercase								=					torch.sum(torch.abs(lowercase_				)				)
											__lowercase								=					torch.mean(torch.abs(lowercase_				)				)

											assert abs(result_sum.item() - 67.3_986				) < 1e-2
											assert abs(result_mean.item() - 0.0_878				) < 1e-3





							def 			a__ ( self  :   Optional[Any]				)						->		Optional[int]:



											"""simple docstring"""


											__lowercase								=					self.full_loop(set_alpha_to_one=lowercase_     , beta_start=0.01				)
											__lowercase								=					torch.sum(torch.abs(lowercase_				)				)
											__lowercase								=					torch.mean(torch.abs(lowercase_				)				)

											assert abs(result_sum.item() - 2_30.03_99				) < 1e-2
											assert abs(result_mean.item() - 0.2_995				) < 1e-3





							def 			a__ ( self  :   Optional[Any]				)						->		List[Any]:



											"""simple docstring"""


											__lowercase								=					self.full_loop(set_alpha_to_one=lowercase_     , beta_start=0.01				)
											__lowercase								=					torch.sum(torch.abs(lowercase_				)				)
											__lowercase								=					torch.mean(torch.abs(lowercase_				)				)

											assert abs(result_sum.item() - 1_86.94_82				) < 1e-2
											assert abs(result_mean.item() - 0.2_434				) < 1e-3













"""simple docstring"""






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

import sentencepiece as spm

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


UpperCAmelCase_   : Dict   			= logging.get_logger(__name__)

UpperCAmelCase_   : Tuple   			= """▁"""

UpperCAmelCase_   : Optional[Any]   			= {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase_   : str   			= {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCAmelCase_   : str   			= {
    """facebook/xglm-564M""": 2048,
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  VOCAB_FILES_NAMES
      __UpperCamelCase	 =  PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase	 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase	 =  ["input_ids", "attention_mask"]
      def __init__(						self     :  List[Any]						,	lowercase_     :  str						,	lowercase_     :  Tuple="<s>"						,	lowercase_     :  Any="</s>"						,	lowercase_     :  Optional[int]="</s>"						,	lowercase_     :  List[Any]="<s>"						,	lowercase_     :  Union[str, Any]="<unk>"						,	lowercase_     :  Union[str, Any]="<pad>"						,	lowercase_     :  Optional[Dict[str, Any]] = None						,	**lowercase_     :  Tuple						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		{} if sp_model_kwargs is None else sp_model_kwargs

          # Compatibility with the original tokenizer
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		7
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		[F'<madeupword{i}>' for i in range(self.num_madeup_words)]

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kwargs.get('''additional_special_tokens'''						,	[])
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

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

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(str(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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'

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		1

          # Mimic fairseq token-to-id alignment for the first 4 token
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		len(self.sp_model)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words)}
          self.fairseq_tokens_to_ids.update(lowercase_)

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
      def __getstate__(						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.__dict__.copy()
          SCREAMING_SNAKE_CASE_      :  str    						=		None
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.sp_model.serialized_model_proto()
          return state
      def __setstate__(						self     :  Tuple						,	lowercase_     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		d

          # for backward compatibility
          if not hasattr(self						,	'''sp_model_kwargs'''):
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{}

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None):




          '''simple docstring'''


          if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
          SCREAMING_SNAKE_CASE_      :  Dict    						=		[self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None						,	lowercase_     :  bool = False):




          '''simple docstring'''


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

          if token_ids_a is None:
              return [1] + ([0] * len(lowercase_))
          return [1] + ([0] * len(lowercase_)) + [1, 1] + ([0] * len(lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[self.sep_token_id]

          if token_ids_a is None:
              return len(sep + token_ids_a) * [0]
          return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		{self.convert_ids_to_tokens(lowercase_): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab
      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  str):




          '''simple docstring'''


          return self.sp_model.encode(lowercase_						,	out_type=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.sp_model.PieceToId(lowercase_)

          # 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     :  int						,	lowercase_     :  Optional[Any]):




          '''simple docstring'''


          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     :  int						,	lowercase_     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		''''''.join(lowercase_).replace(lowercase_						,	''' ''').strip()
          return out_string



      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  Optional[str] = None):




          '''simple docstring'''


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

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

          return (out_vocab_file,)


