
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       (DDPMScheduler,)



		def __UpperCamelCase				(				self       ,   **A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  {
				    'num_train_timesteps': 1_000,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'variance_type': 'fixed_small',
				    'clip_sample': True,
				}

				config.update(**A_ )
				return config



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				for timesteps in [1, 5, 100, 1_000]:
						self.check_over_configs(num_train_timesteps=A_ )



		def __UpperCamelCase				(				self )						->      str:
				"""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=A_       ,   beta_end=A_ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				for schedule in ["linear", "squaredcos_cap_v2"]:
						self.check_over_configs(beta_schedule=A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				for variance in ["fixed_small", "fixed_large", "other"]:
						self.check_over_configs(variance_type=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				for clip_sample in [True, False]:
						self.check_over_configs(clip_sample=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				self.check_over_configs(thresholding=A_ )
				for threshold in [0.5, 1.0, 2.0]:
						for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(
								    thresholding=A_       ,   prediction_type=A_       ,   sample_max_value=A_       ,   )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				for prediction_type in ["epsilon", "sample", "v_prediction"]:
						self.check_over_configs(prediction_type=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				for t in [0, 500, 999]:
						self.check_over_forward(time_step=A_ )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				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 )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

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

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

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

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

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

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



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config(prediction_type='v_prediction' )
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

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

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

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

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

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

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



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

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

				scheduler.set_timesteps(timesteps=A_ )

				UpperCamelCase						=  scheduler.timesteps

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

						UpperCamelCase						=  scheduler.previous_timestep(A_ )
						UpperCamelCase						=  prev_t.item()

						self.assertEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

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

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



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]
				UpperCamelCase						=  len(A_ )

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



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [scheduler.config.num_train_timesteps]

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


import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

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


_UpperCAmelCase      :				Dict          = logging.get_logger(__name__)


_UpperCAmelCase      :				Optional[Any]          = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_UpperCAmelCase      :				str          = {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

_UpperCAmelCase      :				List[str]          = {"facebook/blenderbot_small-90M": 512}


def    A (      lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  set()
		UpperCamelCase						=  word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)				)
				UpperCamelCase						=  char

		UpperCamelCase						=  set(lowercase				)
		return pairs


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Optional[Any]			  =       VOCAB_FILES_NAMES
		__lowercase : Tuple			  =       PRETRAINED_VOCAB_FILES_MAP
		__lowercase : List[Any]			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase : Any			  =       ["input_ids", "attention_mask"]



		def __init__(				self       ,   A_       ,   A_       ,   A_="__start__"       ,   A_="__end__"       ,   A_="__unk__"       ,   A_="__null__"       ,   **A_       ,   )						->      List[Any]:
				"""simple docstring"""
				super().__init__(unk_token=A_       ,   bos_token=A_       ,   eos_token=A_       ,   pad_token=A_       ,   **A_ )

				with open(A_       ,   encoding='utf-8' ) as vocab_handle:
						UpperCamelCase						=  json.load(A_ )
				UpperCamelCase						=  {v: k for k, v in self.encoder.items()}
				with open(A_       ,   encoding='utf-8' ) as merges_handle:
						UpperCamelCase						=  merges_handle.read().split('\n' )[1:-1]
				UpperCamelCase						=  [tuple(merge.split() ) for merge in merges]
				UpperCamelCase						=  dict(zip(A_       ,   range(len(A_ ) ) ) )
				UpperCamelCase						=  {}



		@property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return len(self.encoder )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				return dict(self.encoder       ,   **self.added_tokens_encoder )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				if token in self.cache:
						return self.cache[token]
				UpperCamelCase						=  re.sub('([.,!?()])'       ,   r' \1'       ,   A_ )
				UpperCamelCase						=  re.sub('(\')'       ,   r' \1 '       ,   A_ )
				UpperCamelCase						=  re.sub(r'\s{2,}'       ,   ' '       ,   A_ )
				if "\n" in token:
						UpperCamelCase						=  token.replace('\n'       ,   ' __newln__' )

				UpperCamelCase						=  token.split(' ' )
				UpperCamelCase						=  []
				for token in tokens:
						if not len(A_ ):
								continue

						UpperCamelCase						=  token.lower()
						UpperCamelCase						=  tuple(A_ )
						UpperCamelCase						=  tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
						UpperCamelCase						=  get_pairs(A_ )

						if not pairs:
								words.append(A_ )
								continue

						while True:
								UpperCamelCase						=  min(A_       ,   key=lambda A_ : self.bpe_ranks.get(A_       ,   float('inf' ) ) )
								if bigram not in self.bpe_ranks:
										break
								UpperCamelCase			,						UpperCamelCase						=  bigram
								UpperCamelCase						=  []
								UpperCamelCase						=  0

								while i < len(A_ ):
										try:
												UpperCamelCase						=  word.index(A_       ,   A_ )
												new_word.extend(word[i:j] )
												UpperCamelCase						=  j
										except ValueError:
												new_word.extend(word[i:] )
												break

										if word[i] == first and i < len(A_ ) - 1 and word[i + 1] == second:
												new_word.append(first + second )
												i += 2
										else:
												new_word.append(word[i] )
												i += 1
								UpperCamelCase						=  tuple(A_ )
								UpperCamelCase						=  new_word
								if len(A_ ) == 1:
										break
								else:
										UpperCamelCase						=  get_pairs(A_ )
						UpperCamelCase						=  '@@ '.join(A_ )
						UpperCamelCase						=  word[:-4]

						UpperCamelCase						=  word
						words.append(A_ )
				return " ".join(A_ )



		def __UpperCamelCase				(				self       ,   A_ )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  []

				UpperCamelCase						=  re.findall(r'\S+\n?'       ,   A_ )

				for token in words:
						split_tokens.extend(list(self.bpe(A_ ).split(' ' ) ) )
				return split_tokens



		def __UpperCamelCase				(				self       ,   A_ )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  token.lower()
				return self.encoder.get(A_       ,   self.encoder.get(self.unk_token ) )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				return self.decoder.get(A_       ,   self.unk_token )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  ' '.join(A_ ).replace('@@ '       ,   '' ).strip()
				return out_string



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      Tuple[str]:
				"""simple docstring"""
				if not os.path.isdir(A_ ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
						return
				UpperCamelCase						=  os.path.join(
				    A_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
				UpperCamelCase						=  os.path.join(
				    A_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

				with open(A_       ,   'w'       ,   encoding='utf-8' ) as f:
						f.write(json.dumps(self.encoder       ,   indent=2       ,   sort_keys=A_       ,   ensure_ascii=A_ ) + '\n' )

				UpperCamelCase						=  0
				with open(A_       ,   'w'       ,   encoding='utf-8' ) as writer:
						writer.write('#version: 0.2\n' )
						for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,   key=lambda A_ : kv[1] ):
								if index != token_index:
										logger.warning(
										    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
										    ' Please check that the tokenizer is not corrupted!' )
										UpperCamelCase						=  token_index
								writer.write(' '.join(A_ ) + '\n' )
								index += 1

				return vocab_file, merge_file
