
"""simple docstring"""


import json
import os
from typing import Optional, Tuple

import regex as re

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


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_lowercase							   =				{
    '''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
    '''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}

_lowercase							   =				{
    '''ctrl''': 2_56,
}

_lowercase							   =				{
    '''Pregnancy''': 16_86_29,
    '''Christianity''': 76_75,
    '''Explain''': 10_64_23,
    '''Fitness''': 6_34_40,
    '''Saving''': 6_31_63,
    '''Ask''': 2_71_71,
    '''Ass''': 9_59_85,
    '''Joke''': 16_35_09,
    '''Questions''': 4_56_22,
    '''Thoughts''': 4_96_05,
    '''Retail''': 5_23_42,
    '''Feminism''': 16_43_38,
    '''Writing''': 1_19_92,
    '''Atheism''': 19_22_63,
    '''Netflix''': 4_86_16,
    '''Computing''': 3_96_39,
    '''Opinion''': 4_32_13,
    '''Alone''': 4_49_67,
    '''Funny''': 5_89_17,
    '''Gaming''': 4_03_58,
    '''Human''': 40_88,
    '''India''': 13_31,
    '''Joker''': 7_71_38,
    '''Diet''': 3_62_06,
    '''Legal''': 1_18_59,
    '''Norman''': 49_39,
    '''Tip''': 7_26_89,
    '''Weight''': 5_23_43,
    '''Movies''': 4_62_73,
    '''Running''': 2_34_25,
    '''Science''': 20_90,
    '''Horror''': 3_77_93,
    '''Confession''': 6_05_72,
    '''Finance''': 1_22_50,
    '''Politics''': 1_63_60,
    '''Scary''': 19_19_85,
    '''Support''': 1_26_54,
    '''Technologies''': 3_25_16,
    '''Teenage''': 6_61_60,
    '''Event''': 3_27_69,
    '''Learned''': 6_74_60,
    '''Notion''': 18_27_70,
    '''Wikipedia''': 3_75_83,
    '''Books''': 66_65,
    '''Extract''': 7_60_50,
    '''Confessions''': 10_27_01,
    '''Conspiracy''': 7_59_32,
    '''Links''': 6_36_74,
    '''Narcissus''': 15_04_25,
    '''Relationship''': 5_47_66,
    '''Relationships''': 13_47_96,
    '''Reviews''': 4_16_71,
    '''News''': 42_56,
    '''Translation''': 2_68_20,
    '''multilingual''': 12_84_06,
}






def       _snake_case				(		snake_case__   :     Optional[Any]     ):
						A						   =   set()
						A						   =   word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)     )
												A						   =   char

						A						   =   set(SCREAMING_SNAKE_CASE_     )
						return pairs







class 				lowerCAmelCase_				(      _UpperCAmelCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[Any]      =    VOCAB_FILES_NAMES
						_lowerCamelCase:    Dict      =    PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:    Optional[Any]      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:    Union[str, Any]      =    CONTROL_CODES


						def __init__( self     :      Optional[Any]  ,A_     :      int  ,A_     :      str  ,A_     :      int="<unk>"  ,**A_     :      Union[str, Any]      )     ->   Optional[int]:
												super().__init__(unk_token=lowercase__  ,**lowercase__      )

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


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[Any]:
												return len(self.encoder      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[str]:
												return dict(self.encoder  ,**self.added_tokens_encoder      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      Dict      )     ->   Any:






												if token in self.cache:
																		return self.cache[token]
												A						   =   tuple(lowercase__      )
												A						   =   tuple(list(word[:-1]      ) + [word[-1] + '</w>']      )
												A						   =   get_pairs(lowercase__      )

												if not pairs:
																		return token

												while True:
																		A						   =   min(lowercase__  ,key=lambda A_      : self.bpe_ranks.get(lowercase__  ,float('inf'      )      )      )
																		if bigram not in self.bpe_ranks:
																								break
																		A						   =   bigram
																		A						   =   []
																		A						   =   0
																		while i < len(lowercase__      ):
																								try:
																														A						   =   word.index(lowercase__  ,lowercase__      )
																								except ValueError:
																														new_word.extend(word[i:]      )
																														break
																								else:
																														new_word.extend(word[i:j]      )
																														A						   =   j

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      int      )     ->   Union[str, Any]:
												A						   =   []

												A						   =   re.findall(R'\S+\n?'  ,lowercase__      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      List[Any]      )     ->   Any:
												return self.encoder.get(lowercase__  ,self.encoder.get(self.unk_token      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]      )     ->   List[str]:
												return self.decoder.get(lowercase__  ,self.unk_token      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      str      )     ->   Optional[int]:
												A						   =   " ".join(lowercase__      ).replace('@@ '  ,''      ).strip()
												return out_string




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Union[str, Any]  ,A_     :      Union[str, Any] = None      )     ->   Dict:






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

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

												A						   =   0
												with open(lowercase__  ,'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!'      )
																														A						   =   token_index
																								writer.write(' '.join(lowercase__      ) + '\n'      )
																								index += 1

												return vocab_file, merge_file

						# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
						#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
						#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
						#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
						#     return ''.join(tokens_generated_so_far)

"""simple docstring"""


from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_lowercase							   =				8






def       _snake_case				(		snake_case__   :     Tuple	,      snake_case__   :     Optional[int]=BITS     ):
	A						   =   x.device

	A						   =   (x * 255).int().clamp(0	,      255     )

	A						   =   2 ** torch.arange(bits - 1	,      -1	,      -1	,      device=snake_case__     )
	A						   =   rearrange(snake_case__	,      'd -> d 1 1'     )
	A						   =   rearrange(snake_case__	,      'b c h w -> b c 1 h w'     )

	A						   =   ((x & mask) != 0).float()
	A						   =   rearrange(snake_case__	,      'b c d h w -> b (c d) h w'     )
	A						   =   bits * 2 - 1
	return bits






def       _snake_case				(		snake_case__   :     Any	,      snake_case__   :     Any=BITS     ):
	A						   =   x.device

	A						   =   (x > 0).int()
	A						   =   2 ** torch.arange(bits - 1	,      -1	,      -1	,      device=snake_case__	,      dtype=torch.intaa     )

	A						   =   rearrange(snake_case__	,      'd -> d 1 1'     )
	A						   =   rearrange(snake_case__	,      'b (c d) h w -> b c d h w'	,      d=8     )
	A						   =   reduce(x * mask	,      'b c d h w -> b c h w'	,      'sum'     )
	return (dec / 255).clamp(0.0	,      1.0     )






def       _snake_case				(		self   :     Optional[int]	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     int	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     float = 0.0	,      snake_case__   :     bool = True	,      snake_case__   :     List[str]=None	,      snake_case__   :     bool = True	,      ):
	if self.num_inference_steps is None:
		raise ValueError(
		    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'     )

	# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
	# Ideally, read DDIM paper in-detail understanding

	# Notation (<variable name> -> <name in paper>
	# - pred_noise_t -> e_theta(x_t, t)
	# - pred_original_sample -> f_theta(x_t, t) or x_0
	# - std_dev_t -> sigma_t
	# - eta -> η
	# - pred_sample_direction -> "direction pointing to x_t"
	# - pred_prev_sample -> "x_t-1"

	# 1. get previous step value (=t-1)
	A						   =   timestep - self.config.num_train_timesteps // self.num_inference_steps

	# 2. compute alphas, betas
	A						   =   self.alphas_cumprod[timestep]
	A						   =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

	A						   =   1 - alpha_prod_t

	# 3. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	A						   =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

	# 4. Clip "predicted x_0"
	A						   =   self.bit_scale
	if self.config.clip_sample:
		A						   =   torch.clamp(snake_case__	,      -scale	,      snake_case__     )

	# 5. compute variance: "sigma_t(η)" -> see formula (16)
	# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
	A						   =   self._get_variance(snake_case__	,      snake_case__     )
	A						   =   eta * variance ** 0.5

	if use_clipped_model_output:
		# the model_output is always re-derived from the clipped x_0 in Glide
		A						   =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

	# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	A						   =   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

	# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	A						   =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

	if eta > 0:
		# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
		A						   =   model_output.device if torch.is_tensor(snake_case__     ) else 'cpu'
		A						   =   torch.randn(model_output.shape	,      dtype=model_output.dtype	,      generator=snake_case__     ).to(snake_case__     )
		A						   =   self._get_variance(snake_case__	,      snake_case__     ) ** 0.5 * eta * noise

		A						   =   prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=snake_case__	,      pred_original_sample=snake_case__     )






def       _snake_case				(		self   :     Dict	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     int	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     Tuple="epsilon"	,      snake_case__   :     List[str]=None	,      snake_case__   :     bool = True	,      ):
	A						   =   timestep

	if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
		A   ,      A						   =   torch.split(snake_case__	,      sample.shape[1]	,      dim=1     )
	else:
		A						   =   None

	# 1. compute alphas, betas
	A						   =   self.alphas_cumprod[t]
	A						   =   self.alphas_cumprod[t - 1] if t > 0 else self.one
	A						   =   1 - alpha_prod_t
	A						   =   1 - alpha_prod_t_prev

	# 2. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
	if prediction_type == "epsilon":
		A						   =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		A						   =   model_output
	else:
		raise ValueError(F'Unsupported prediction_type {prediction_type}.'     )

	# 3. Clip "predicted x_0"
	A						   =   self.bit_scale
	if self.config.clip_sample:
		A						   =   torch.clamp(snake_case__	,      -scale	,      snake_case__     )

	# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	A						   =   (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
	A						   =   self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

	# 5. Compute predicted previous sample µ_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	A						   =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

	# 6. Add noise
	A						   =   0
	if t > 0:
		A						   =   torch.randn(
		    model_output.size()	,      dtype=model_output.dtype	,      layout=model_output.layout	,      generator=snake_case__     ).to(model_output.device     )
		A						   =   (self._get_variance(snake_case__	,      predicted_variance=snake_case__     ) ** 0.5) * noise

	A						   =   pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=snake_case__	,      pred_original_sample=snake_case__     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      Union[str, Any]  ,A_     :      UNetaDConditionModel  ,A_     :      Union[DDIMScheduler, DDPMScheduler]  ,A_     :      Optional[float] = 1.0  ,)     ->   Optional[int]:
							super().__init__()
							A						   =   bit_scale
							A						   =   (
							    ddim_bit_scheduler_step if isinstance(A_  ,A_      ) else ddpm_bit_scheduler_step
							)

							self.register_modules(unet=A_  ,scheduler=A_      )




						@torch.no_grad()
						def __call__( self     :      Tuple  ,A_     :      Optional[int] = 256  ,A_     :      Optional[int] = 256  ,A_     :      Optional[int] = 50  ,A_     :      Optional[torch.Generator] = None  ,A_     :      Optional[int] = 1  ,A_     :      Optional[str] = "pil"  ,A_     :      bool = True  ,**A_     :      Optional[Any]  ,)     ->   Union[Tuple, ImagePipelineOutput]:
							A						   =   torch.randn(
							    (batch_size, self.unet.config.in_channels, height, width)  ,generator=A_  ,)
							A						   =   decimal_to_bits(A_      ) * self.bit_scale
							A						   =   latents.to(self.device      )

							self.scheduler.set_timesteps(A_      )

							for t in self.progress_bar(self.scheduler.timesteps      ):
								# predict the noise residual
								A						   =   self.unet(A_  ,A_      ).sample

								# compute the previous noisy sample x_t -> x_t-1
								A						   =   self.scheduler.step(A_  ,A_  ,A_      ).prev_sample

							A						   =   bits_to_decimal(A_      )

							if output_type == "pil":
								A						   =   self.numpy_to_pil(A_      )

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=A_      )