






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 						__UpperCAmelCase			(     a_							,     a_=BITS):
							snake_case_				=							x.device

							snake_case_				=							(x * 2_55).int().clamp(0							,     2_55)

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

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





def 						__UpperCAmelCase			(     a_							,     a_=BITS):
							snake_case_				=							x.device

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

							snake_case_				=							rearrange(a_							,     'd -> d 1 1')
							snake_case_				=							rearrange(a_							,     'b (c d) h w -> b c d h w'							,     d=8)
							snake_case_				=							reduce(x * mask							,     'b c d h w -> b c h w'							,     'sum')
							return (dec / 2_55).clamp(0.0							,     1.0)





def 						__UpperCAmelCase			(     self							,     a_							,     a_							,     a_							,     a_ = 0.0							,     a_ = True							,     a_=None							,     a_ = 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)
							snake_case_				=							timestep - self.config.num_train_timesteps // self.num_inference_steps

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

							snake_case_				=							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
							snake_case_				=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

							# 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)
							snake_case_				=							self._get_variance(a_							,     a_)
							snake_case_				=							eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														snake_case_				=							(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
							snake_case_				=							(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
							snake_case_				=							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
														snake_case_				=							model_output.device if torch.is_tensor(a_) else 'cpu'
														snake_case_				=							torch.randn(model_output.shape							,     dtype=model_output.dtype							,     generator=a_).to(a_)
														snake_case_				=							self._get_variance(a_							,     a_) ** 0.5 * eta * noise

														snake_case_				=							prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=a_							,     pred_original_sample=a_)





def 						__UpperCAmelCase			(     self							,     a_							,     a_							,     a_							,     a_="epsilon"							,     a_=None							,     a_ = True							,     ):
							snake_case_				=							timestep

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

							# 1. compute alphas, betas
							snake_case_				=							self.alphas_cumprod[t]
							snake_case_				=							self.alphas_cumprod[t - 1] if t > 0 else self.one
							snake_case_				=							1 - alpha_prod_t
							snake_case_				=							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":
														snake_case_				=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														snake_case_				=							model_output
							else:
														raise ValueError(f'''Unsupported prediction_type {prediction_type}.''')

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

							# 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
							snake_case_				=							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							snake_case_				=							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
							snake_case_				=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

							snake_case_				=							pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=a_							,     pred_original_sample=a_)




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a       ,			a = 1.0       ,			)       ->    str:
								super().__init__()
								snake_case_				=							bit_scale
								snake_case_				=							(
								    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       ,			a = 2_56       ,			a = 2_56       ,			a = 50       ,			a = None       ,			a = 1       ,			a = "pil"       ,			a = True       ,			**a       ,			)       ->    Union[Tuple, ImagePipelineOutput]:
								snake_case_				=							torch.randn(
								    (batch_size, self.unet.config.in_channels, height, width)       ,			generator=a       ,			)
								snake_case_				=							decimal_to_bits(a					) * self.bit_scale
								snake_case_				=							latents.to(self.device					)

								self.scheduler.set_timesteps(a					)

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

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

								snake_case_				=							bits_to_decimal(a					)

								if output_type == "pil":
															snake_case_				=							self.numpy_to_pil(a					)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a					)













import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase  							=					{
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

lowercase  							=					{
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


lowercase  							=					{
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase													= SqueezeBertTokenizer




	def __init__(       self       ,			a=None       ,			a=None       ,			a=True       ,			a="[UNK]"       ,			a="[SEP]"       ,			a="[PAD]"       ,			a="[CLS]"       ,			a="[MASK]"       ,			a=True       ,			a=None       ,			**a       ,			)       ->    Tuple:
								super().__init__(
								    a       ,			tokenizer_file=a       ,			do_lower_case=a       ,			unk_token=a       ,			sep_token=a       ,			pad_token=a       ,			cls_token=a       ,			mask_token=a       ,			tokenize_chinese_chars=a       ,			strip_accents=a       ,			**a       ,			)

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

								snake_case_				=							do_lower_case




	def 							_UpperCamelCase			(       self       ,			a       ,			a=None					)       ->    Tuple:
								snake_case_				=							[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       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[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       ,			a       ,			a = None					)       ->    Tuple[str]:
								snake_case_				=							self._tokenizer.model.save(a       ,			name=a					)
								return tuple(a					)





