


"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      Any=0.999     ,   _snake_case						:      str="cosine"     ,   )     ->Optional[Any]:
	"""simple docstring"""





	if alpha_transform_type == "cosine":

		def alpha_bar_fn(_snake_case						:      Tuple     ):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(_snake_case						:      int     ):
			return math.exp(t * -12.0     )

	else:
		raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )

	__snake_case	:						Optional[int]						=				[]
	for i in range(_snake_case     ):
		__snake_case	:						Dict						=				i / num_diffusion_timesteps
		__snake_case	:						Dict						=				(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(_snake_case     ) / alpha_bar_fn(_snake_case     )     ,   _snake_case     )     )
	return torch.tensor(_snake_case     ,   dtype=torch.floataa     )



class 			_UpperCAmelCase (							lowercase_,				lowercase_				):






		'''simple docstring'''

		lowerCamelCase__				=[e.name for e in KarrasDiffusionSchedulers]
		lowerCamelCase__				=2



		@register_to_config
		def __init__(self				,       a_ = 10_00				,       a_ = 0.0_0085				,       a_ = 0.012				,       a_ = "linear"				,       a_ = None				,       a_ = "epsilon"				,       a_ = "linspace"				,       a_ = 0				,       ):





			'''simple docstring'''

			if trained_betas is not None:
				__snake_case	:						Optional[int]						=				torch.tensor(a_				,       dtype=torch.floataa					)
			elif beta_schedule == "linear":
				__snake_case	:						Any						=				torch.linspace(a_				,       a_				,       a_				,       dtype=torch.floataa					)
			elif beta_schedule == "scaled_linear":
				# this schedule is very specific to the latent diffusion model.
				__snake_case	:						Any						=				(
				    torch.linspace(beta_start**0.5				,       beta_end**0.5				,       a_				,       dtype=torch.floataa					) ** 2
				)
			elif beta_schedule == "squaredcos_cap_v2":
				# Glide cosine schedule
				__snake_case	:						Tuple						=				betas_for_alpha_bar(a_					)
			else:
				raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""					)

			__snake_case	:						Dict						=				1.0 - self.betas
			__snake_case	:						Tuple						=				torch.cumprod(self.alphas				,       dim=0					)

			#  set all values
			self.set_timesteps(a_				,       a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_=None					):





			'''simple docstring'''

			if schedule_timesteps is None:
				__snake_case	:						List[Any]						=				self.timesteps

			__snake_case	:						Dict						=				(schedule_timesteps == timestep).nonzero()

			# The sigma index that is taken for the **very** first `step`
			# is always the second index (or the last index if there is only 1)
			# This way we can ensure we don't accidentally skip a sigma in
			# case we start in the middle of the denoising schedule (e.g. for image-to-image)
			if len(self._index_counter					) == 0:
				__snake_case	:						List[str]						=				1 if len(a_					) > 1 else 0
			else:
				__snake_case	:						Union[str, Any]						=				timestep.cpu().item() if torch.is_tensor(a_					) else timestep
				__snake_case	:						Any						=				self._index_counter[timestep_int]

			return indices[pos].item()



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			if self.config.timestep_spacing in ["linspace", "trailing"]:
				return self.sigmas.max()

			return (self.sigmas.max() ** 2 + 1) ** 0.5



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       ):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				self.index_for_timestep(a_					)

			if self.state_in_first_order:
				__snake_case	:						List[str]						=				self.sigmas[step_index]
			else:
				__snake_case	:						Union[str, Any]						=				self.sigmas_interpol[step_index]

			__snake_case	:						Dict						=				sample / ((sigma**2 + 1) ** 0.5)
			return sample



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None				,       a_ = None				,       ):





			'''simple docstring'''

			__snake_case	:						List[str]						=				num_inference_steps

			__snake_case	:						Any						=				num_train_timesteps or self.config.num_train_timesteps

			# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
			if self.config.timestep_spacing == "linspace":
				__snake_case	:						List[Any]						=				np.linspace(0				,       num_train_timesteps - 1				,       a_				,       dtype=a_					)[::-1].copy()
			elif self.config.timestep_spacing == "leading":
				__snake_case	:						Optional[Any]						=				num_train_timesteps // self.num_inference_steps
				# creates integer timesteps by multiplying by ratio
				# casting to int to avoid issues when num_inference_step is power of 3
				__snake_case	:						Optional[int]						=				(np.arange(0				,       a_					) * step_ratio).round()[::-1].copy().astype(a_					)
				timesteps += self.config.steps_offset
			elif self.config.timestep_spacing == "trailing":
				__snake_case	:						Any						=				num_train_timesteps / self.num_inference_steps
				# creates integer timesteps by multiplying by ratio
				# casting to int to avoid issues when num_inference_step is power of 3
				__snake_case	:						List[str]						=				(np.arange(a_				,       0				,       -step_ratio					)).round().copy().astype(a_					)
				timesteps -= 1
			else:
				raise ValueError(
				    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""					)

			__snake_case	:						Tuple						=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
			__snake_case	:						str						=				torch.from_numpy(np.log(a_					)					).to(a_					)

			__snake_case	:						Union[str, Any]						=				np.interp(a_				,       np.arange(0				,       len(a_					)					)				,       a_					)
			__snake_case	:						List[str]						=				np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
			__snake_case	:						List[str]						=				torch.from_numpy(a_					).to(device=a_					)

			# interpolate sigmas
			__snake_case	:						Union[str, Any]						=				sigmas.log().lerp(sigmas.roll(1					).log()				,       0.5					).exp()

			__snake_case	:						List[Any]						=				torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
			__snake_case	:						int						=				torch.cat(
			    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)

			if str(a_					).startswith('''mps'''					):
				# mps does not support float64
				__snake_case	:						Optional[Any]						=				torch.from_numpy(a_					).to(a_				,       dtype=torch.floataa					)
			else:
				__snake_case	:						str						=				torch.from_numpy(a_					).to(a_					)

			# interpolate timesteps
			__snake_case	:						List[str]						=				self.sigma_to_t(a_					).to(a_				,       dtype=timesteps.dtype					)
			__snake_case	:						Optional[Any]						=				torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])				,       dim=-1					).flatten()

			__snake_case	:						Union[str, Any]						=				torch.cat([timesteps[:1], interleaved_timesteps]					)

			__snake_case	:						Dict						=				None

			# for exp beta schedules, such as the one for `pipeline_shap_e.py`
			# we need an index counter
			__snake_case	:						Optional[Any]						=				defaultdict(a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						Dict						=				sigma.log()

			# get distribution
			__snake_case	:						Optional[Any]						=				log_sigma - self.log_sigmas[:, None]

			# get sigmas range
			__snake_case	:						Optional[int]						=				dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
			__snake_case	:						Any						=				low_idx + 1

			__snake_case	:						List[Any]						=				self.log_sigmas[low_idx]
			__snake_case	:						List[Any]						=				self.log_sigmas[high_idx]

			# interpolate sigmas
			__snake_case	:						Union[str, Any]						=				(low - log_sigma) / (low - high)
			__snake_case	:						Dict						=				w.clamp(0				,       1					)

			# transform interpolation to time range
			__snake_case	:						str						=				(1 - w) * low_idx + w * high_idx
			__snake_case	:						str						=				t.view(sigma.shape					)
			return t



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.sample is None



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_ = True				,       ):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				self.index_for_timestep(a_					)

			# advance index counter by 1
			__snake_case	:						Dict						=				timestep.cpu().item() if torch.is_tensor(a_					) else timestep
			self._index_counter[timestep_int] += 1

			if self.state_in_first_order:
				__snake_case	:						str						=				self.sigmas[step_index]
				__snake_case	:						Dict						=				self.sigmas_interpol[step_index + 1]
				__snake_case	:						Tuple						=				self.sigmas[step_index + 1]
			else:
				# 2nd order / KDPM2's method
				__snake_case	:						int						=				self.sigmas[step_index - 1]
				__snake_case	:						Optional[Any]						=				self.sigmas_interpol[step_index]
				__snake_case	:						List[str]						=				self.sigmas[step_index]

			# currently only gamma=0 is supported. This usually works best anyways.
			# We can support gamma in the future but then need to scale the timestep before
			# passing it to the model which requires a change in API
			__snake_case	:						int						=				0
			__snake_case	:						str						=				sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

			# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
			if self.config.prediction_type == "epsilon":
				__snake_case	:						List[Any]						=				sigma_hat if self.state_in_first_order else sigma_interpol
				__snake_case	:						Optional[Any]						=				sample - sigma_input * model_output
			elif self.config.prediction_type == "v_prediction":
				__snake_case	:						str						=				sigma_hat if self.state_in_first_order else sigma_interpol
				__snake_case	:						Tuple						=				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
				    sample / (sigma_input**2 + 1)
				)
			elif self.config.prediction_type == "sample":
				raise NotImplementedError('''prediction_type not implemented yet: sample'''					)
			else:
				raise ValueError(
				    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""					)

			if self.state_in_first_order:
				# 2. Convert to an ODE derivative for 1st order
				__snake_case	:						Tuple						=				(sample - pred_original_sample) / sigma_hat
				# 3. delta timestep
				__snake_case	:						Union[str, Any]						=				sigma_interpol - sigma_hat

				# store for 2nd order step
				__snake_case	:						List[Any]						=				sample
			else:
				# DPM-Solver-2
				# 2. Convert to an ODE derivative for 2nd order
				__snake_case	:						List[str]						=				(sample - pred_original_sample) / sigma_interpol

				# 3. delta timestep
				__snake_case	:						Any						=				sigma_next - sigma_hat

				__snake_case	:						List[str]						=				self.sample
				__snake_case	:						Optional[Any]						=				None

			__snake_case	:						List[str]						=				sample + derivative * dt

			if not return_dict:
				return (prev_sample,)

			return SchedulerOutput(prev_sample=a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       ):





			'''simple docstring'''

			__snake_case	:						Tuple						=				self.sigmas.to(device=original_samples.device				,       dtype=original_samples.dtype					)
			if original_samples.device.type == "mps" and torch.is_floating_point(a_					):
				# mps does not support float64
				__snake_case	:						str						=				self.timesteps.to(original_samples.device				,       dtype=torch.floataa					)
				__snake_case	:						Optional[int]						=				timesteps.to(original_samples.device				,       dtype=torch.floataa					)
			else:
				__snake_case	:						Any						=				self.timesteps.to(original_samples.device					)
				__snake_case	:						List[Any]						=				timesteps.to(original_samples.device					)

			__snake_case	:						Optional[int]						=				[self.index_for_timestep(a_				,       a_					) for t in timesteps]

			__snake_case	:						Dict						=				sigmas[step_indices].flatten()
			while len(sigma.shape					) < len(original_samples.shape					):
				__snake_case	:						Tuple						=				sigma.unsqueeze(-1					)

			__snake_case	:						List[str]						=				original_samples + noise * sigma
			return noisy_samples



		def __len__(self					):





			'''simple docstring'''

			return self.config.num_train_timesteps









'''simple docstring'''

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_ta import TaTokenizer
else:
 lowerCamelCase							:   Any  						=	None


lowerCamelCase							:   Dict  						=	logging.get_logger(__name__)

lowerCamelCase							:   Optional[int]  						=	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase							:   List[str]  						=	{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase							:   Any  						=	{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[str]  					=							VOCAB_FILES_NAMES
      lowerCAmelCase__					:     str  					=							PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__					:     Optional[Any]  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__					:     int  					=							["""input_ids""", """attention_mask"""]
      lowerCAmelCase__					:     Optional[int]  					=							TaTokenizer

      lowerCAmelCase__					:     List[int]  					=							[]



      def __init__(self    :    Dict   ,						UpperCamelCase    :    str=None   ,						UpperCamelCase    :    Optional[Any]=None   ,						UpperCamelCase    :    Any="</s>"   ,						UpperCamelCase    :    str="<unk>"   ,						UpperCamelCase    :    List[str]="<pad>"   ,						UpperCamelCase    :    List[str]=100   ,						UpperCamelCase    :    Tuple=None   ,						**UpperCamelCase    :    List[str]   ,						):

             '''simple docstring'''







             if extra_ids > 0 and additional_special_tokens is None:
                    lowercase__      			= [f"<extra_id_{i}>" for i in range(UpperCamelCase )]
             elif extra_ids > 0 and additional_special_tokens is not None:
                    # Check that we have the right number of extra special tokens
                    lowercase__      			= len(set(filter(lambda UpperCamelCase : bool('''extra_id_''' in str(UpperCamelCase ) )   ,						UpperCamelCase ) ) )
                    if extra_tokens != extra_ids:
                           raise ValueError(
                               f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
                               ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                               ''' tokens''' )

             super().__init__(
                 UpperCamelCase   ,						tokenizer_file=UpperCamelCase   ,						eos_token=UpperCamelCase   ,						unk_token=UpperCamelCase   ,						pad_token=UpperCamelCase   ,						extra_ids=UpperCamelCase   ,						additional_special_tokens=UpperCamelCase   ,						**UpperCamelCase   ,						)

             lowercase__      			= vocab_file
             lowercase__      			= False if not self.vocab_file else True
             lowercase__      			= extra_ids



      @staticmethod
      def       UpperCamelCase__		(UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[Any] ):

             '''simple docstring'''







             if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                    lowercase__      			= TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
                    if init_max_model_length is not None and init_max_model_length != max_model_length:
                           return init_max_model_length
                    elif init_max_model_length is None:
                           warnings.warn(
                               '''This tokenizer was incorrectly instantiated with a model max length of'''
                               f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
                               ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                               ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                               f" {pretrained_model_name_or_path} automatically truncating your input to"
                               f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
                               f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
                               ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                               ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''   ,						UpperCamelCase   ,						)

             return max_model_length



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Optional[str] = None ):

             '''simple docstring'''







             if not self.can_save_slow_tokenizer:
                    raise ValueError(
                        '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                        '''tokenizer.''' )

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

             if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase ):
                    copyfile(self.vocab_file   ,						UpperCamelCase )
                    logger.info(f"Copy vocab file to {out_vocab_file}" )

             return (out_vocab_file,)



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    List[int]   ,						UpperCamelCase    :    Optional[List[int]] = None ):

             '''simple docstring'''







             lowercase__      			= token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                    return self.prefix_tokens + token_ids_a
             else:
                    lowercase__      			= token_ids_a + [self.eos_token_id]
                    return self.prefix_tokens + token_ids_a + token_ids_a



      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    List[int]   ,						UpperCamelCase    :    Optional[List[int]] = None ):

             '''simple docstring'''







             lowercase__      			= [self.eos_token_id]

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



      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             return list(
                 set(filter(lambda UpperCamelCase : bool(re.search(R'''<extra_id_\d+>'''   ,						UpperCamelCase ) ) is not None   ,						self.additional_special_tokens ) ) )






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             return [self.convert_tokens_to_ids(UpperCamelCase ) for token in self.get_sentinel_tokens()]




