



'''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        __A   (							_SCREAMING_SNAKE_CASE     :  List[Any]					,   _SCREAMING_SNAKE_CASE     :  Any=0.9_9_9					,   _SCREAMING_SNAKE_CASE     :  Optional[Any]="cosine"					,   ):







					"""simple docstring"""






					if alpha_transform_type == "cosine":

										def alpha_bar_fn(_SCREAMING_SNAKE_CASE     :  Optional[int]	):
															return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(_SCREAMING_SNAKE_CASE     :  Dict	):
															return math.exp(t * -1_2.0	)

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

					__SCREAMING_SNAKE_CASE				:    Dict						    =							[]
					for i in range(UpperCamelCase__	):
										__SCREAMING_SNAKE_CASE				:    List[str]						    =							i / num_diffusion_timesteps
										__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(UpperCamelCase__	) / alpha_bar_fn(UpperCamelCase__	)					,   UpperCamelCase__	)	)
					return torch.tensor(UpperCamelCase__					,   dtype=torch.floataa	)






class    __lowerCamelCase							( __SCREAMING_SNAKE_CASE							,							__SCREAMING_SNAKE_CASE   ):







	'''simple docstring'''
	snake_case__		:   Optional[Any]       =				[e.name for e in KarrasDiffusionSchedulers]
	snake_case__		:   List[Any]       =				2






	@register_to_config
	def __init__(      self      ,     a__ = 1000      ,     a__ = 0.00085      ,     a__ = 0.012      ,     a__ = "linear"      ,     a__ = None      ,     a__ = "epsilon"      ,     a__ = False      ,     a__ = False      ,     a__ = 1.0      ,     a__ = "linspace"      ,     a__ = 0      ,     ):
						if trained_betas is not None:
											__SCREAMING_SNAKE_CASE				:    Dict						    =							torch.tensor(__a      ,     dtype=torch.floataa					)
						elif beta_schedule == "linear":
											__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							torch.linspace(__a      ,     __a      ,     __a      ,     dtype=torch.floataa					)
						elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							(
											    torch.linspace(beta_start**0.5      ,     beta_end**0.5      ,     __a      ,     dtype=torch.floataa					) ** 2
											)
						elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							betas_for_alpha_bar(__a      ,     alpha_transform_type="cosine"					)
						elif beta_schedule == "exp":
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							betas_for_alpha_bar(__a      ,     alpha_transform_type="exp"					)
						else:
											raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'					)

						__SCREAMING_SNAKE_CASE				:    str						    =							1.0 - self.betas
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							torch.cumprod(self.alphas      ,     dim=0					)

						#  set all values
						self.set_timesteps(__a      ,     __a      ,     __a					)
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							use_karras_sigmas






	def  a_					(      self      ,     a__      ,     a__=None					):
						if schedule_timesteps is None:
											__SCREAMING_SNAKE_CASE				:    Any						    =							self.timesteps

						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							(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:
											__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							1 if len(__a					) > 1 else 0
						else:
											__SCREAMING_SNAKE_CASE				:    str						    =							timestep.cpu().item() if torch.is_tensor(__a					) else timestep
											__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							self._index_counter[timestep_int]

						return indices[pos].item()






	@property
	def  a_					(      self					):
						# standard deviation of the initial noise distribution
						if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

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






	def  a_					(      self      ,     a__      ,     a__      ,     ):
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							self.index_for_timestep(__a					)

						__SCREAMING_SNAKE_CASE				:    int						    =							self.sigmas[step_index]
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							sample / ((sigma**2 + 1) ** 0.5)
						return sample






	def  a_					(      self      ,     a__      ,     a__ = None      ,     a__ = None      ,     ):
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							num_inference_steps

						__SCREAMING_SNAKE_CASE				:    int						    =							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":
											__SCREAMING_SNAKE_CASE				:    str						    =							np.linspace(0      ,     num_train_timesteps - 1      ,     __a      ,     dtype=__a					)[::-1].copy()
						elif self.config.timestep_spacing == "leading":
											__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							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
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							(np.arange(0      ,     __a					) * step_ratio).round()[::-1].copy().astype(__a					)
											timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
											__SCREAMING_SNAKE_CASE				:    str						    =							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
											__SCREAMING_SNAKE_CASE				:    List[Any]						    =							(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\'.'					)

						__SCREAMING_SNAKE_CASE				:    int						    =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							np.log(__a					)
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							np.interp(__a      ,     np.arange(0      ,     len(__a					)					)      ,     __a					)

						if self.config.use_karras_sigmas:
											__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							self._convert_to_karras(in_sigmas=__a      ,     num_inference_steps=self.num_inference_steps					)
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							np.array([self._sigma_to_t(__a      ,     __a					) for sigma in sigmas]					)

						__SCREAMING_SNAKE_CASE				:    Dict						    =							np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
						__SCREAMING_SNAKE_CASE				:    Dict						    =							torch.from_numpy(__a					).to(device=__a					)
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2					), sigmas[-1:]]					)

						__SCREAMING_SNAKE_CASE				:    Tuple						    =							torch.from_numpy(__a					)
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2					)]					)

						if str(__a					).startswith("mps"					):
											# mps does not support float64
											__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							timesteps.to(__a      ,     dtype=torch.floataa					)
						else:
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							timesteps.to(device=__a					)

						# empty dt and derivative
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							None
						__SCREAMING_SNAKE_CASE				:    Any						    =							None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							defaultdict(__a					)






	def  a_					(      self      ,     a__      ,     a__					):
						# get log sigma
						__SCREAMING_SNAKE_CASE				:    int						    =							np.log(__a					)

						# get distribution
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							log_sigma - log_sigmas[:, np.newaxis]

						# get sigmas range
						__SCREAMING_SNAKE_CASE				:    int						    =							np.cumsum((dists >= 0)      ,     axis=0					).argmax(axis=0					).clip(max=log_sigmas.shape[0] - 2					)
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							low_idx + 1

						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							log_sigmas[low_idx]
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							log_sigmas[high_idx]

						# interpolate sigmas
						__SCREAMING_SNAKE_CASE				:    Any						    =							(low - log_sigma) / (low - high)
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							np.clip(__a      ,     0      ,     1					)

						# transform interpolation to time range
						__SCREAMING_SNAKE_CASE				:    Any						    =							(1 - w) * low_idx + w * high_idx
						__SCREAMING_SNAKE_CASE				:    int						    =							t.reshape(sigma.shape					)
						return t






	def  a_					(      self      ,     a__      ,     a__					):
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							in_sigmas[-1].item()
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							in_sigmas[0].item()

						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							7.0  # 7.0 is the value used in the paper
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							np.linspace(0      ,     1      ,     __a					)
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							sigma_min ** (1 / rho)
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							sigma_max ** (1 / rho)
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
						return sigmas






	@property
	def  a_					(      self					):
						return self.dt is None






	def  a_					(      self      ,     a__      ,     a__      ,     a__      ,     a__ = True      ,     ):
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							self.index_for_timestep(__a					)

						# advance index counter by 1
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							timestep.cpu().item() if torch.is_tensor(__a					) else timestep
						self._index_counter[timestep_int] += 1

						if self.state_in_first_order:
											__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							self.sigmas[step_index]
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							self.sigmas[step_index + 1]
						else:
											# 2nd order / Heun's method
											__SCREAMING_SNAKE_CASE				:    int						    =							self.sigmas[step_index - 1]
											__SCREAMING_SNAKE_CASE				:    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
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							0
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							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":
											__SCREAMING_SNAKE_CASE				:    int						    =							sigma_hat if self.state_in_first_order else sigma_next
											__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							sigma_hat if self.state_in_first_order else sigma_next
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
											    sample / (sigma_input**2 + 1)
											)
						elif self.config.prediction_type == "sample":
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							model_output
						else:
											raise ValueError(
											    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'					)

						if self.config.clip_sample:
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							pred_original_sample.clamp(
											    -self.config.clip_sample_range      ,     self.config.clip_sample_range					)

						if self.state_in_first_order:
											# 2. Convert to an ODE derivative for 1st order
											__SCREAMING_SNAKE_CASE				:    int						    =							(sample - pred_original_sample) / sigma_hat
											# 3. delta timestep
											__SCREAMING_SNAKE_CASE				:    List[Any]						    =							sigma_next - sigma_hat

											# store for 2nd order step
											__SCREAMING_SNAKE_CASE				:    List[Any]						    =							derivative
											__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							dt
											__SCREAMING_SNAKE_CASE				:    List[Any]						    =							sample
						else:
											# 2. 2nd order / Heun's method
											__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							(sample - pred_original_sample) / sigma_next
											__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							(self.prev_derivative + derivative) / 2

											# 3. take prev timestep & sample
											__SCREAMING_SNAKE_CASE				:    List[Any]						    =							self.dt
											__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							self.sample

											# free dt and derivative
											# Note, this puts the scheduler in "first order mode"
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							None
											__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							None
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							None

						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							sample + derivative * dt

						if not return_dict:
											return (prev_sample,)

						return SchedulerOutput(prev_sample=__a					)






	def  a_					(      self      ,     a__      ,     a__      ,     a__      ,     ):
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						__SCREAMING_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
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							self.timesteps.to(original_samples.device      ,     dtype=torch.floataa					)
											__SCREAMING_SNAKE_CASE				:    Dict						    =							timesteps.to(original_samples.device      ,     dtype=torch.floataa					)
						else:
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							self.timesteps.to(original_samples.device					)
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							timesteps.to(original_samples.device					)

						__SCREAMING_SNAKE_CASE				:    str						    =							[self.index_for_timestep(__a      ,     __a					) for t in timesteps]

						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							sigmas[step_indices].flatten()
						while len(sigma.shape					) < len(original_samples.shape					):
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							sigma.unsqueeze(-1					)

						__SCREAMING_SNAKE_CASE				:    int						    =							original_samples + noise * sigma
						return noisy_samples






	def __len__(      self					):
						return self.config.num_train_timesteps



'''simple docstring'''


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class     A :





							def __init__(   self					:				List[str]  ,		__a					:				Any  ,		__a					:				int=9_9  ,		__a					:				Any=1_3  ,		__a					:				Tuple=7  ,		__a					:				Tuple=9  ,		__a					:				Tuple=True  ,		__a					:				Union[str, Any]=True  ,		__a					:				List[Any]=False  ,		__a					:				Optional[Any]=3_2  ,		__a					:				str=5  ,		__a					:				Optional[int]=4  ,		__a					:				Union[str, Any]=3_7  ,		__a					:				List[str]=8  ,		__a					:				Optional[int]=0.1  ,		__a					:				List[str]=0.0_0_2  ,		__a					:				List[Any]=1  ,		__a					:				str=0  ,		__a					:				Dict=0  ,		__a					:				int=None  ,		__a					:				List[Any]=None  ,		)				->						Tuple:
								__UpperCAmelCase      					=      parent
								__UpperCAmelCase      					=      batch_size
								__UpperCAmelCase      					=      encoder_seq_length
								__UpperCAmelCase      					=      decoder_seq_length
								# For common tests
								__UpperCAmelCase      					=      self.decoder_seq_length
								__UpperCAmelCase      					=      is_training
								__UpperCAmelCase      					=      use_attention_mask
								__UpperCAmelCase      					=      use_labels
								__UpperCAmelCase      					=      vocab_size
								__UpperCAmelCase      					=      hidden_size
								__UpperCAmelCase      					=      num_hidden_layers
								__UpperCAmelCase      					=      num_attention_heads
								__UpperCAmelCase      					=      d_ff
								__UpperCAmelCase      					=      relative_attention_num_buckets
								__UpperCAmelCase      					=      dropout_rate
								__UpperCAmelCase      					=      initializer_factor
								__UpperCAmelCase      					=      eos_token_id
								__UpperCAmelCase      					=      pad_token_id
								__UpperCAmelCase      					=      decoder_start_token_id
								__UpperCAmelCase      					=      None
								__UpperCAmelCase      					=      decoder_layers





							def    snake_case__    (   self					:				Union[str, Any]						)				->						int:
								return TaConfig.from_pretrained('''google/umt5-base'''						)





							def    snake_case__    (   self					:				List[Any]  ,		__a					:				List[str]  ,		__a					:				str  ,		__a					:				Optional[int]  ,		__a					:				List[Any]=None  ,		__a					:				List[Any]=None  ,		__a					:				Any=None  ,		__a					:				str=None  ,		__a					:				Any=None  ,		)				->						List[Any]:
								if attention_mask is None:
									__UpperCAmelCase      					=      input_ids.ne(config.pad_token_id						)
								if decoder_attention_mask is None:
									__UpperCAmelCase      					=      decoder_input_ids.ne(config.pad_token_id						)
								if head_mask is None:
									__UpperCAmelCase      					=      torch.ones(config.num_hidden_layers  ,		config.num_attention_heads  ,		device=__a						)
								if decoder_head_mask is None:
									__UpperCAmelCase      					=      torch.ones(config.num_decoder_layers  ,		config.num_attention_heads  ,		device=__a						)
								if cross_attn_head_mask is None:
									__UpperCAmelCase      					=      torch.ones(
									    config.num_decoder_layers  ,		config.num_attention_heads  ,		device=__a						)
								return {
								    "input_ids": input_ids,
								    "decoder_input_ids": decoder_input_ids,
								    "attention_mask": attention_mask,
								    "decoder_attention_mask": decoder_attention_mask,
								    "head_mask": head_mask,
								    "decoder_head_mask": decoder_head_mask,
								    "cross_attn_head_mask": cross_attn_head_mask,
								}





							def    snake_case__    (   self					:				List[str]						)				->						Dict:
								__UpperCAmelCase      					=      ids_tensor([self.batch_size, self.encoder_seq_length]  ,		self.vocab_size						)
								__UpperCAmelCase      					=      ids_tensor([self.batch_size, self.decoder_seq_length]  ,		self.vocab_size						)

								# we need to clamp the input ids here to avoid having pad token in between
								# this is because for NllbMoe the position_ids are prepared such that
								# all pad tokens have pos id = 2 and rest are between 2..seq_length
								# and the seq_length here is seq_length - num_pad_tokens
								# but when using past, there is no way of knowing if the past input ids had
								# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
								# position_ids being off by num_pad_tokens in past input
								__UpperCAmelCase      					=      input_ids.clamp(self.pad_token_id + 1						)
								__UpperCAmelCase      					=      decoder_input_ids.clamp(self.pad_token_id + 1						)

								__UpperCAmelCase      					=      self.get_config()
								__UpperCAmelCase      					=      config.num_attention_heads
								__UpperCAmelCase      					=      self.prepare_inputs_dict(__a  ,		__a  ,		__a						)
								return config, input_dict





							def    snake_case__    (   self					:				Union[str, Any]						)				->						Optional[Any]:
								__UpperCAmelCase					,      __UpperCAmelCase      					=      self.prepare_config_and_inputs()
								return config, inputs_dict





							def    snake_case__    (   self					:				int						)				->						Optional[int]:
								return TaConfig(
								    vocab_size=1_6_6  ,		d_model=self.hidden_size  ,		d_ff=self.d_ff  ,		d_kv=self.hidden_size // self.num_attention_heads  ,		num_layers=self.num_hidden_layers  ,		num_decoder_layers=self.decoder_layers  ,		num_heads=self.num_attention_heads  ,		relative_attention_num_buckets=self.relative_attention_num_buckets  ,		dropout_rate=self.dropout_rate  ,		initializer_factor=self.initializer_factor  ,		eos_token_id=self.eos_token_id  ,		bos_token_id=self.pad_token_id  ,		pad_token_id=self.pad_token_id  ,		decoder_start_token_id=self.decoder_start_token_id  ,		)





							def    snake_case__    (   self					:				Optional[int]						)				->						Any:
								return TaConfig(
								    vocab_size=self.vocab_size  ,		d_model=self.hidden_size  ,		d_ff=self.d_ff  ,		d_kv=self.hidden_size // self.num_attention_heads  ,		num_layers=self.num_hidden_layers  ,		num_decoder_layers=self.decoder_layers  ,		num_heads=self.num_attention_heads  ,		relative_attention_num_buckets=self.relative_attention_num_buckets  ,		dropout_rate=self.dropout_rate  ,		initializer_factor=self.initializer_factor  ,		eos_token_id=self.eos_token_id  ,		bos_token_id=self.pad_token_id  ,		pad_token_id=self.pad_token_id  ,		decoder_start_token_id=self.decoder_start_token_id  ,		)





							def    snake_case__    (   self					:				int  ,		__a					:				Any  ,		__a					:				Union[str, Any]  ,		__a					:				List[Any]  ,		__a					:				Dict  ,		__a					:				Optional[Any]  ,		__a					:				int  ,		)				->						List[Any]:
								__UpperCAmelCase      					=      UMTaModel(config=__a						)
								model.to(__a						)
								model.eval()
								__UpperCAmelCase      					=      model(
								    input_ids=__a  ,		decoder_input_ids=__a  ,		attention_mask=__a  ,		decoder_attention_mask=__a  ,		)
								__UpperCAmelCase      					=      model(input_ids=__a  ,		decoder_input_ids=__a						)
								__UpperCAmelCase      					=      result.last_hidden_state
								__UpperCAmelCase      					=      result.past_key_values
								__UpperCAmelCase      					=      result.encoder_last_hidden_state

								self.parent.assertEqual(encoder_output.size()  ,		(self.batch_size, self.encoder_seq_length, self.hidden_size)						)
								self.parent.assertEqual(decoder_output.size()  ,		(self.batch_size, self.decoder_seq_length, self.hidden_size)						)
								# There should be `num_layers` key value embeddings stored in decoder_past
								self.parent.assertEqual(len(__a						)  ,		config.num_layers						)
								# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
								self.parent.assertEqual(len(decoder_past[0]						)  ,		4						)





							def    snake_case__    (   self					:				List[str]  ,		__a					:				Any  ,		__a					:				Tuple  ,		__a					:				List[str]  ,		__a					:				Optional[Any]  ,		__a					:				Dict  ,		__a					:				Any  ,		)				->						Optional[Any]:
								__UpperCAmelCase      					=      UMTaModel(config=__a						).get_decoder().to(__a						).eval()
								# first forward pass
								__UpperCAmelCase      					=      model(__a  ,		use_cache=__a						)
								__UpperCAmelCase      					=      model(__a						)
								__UpperCAmelCase      					=      model(__a  ,		use_cache=__a						)

								self.parent.assertTrue(len(__a						) == len(__a						)						)
								self.parent.assertTrue(len(__a						) == len(__a						) + 1						)

								__UpperCAmelCase					,      __UpperCAmelCase      					=      outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								__UpperCAmelCase      					=      ids_tensor((self.batch_size, 1)  ,		config.vocab_size						)

								# append to next input_ids and
								__UpperCAmelCase      					=      torch.cat([input_ids, next_tokens]  ,		dim=-1						)

								__UpperCAmelCase      					=      model(__a						)['''last_hidden_state''']
								__UpperCAmelCase      					=      model(__a  ,		past_key_values=__a						)['''last_hidden_state''']

								# select random slice
								__UpperCAmelCase      					=      ids_tensor((1,)  ,		output_from_past.shape[-1]						).item()
								__UpperCAmelCase      					=      output_from_no_past[:, -1, random_slice_idx].detach()
								__UpperCAmelCase      					=      output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(__a  ,		__a  ,		atol=1e-3						)						)






							def    snake_case__    (   self					:				List[Any]  ,		__a					:				Union[str, Any]  ,		__a					:				Dict  ,		)				->						Optional[int]:
								__UpperCAmelCase      					=      UMTaModel(config=__a						).to(__a						).half().eval()
								__UpperCAmelCase      					=      model(**__a						)['''last_hidden_state''']
								self.parent.assertFalse(torch.isnan(__a						).any().item()						)






@require_torch
class     A ( UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						unittest.TestCase ):
							a_										=   (
							    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
							)
							a_										=   (UMTaForConditionalGeneration,) if is_torch_available() else ()
							a_										=   (
							    {
							        '''conversational''': UMTaForConditionalGeneration,
							        '''feature-extraction''': UMTaModel,
							        '''summarization''': UMTaForConditionalGeneration,
							        '''text2text-generation''': UMTaForConditionalGeneration,
							        '''translation''': UMTaForConditionalGeneration,
							        '''question-answering''': UMTaForQuestionAnswering,
							    }
							    if is_torch_available()
							    else {}
							)
							a_										=   True
							a_										=   False
							a_										=   False
							a_										=   True
							a_										=   True
							# The small UMT5 model needs higher percentages for CPU/MP tests
							a_										=   [0.8, 0.9]





							def    snake_case__    (   self					:				Tuple						)				->						Optional[int]:
								__UpperCAmelCase      					=      UMTaModelTester(self						)





							@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''						)
							def    snake_case__    (   self					:				str						)				->						Optional[int]:
								__UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs()
								__UpperCAmelCase      					=      UMTaModel(config_and_inputs[0]						).to(__a						)
								with tempfile.TemporaryDirectory() as tmpdirname:
									torch.onnx.export(
									    __a  ,		(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])  ,		f"""{tmpdirname}/t5_test.onnx"""  ,		export_params=__a  ,		opset_version=9  ,		input_names=['''input_ids''', '''decoder_input_ids''']  ,		)





							@unittest.skipIf(torch_device == '''cpu'''  ,		'''Cant do half precision'''						)
							def    snake_case__    (   self					:				Union[str, Any]						)				->						List[str]:
								__UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model_fpaa_forward(*__a						)





							def    snake_case__    (   self					:				List[Any]						)				->						str:
								__UpperCAmelCase      					=      ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
								__UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs()
								__UpperCAmelCase      					=      config_and_inputs[0]
								__UpperCAmelCase      					=      UMTaForConditionalGeneration(__a						).eval()
								model.to(__a						)

								__UpperCAmelCase      					=      {
								    '''head_mask''': torch.zeros(config.num_layers  ,		config.num_heads  ,		device=__a						),
								    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers  ,		config.num_heads  ,		device=__a						),
								    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers  ,		config.num_heads  ,		device=__a						),
								}

								for attn_name, (name, mask) in zip(__a  ,		head_masking.items()						):
									__UpperCAmelCase      					=      {name: mask}
									# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
									if name == "head_mask":
										__UpperCAmelCase      					=      torch.ones(
										    config.num_decoder_layers  ,		config.num_heads  ,		device=__a						)

									__UpperCAmelCase      					=      model.generate(
									    config_and_inputs[1]['''input_ids''']  ,		num_beams=1  ,		max_length=3  ,		output_attentions=__a  ,		return_dict_in_generate=__a  ,		**__a  ,		)
									# We check the state of decoder_attentions and cross_attentions just from the last step
									__UpperCAmelCase      					=      out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
									self.assertEqual(sum([w.sum().item() for w in attn_weights]						)  ,		0.0						)






							@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''						)
							def    snake_case__    (   self					:				Optional[int]						)				->						int:
								pass






@require_torch
@require_sentencepiece
@require_tokenizers
class     A ( unittest.TestCase ):





							@slow
							@unittest.skip(
							    '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''						)
							def    snake_case__    (   self					:				Any						)				->						int:
								__UpperCAmelCase      					=      UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''  ,		return_dict=__a						).to(__a						)
								__UpperCAmelCase      					=      AutoTokenizer.from_pretrained('''google/umt5-small'''  ,		use_fast=__a  ,		legacy=__a						)
								__UpperCAmelCase      					=      [
								    '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
								    '''No se como puedo <extra_id_0>.''',
								    '''This is the reason why we <extra_id_0> them.''',
								    '''The <extra_id_0> walks in <extra_id_1>, seats''',
								    '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
								]
								__UpperCAmelCase      					=      tokenizer(__a  ,		return_tensors='''pt'''  ,		padding=__a						).input_ids
								# fmt: off
								__UpperCAmelCase      					=      torch.tensor(
								    [
								        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
								        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
								    ]						)
								# fmt: on
								torch.testing.assert_allclose(__a  ,		__a						)

								__UpperCAmelCase      					=      model.generate(input_ids.to(__a						)						)
								__UpperCAmelCase      					=      [
								    '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
								    '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								    '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								    '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								    '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
								]
								__UpperCAmelCase      					=      tokenizer.batch_decode(__a						)
								self.assertEqual(__a  ,		__a						)
