


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   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=0.9_9_9 ,			SCREAMING_SNAKE_CASE="cosine" ,			):
  if alpha_transform_type == "cosine":

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

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

  A_      :				List[str]		     =					[]
  for i in range(SCREAMING_SNAKE_CASE       ):
    A_      :				Any		     =					i / num_diffusion_timesteps
    A_      :				Optional[int]		     =					(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE       ) / alpha_bar_fn(SCREAMING_SNAKE_CASE       ) ,			SCREAMING_SNAKE_CASE       )       )
  return torch.tensor(SCREAMING_SNAKE_CASE ,			dtype=torch.floataa       )







class   _lowerCamelCase (					UpperCamelCase					,							UpperCamelCase			):




       """simple docstring"""
       snake_case      =    [e.name for e in KarrasDiffusionSchedulers]
       snake_case      =    2



       @register_to_config
       def __init__(      self				,       _SCREAMING_SNAKE_CASE = 1000				,       _SCREAMING_SNAKE_CASE = 0.0_0_0_8_5				,       _SCREAMING_SNAKE_CASE = 0.0_1_2				,       _SCREAMING_SNAKE_CASE = "linear"				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = "epsilon"				,       _SCREAMING_SNAKE_CASE = False				,       _SCREAMING_SNAKE_CASE = False				,       _SCREAMING_SNAKE_CASE = 1.0				,       _SCREAMING_SNAKE_CASE = "linspace"				,       _SCREAMING_SNAKE_CASE = 0				,       )->List[Any]:




         '''simple docstring'''





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

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

         #  set all values
         self.set_timesteps(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Optional[Any]		     =					use_karras_sigmas



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=None		)->Optional[Any]:




         '''simple docstring'''





         if schedule_timesteps is None:
           A_      :				Any		     =					self.timesteps

         A_      :				Union[str, Any]		     =					(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:
           A_      :				List[str]		     =					1 if len(_SCREAMING_SNAKE_CASE		) > 1 else 0
         else:
           A_      :				int		     =					timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE		) else timestep
           A_      :				Optional[int]		     =					self._index_counter[timestep_int]

         return indices[pos].item()



       @property
       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





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

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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       )->torch.FloatTensor:




         '''simple docstring'''





         A_      :				List[str]		     =					self.index_for_timestep(_SCREAMING_SNAKE_CASE		)

         A_      :				Tuple		     =					self.sigmas[step_index]
         A_      :				Union[str, Any]		     =					sample / ((sigma**2 + 1) ** 0.5)
         return sample



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       )->str:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					num_inference_steps

         A_      :				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":
           A_      :				Tuple		     =					np.linspace(0				,       num_train_timesteps - 1				,       _SCREAMING_SNAKE_CASE				,       dtype=_SCREAMING_SNAKE_CASE		)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
           A_      :				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
           A_      :				Optional[int]		     =					(np.arange(0				,       _SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE		)
           timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
           A_      :				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
           A_      :				int		     =					(np.arange(_SCREAMING_SNAKE_CASE				,       0				,       -step_ratio		)).round().copy().astype(_SCREAMING_SNAKE_CASE		)
           timesteps -= 1
         else:
           raise ValueError(
               F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''		)

         A_      :				str		     =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
         A_      :				str		     =					np.log(_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					np.interp(_SCREAMING_SNAKE_CASE				,       np.arange(0				,       len(_SCREAMING_SNAKE_CASE		)		)				,       _SCREAMING_SNAKE_CASE		)

         if self.config.use_karras_sigmas:
           A_      :				List[str]		     =					self._convert_to_karras(in_sigmas=_SCREAMING_SNAKE_CASE				,       num_inference_steps=self.num_inference_steps		)
           A_      :				Optional[Any]		     =					np.array([self._sigma_to_t(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) for sigma in sigmas]		)

         A_      :				Tuple		     =					np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
         A_      :				List[Any]		     =					torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2		), sigmas[-1:]]		)

         A_      :				Any		     =					torch.from_numpy(_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2		)]		)

         if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
           # mps does not support float64
           A_      :				List[str]		     =					timesteps.to(_SCREAMING_SNAKE_CASE				,       dtype=torch.floataa		)
         else:
           A_      :				Union[str, Any]		     =					timesteps.to(device=_SCREAMING_SNAKE_CASE		)

         # empty dt and derivative
         A_      :				Tuple		     =					None
         A_      :				Any		     =					None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         A_      :				Any		     =					defaultdict(_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Dict:




         '''simple docstring'''





         A_      :				int		     =					np.log(_SCREAMING_SNAKE_CASE		)

         # get distribution
         A_      :				Optional[Any]		     =					log_sigma - log_sigmas[:, np.newaxis]

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

         A_      :				Optional[int]		     =					log_sigmas[low_idx]
         A_      :				Optional[Any]		     =					log_sigmas[high_idx]

         # interpolate sigmas
         A_      :				List[str]		     =					(low - log_sigma) / (low - high)
         A_      :				List[str]		     =					np.clip(_SCREAMING_SNAKE_CASE				,       0				,       1		)

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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->torch.FloatTensor:




         '''simple docstring'''





         A_      :				float		     =					in_sigmas[-1].item()
         A_      :				float		     =					in_sigmas[0].item()

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



       @property
       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         return self.dt is None



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = True				,       )->Union[SchedulerOutput, Tuple]:




         '''simple docstring'''





         A_      :				int		     =					self.index_for_timestep(_SCREAMING_SNAKE_CASE		)

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

         if self.state_in_first_order:
           A_      :				Any		     =					self.sigmas[step_index]
           A_      :				Tuple		     =					self.sigmas[step_index + 1]
         else:
           # 2nd order / Heun's method
           A_      :				List[Any]		     =					self.sigmas[step_index - 1]
           A_      :				Any		     =					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
         A_      :				List[str]		     =					0
         A_      :				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":
           A_      :				Union[str, Any]		     =					sigma_hat if self.state_in_first_order else sigma_next
           A_      :				List[str]		     =					sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
           A_      :				Optional[Any]		     =					sigma_hat if self.state_in_first_order else sigma_next
           A_      :				List[str]		     =					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**2 + 1)
           )
         elif self.config.prediction_type == "sample":
           A_      :				Dict		     =					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:
           A_      :				Union[str, Any]		     =					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
           A_      :				Optional[Any]		     =					(sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           A_      :				Optional[int]		     =					sigma_next - sigma_hat

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

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

           # free dt and derivative
           # Note, this puts the scheduler in "first order mode"
           A_      :				Tuple		     =					None
           A_      :				Any		     =					None
           A_      :				int		     =					None

         A_      :				Tuple		     =					sample + derivative * dt

         if not return_dict:
           return (prev_sample,)

         return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       )->torch.FloatTensor:




         '''simple docstring'''





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

         A_      :				str		     =					[self.index_for_timestep(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) for t in timesteps]

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

         A_      :				List[Any]		     =					original_samples + noise * sigma
         return noisy_samples







       def __len__(      self		)->int:




         '''simple docstring'''





         return self.config.num_train_timesteps








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   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=99				,       _SCREAMING_SNAKE_CASE=13				,       _SCREAMING_SNAKE_CASE=7				,       _SCREAMING_SNAKE_CASE=9				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=5				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=37				,       _SCREAMING_SNAKE_CASE=8				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=0.0_0_2				,       _SCREAMING_SNAKE_CASE=1				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       )->str:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					parent
         A_      :				Optional[Any]		     =					batch_size
         A_      :				int		     =					encoder_seq_length
         A_      :				List[Any]		     =					decoder_seq_length
         # For common tests
         A_      :				str		     =					self.decoder_seq_length
         A_      :				int		     =					is_training
         A_      :				Tuple		     =					use_attention_mask
         A_      :				Any		     =					use_labels
         A_      :				List[str]		     =					vocab_size
         A_      :				Dict		     =					hidden_size
         A_      :				Optional[Any]		     =					num_hidden_layers
         A_      :				List[Any]		     =					num_attention_heads
         A_      :				Tuple		     =					d_ff
         A_      :				List[str]		     =					relative_attention_num_buckets
         A_      :				Tuple		     =					dropout_rate
         A_      :				Optional[Any]		     =					initializer_factor
         A_      :				List[str]		     =					eos_token_id
         A_      :				Union[str, Any]		     =					pad_token_id
         A_      :				Any		     =					decoder_start_token_id
         A_      :				int		     =					None
         A_      :				Tuple		     =					decoder_layers



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         return TaConfig.from_pretrained('''google/umt5-base'''		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       )->List[str]:




         '''simple docstring'''





         if attention_mask is None:
           A_      :				Union[str, Any]		     =					input_ids.ne(config.pad_token_id		)
         if decoder_attention_mask is None:
           A_      :				Any		     =					decoder_input_ids.ne(config.pad_token_id		)
         if head_mask is None:
           A_      :				str		     =					torch.ones(config.num_hidden_layers				,       config.num_attention_heads				,       device=_SCREAMING_SNAKE_CASE		)
         if decoder_head_mask is None:
           A_      :				List[str]		     =					torch.ones(config.num_decoder_layers				,       config.num_attention_heads				,       device=_SCREAMING_SNAKE_CASE		)
         if cross_attn_head_mask is None:
           A_      :				Union[str, Any]		     =					torch.ones(
               config.num_decoder_layers				,       config.num_attention_heads				,       device=_SCREAMING_SNAKE_CASE		)
         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		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					ids_tensor([self.batch_size, self.encoder_seq_length]				,       self.vocab_size		)
         A_      :				int		     =					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
         A_      :				Tuple		     =					input_ids.clamp(self.pad_token_id + 1		)
         A_      :				int		     =					decoder_input_ids.clamp(self.pad_token_id + 1		)

         A_      :				Optional[Any]		     =					self.get_config()
         A_      :				int		     =					config.num_attention_heads
         A_      :				Dict		     =					self.prepare_inputs_dict(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         return config, input_dict



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         A_     ,       A_      :				str		     =					self.prepare_config_and_inputs()
         return config, inputs_dict



       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         return TaConfig(
             vocab_size=166				,       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		)->Dict:




         '''simple docstring'''





         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				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       )->Dict:




         '''simple docstring'''





         A_      :				Dict		     =					UMTaModel(config=_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				Tuple		     =					model(
             input_ids=_SCREAMING_SNAKE_CASE				,       decoder_input_ids=_SCREAMING_SNAKE_CASE				,       attention_mask=_SCREAMING_SNAKE_CASE				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE				,       )
         A_      :				List[Any]		     =					model(input_ids=_SCREAMING_SNAKE_CASE				,       decoder_input_ids=_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					result.last_hidden_state
         A_      :				List[Any]		     =					result.past_key_values
         A_      :				Any		     =					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(_SCREAMING_SNAKE_CASE		)				,       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				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       )->Optional[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					UMTaModel(config=_SCREAMING_SNAKE_CASE		).get_decoder().to(_SCREAMING_SNAKE_CASE		).eval()
         # first forward pass
         A_      :				Union[str, Any]		     =					model(_SCREAMING_SNAKE_CASE				,       use_cache=_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					model(_SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					model(_SCREAMING_SNAKE_CASE				,       use_cache=_SCREAMING_SNAKE_CASE		)

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

         A_     ,       A_      :				int		     =					outputs.to_tuple()

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

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

         A_      :				Tuple		     =					model(_SCREAMING_SNAKE_CASE		)['''last_hidden_state''']
         A_      :				Union[str, Any]		     =					model(_SCREAMING_SNAKE_CASE				,       past_key_values=_SCREAMING_SNAKE_CASE		)['''last_hidden_state''']

         # select random slice
         A_      :				List[Any]		     =					ids_tensor((1,)				,       output_from_past.shape[-1]		).item()
         A_      :				Union[str, Any]		     =					output_from_no_past[:, -1, random_slice_idx].detach()
         A_      :				List[str]		     =					output_from_past[:, 0, random_slice_idx].detach()

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







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




         '''simple docstring'''





         A_      :				List[str]		     =					UMTaModel(config=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		).half().eval()
         A_      :				Tuple		     =					model(**_SCREAMING_SNAKE_CASE		)['''last_hidden_state''']
         self.parent.assertFalse(torch.isnan(_SCREAMING_SNAKE_CASE		).any().item()		)






@require_torch
class   _lowerCamelCase (					UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    (
           (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
       )
       snake_case      =    (UMTaForConditionalGeneration,) if is_torch_available() else ()
       snake_case      =    (
           {
               "conversational": UMTaForConditionalGeneration,
               "feature-extraction": UMTaModel,
               "summarization": UMTaForConditionalGeneration,
               "text2text-generation": UMTaForConditionalGeneration,
               "translation": UMTaForConditionalGeneration,
               "question-answering": UMTaForQuestionAnswering,
           }
           if is_torch_available()
           else {}
       )
       snake_case      =    True
       snake_case      =    False
       snake_case      =    False
       snake_case      =    True
       snake_case      =    True
       # The small UMT5 model needs higher percentages for CPU/MP tests
       snake_case      =    [0.8, 0.9]



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					UMTaModelTester(self		)



       @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''		)
       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Any		     =					self.model_tester.prepare_config_and_inputs()
         A_      :				str		     =					UMTaModel(config_and_inputs[0]		).to(_SCREAMING_SNAKE_CASE		)
         with tempfile.TemporaryDirectory() as tmpdirname:
           torch.onnx.export(
               _SCREAMING_SNAKE_CASE				,       (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])				,       F'''{tmpdirname}/t5_test.onnx'''				,       export_params=_SCREAMING_SNAKE_CASE				,       opset_version=9				,       input_names=['''input_ids''', '''decoder_input_ids''']				,       )



       @unittest.skipIf(torch_device == '''cpu'''				,       '''Cant do half precision'''		)
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				Dict		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_fpaa_forward(*_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Optional[int]		     =					['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
         A_      :				List[Any]		     =					self.model_tester.prepare_config_and_inputs()
         A_      :				Union[str, Any]		     =					config_and_inputs[0]
         A_      :				int		     =					UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE		).eval()
         model.to(_SCREAMING_SNAKE_CASE		)

         A_      :				Any		     =					{
             '''head_mask''': torch.zeros(config.num_layers				,       config.num_heads				,       device=_SCREAMING_SNAKE_CASE		),
             '''decoder_head_mask''': torch.zeros(config.num_decoder_layers				,       config.num_heads				,       device=_SCREAMING_SNAKE_CASE		),
             '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers				,       config.num_heads				,       device=_SCREAMING_SNAKE_CASE		),
         }

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

           A_      :				Union[str, Any]		     =					model.generate(
               config_and_inputs[1]['''input_ids''']				,       num_beams=1				,       max_length=3				,       output_attentions=_SCREAMING_SNAKE_CASE				,       return_dict_in_generate=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )
           # We check the state of decoder_attentions and cross_attentions just from the last step
           A_      :				Tuple		     =					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		)->Any:




         '''simple docstring'''





         pass






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




       """simple docstring"""



       @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		)->int:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''				,       return_dict=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					AutoTokenizer.from_pretrained('''google/umt5-small'''				,       use_fast=_SCREAMING_SNAKE_CASE				,       legacy=_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					[
             '''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>.''',
         ]
         A_      :				Union[str, Any]		     =					tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''				,       padding=_SCREAMING_SNAKE_CASE		).input_ids
         # fmt: off
         A_      :				Dict		     =					torch.tensor(
             [
                 [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                 [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
             ]		)
         # fmt: on
         torch.testing.assert_allclose(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         A_      :				List[Any]		     =					model.generate(input_ids.to(_SCREAMING_SNAKE_CASE		)		)
         A_      :				str		     =					[
             '''<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>''',
         ]
         A_      :				str		     =					tokenizer.batch_decode(_SCREAMING_SNAKE_CASE		)
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)




