



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__      (		_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Union[str, Any]=0.999			,_UpperCamelCase      :			Any="cosine"			,):
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(_UpperCamelCase      :			Optional[int]				):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(_UpperCamelCase      :			Dict				):
         return math.exp(t * -12.0				)

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

   __lowerCamelCase    				=     []
   for i in range(_UpperCamelCase				):
      __lowerCamelCase    				=     i / num_diffusion_timesteps
      __lowerCamelCase    				=     (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  (						lowerCAmelCase__	,						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	[e.name for e in KarrasDiffusionSchedulers]
 lowerCAmelCase__							       =	2




 @register_to_config
 def __init__(    self						,					__UpperCAmelCase = 1000						,					__UpperCAmelCase = 0.00_085						,					__UpperCAmelCase = 0.012						,					__UpperCAmelCase = "linear"						,					__UpperCAmelCase = None						,					__UpperCAmelCase = "epsilon"						,					__UpperCAmelCase = "linspace"						,					__UpperCAmelCase = 0						,					):



    '''simple docstring'''
    if trained_betas is not None:
       __lowerCamelCase    				=     torch.tensor(__UpperCAmelCase						,					dtype=torch.floataa					)
    elif beta_schedule == "linear":
       __lowerCamelCase    				=     torch.linspace(__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					dtype=torch.floataa					)
    elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       __lowerCamelCase    				=     (
           torch.linspace(beta_start**0.5						,					beta_end**0.5						,					__UpperCAmelCase						,					dtype=torch.floataa					) ** 2
       )
    elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       __lowerCamelCase    				=     betas_for_alpha_bar(__UpperCAmelCase					)
    else:
       raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""					)

    __lowerCamelCase    				=     1.0 - self.betas
    __lowerCamelCase    				=     torch.cumprod(self.alphas						,					dim=0					)

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




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=None					):



    '''simple docstring'''
    if schedule_timesteps is None:
       __lowerCamelCase    				=     self.timesteps

    __lowerCamelCase    				=     (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:
       __lowerCamelCase    				=     1 if len(__UpperCAmelCase					) > 1 else 0
    else:
       __lowerCamelCase    				=     timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase					) else timestep
       __lowerCamelCase    				=     self._index_counter[timestep_int]

    return indices[pos].item()




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # 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 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.index_for_timestep(__UpperCAmelCase					)

    if self.state_in_first_order:
       __lowerCamelCase    				=     self.sigmas[step_index]
    else:
       __lowerCamelCase    				=     self.sigmas_interpol[step_index]

    __lowerCamelCase    				=     sample / ((sigma**2 + 1) ** 0.5)
    return sample




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     num_inference_steps

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

    __lowerCamelCase    				=     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
    __lowerCamelCase    				=     torch.from_numpy(np.log(__UpperCAmelCase					)					).to(__UpperCAmelCase					)

    __lowerCamelCase    				=     np.interp(__UpperCAmelCase						,					np.arange(0						,					len(__UpperCAmelCase					)					)						,					__UpperCAmelCase					)
    __lowerCamelCase    				=     np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
    __lowerCamelCase    				=     torch.from_numpy(__UpperCAmelCase					).to(device=__UpperCAmelCase					)

    # interpolate sigmas
    __lowerCamelCase    				=     sigmas.log().lerp(sigmas.roll(1					).log()						,					0.5					).exp()

    __lowerCamelCase    				=     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
    __lowerCamelCase    				=     torch.cat(
        [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)

    if str(__UpperCAmelCase					).startswith('''mps'''					):
       # mps does not support float64
       __lowerCamelCase    				=     torch.from_numpy(__UpperCAmelCase					).to(__UpperCAmelCase						,					dtype=torch.floataa					)
    else:
       __lowerCamelCase    				=     torch.from_numpy(__UpperCAmelCase					).to(__UpperCAmelCase					)

    # interpolate timesteps
    __lowerCamelCase    				=     self.sigma_to_t(__UpperCAmelCase					).to(__UpperCAmelCase						,					dtype=timesteps.dtype					)
    __lowerCamelCase    				=     torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])						,					dim=-1					).flatten()

    __lowerCamelCase    				=     torch.cat([timesteps[:1], interleaved_timesteps]					)

    __lowerCamelCase    				=     None

    # for exp beta schedules, such as the one for `pipeline_shap_e.py`
    # we need an index counter
    __lowerCamelCase    				=     defaultdict(__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    # get log sigma
    __lowerCamelCase    				=     sigma.log()

    # get distribution
    __lowerCamelCase    				=     log_sigma - self.log_sigmas[:, None]

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

    __lowerCamelCase    				=     self.log_sigmas[low_idx]
    __lowerCamelCase    				=     self.log_sigmas[high_idx]

    # interpolate sigmas
    __lowerCamelCase    				=     (low - log_sigma) / (low - high)
    __lowerCamelCase    				=     w.clamp(0						,					1					)

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




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return self.sample is None




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase = True						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.index_for_timestep(__UpperCAmelCase					)

    # advance index counter by 1
    __lowerCamelCase    				=     timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase					) else timestep
    self._index_counter[timestep_int] += 1

    if self.state_in_first_order:
       __lowerCamelCase    				=     self.sigmas[step_index]
       __lowerCamelCase    				=     self.sigmas_interpol[step_index + 1]
       __lowerCamelCase    				=     self.sigmas[step_index + 1]
    else:
       # 2nd order / KDPM2's method
       __lowerCamelCase    				=     self.sigmas[step_index - 1]
       __lowerCamelCase    				=     self.sigmas_interpol[step_index]
       __lowerCamelCase    				=     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
    __lowerCamelCase    				=     0
    __lowerCamelCase    				=     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":
       __lowerCamelCase    				=     sigma_hat if self.state_in_first_order else sigma_interpol
       __lowerCamelCase    				=     sample - sigma_input * model_output
    elif self.config.prediction_type == "v_prediction":
       __lowerCamelCase    				=     sigma_hat if self.state_in_first_order else sigma_interpol
       __lowerCamelCase    				=     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
       __lowerCamelCase    				=     (sample - pred_original_sample) / sigma_hat
       # 3. delta timestep
       __lowerCamelCase    				=     sigma_interpol - sigma_hat

       # store for 2nd order step
       __lowerCamelCase    				=     sample
    else:
       # DPM-Solver-2
       # 2. Convert to an ODE derivative for 2nd order
       __lowerCamelCase    				=     (sample - pred_original_sample) / sigma_interpol

       # 3. delta timestep
       __lowerCamelCase    				=     sigma_next - sigma_hat

       __lowerCamelCase    				=     self.sample
       __lowerCamelCase    				=     None

    __lowerCamelCase    				=     sample + derivative * dt

    if not return_dict:
       return (prev_sample,)

    return SchedulerOutput(prev_sample=__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					):



    '''simple docstring'''
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    __lowerCamelCase    				=     self.sigmas.to(device=original_samples.device						,					dtype=original_samples.dtype					)
    if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase					):
       # mps does not support float64
       __lowerCamelCase    				=     self.timesteps.to(original_samples.device						,					dtype=torch.floataa					)
       __lowerCamelCase    				=     timesteps.to(original_samples.device						,					dtype=torch.floataa					)
    else:
       __lowerCamelCase    				=     self.timesteps.to(original_samples.device					)
       __lowerCamelCase    				=     timesteps.to(original_samples.device					)

    __lowerCamelCase    				=     [self.index_for_timestep(__UpperCAmelCase						,					__UpperCAmelCase					) for t in timesteps]

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

    __lowerCamelCase    				=     original_samples + noise * sigma
    return noisy_samples




 def __len__(    self					):



    '''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  :




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase=99						,					__UpperCAmelCase=13						,					__UpperCAmelCase=7						,					__UpperCAmelCase=9						,					__UpperCAmelCase=True						,					__UpperCAmelCase=True						,					__UpperCAmelCase=False						,					__UpperCAmelCase=32						,					__UpperCAmelCase=5						,					__UpperCAmelCase=4						,					__UpperCAmelCase=37						,					__UpperCAmelCase=8						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=0.002						,					__UpperCAmelCase=1						,					__UpperCAmelCase=0						,					__UpperCAmelCase=0						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     parent
    __lowerCamelCase    				=     batch_size
    __lowerCamelCase    				=     encoder_seq_length
    __lowerCamelCase    				=     decoder_seq_length
    # For common tests
    __lowerCamelCase    				=     self.decoder_seq_length
    __lowerCamelCase    				=     is_training
    __lowerCamelCase    				=     use_attention_mask
    __lowerCamelCase    				=     use_labels
    __lowerCamelCase    				=     vocab_size
    __lowerCamelCase    				=     hidden_size
    __lowerCamelCase    				=     num_hidden_layers
    __lowerCamelCase    				=     num_attention_heads
    __lowerCamelCase    				=     d_ff
    __lowerCamelCase    				=     relative_attention_num_buckets
    __lowerCamelCase    				=     dropout_rate
    __lowerCamelCase    				=     initializer_factor
    __lowerCamelCase    				=     eos_token_id
    __lowerCamelCase    				=     pad_token_id
    __lowerCamelCase    				=     decoder_start_token_id
    __lowerCamelCase    				=     None
    __lowerCamelCase    				=     decoder_layers




 def 							lowerCamelCase   (    self					):



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




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					):



    '''simple docstring'''
    if attention_mask is None:
       __lowerCamelCase    				=     input_ids.ne(config.pad_token_id					)
    if decoder_attention_mask is None:
       __lowerCamelCase    				=     decoder_input_ids.ne(config.pad_token_id					)
    if head_mask is None:
       __lowerCamelCase    				=     torch.ones(config.num_hidden_layers						,					config.num_attention_heads						,					device=__UpperCAmelCase					)
    if decoder_head_mask is None:
       __lowerCamelCase    				=     torch.ones(config.num_decoder_layers						,					config.num_attention_heads						,					device=__UpperCAmelCase					)
    if cross_attn_head_mask is None:
       __lowerCamelCase    				=     torch.ones(
           config.num_decoder_layers						,					config.num_attention_heads						,					device=__UpperCAmelCase					)
    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 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     ids_tensor([self.batch_size, self.encoder_seq_length]						,					self.vocab_size					)
    __lowerCamelCase    				=     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
    __lowerCamelCase    				=     input_ids.clamp(self.pad_token_id + 1					)
    __lowerCamelCase    				=     decoder_input_ids.clamp(self.pad_token_id + 1					)

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




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase				,__lowerCamelCase    				=     self.prepare_config_and_inputs()
    return config, inputs_dict




 def 							lowerCamelCase   (    self					):



    '''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 							lowerCamelCase   (    self					):



    '''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 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     UMTaModel(config=__UpperCAmelCase					)
    model.to(__UpperCAmelCase					)
    model.eval()
    __lowerCamelCase    				=     model(
        input_ids=__UpperCAmelCase						,					decoder_input_ids=__UpperCAmelCase						,					attention_mask=__UpperCAmelCase						,					decoder_attention_mask=__UpperCAmelCase						,					)
    __lowerCamelCase    				=     model(input_ids=__UpperCAmelCase						,					decoder_input_ids=__UpperCAmelCase					)
    __lowerCamelCase    				=     result.last_hidden_state
    __lowerCamelCase    				=     result.past_key_values
    __lowerCamelCase    				=     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(__UpperCAmelCase					)						,					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 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     UMTaModel(config=__UpperCAmelCase					).get_decoder().to(__UpperCAmelCase					).eval()
    # first forward pass
    __lowerCamelCase    				=     model(__UpperCAmelCase						,					use_cache=__UpperCAmelCase					)
    __lowerCamelCase    				=     model(__UpperCAmelCase					)
    __lowerCamelCase    				=     model(__UpperCAmelCase						,					use_cache=__UpperCAmelCase					)

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

    __lowerCamelCase				,__lowerCamelCase    				=     outputs.to_tuple()

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

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

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

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

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




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     UMTaModel(config=__UpperCAmelCase					).to(__UpperCAmelCase					).half().eval()
    __lowerCamelCase    				=     model(**__UpperCAmelCase					)['''last_hidden_state''']
    self.parent.assertFalse(torch.isnan(__UpperCAmelCase					).any().item()					)



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




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     UMTaModelTester(self					)




 @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''					)
 def 							lowerCamelCase   (    self					):



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




 @unittest.skipIf(torch_device == '''cpu'''						,					'''Cant do half precision'''					)
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model_fpaa_forward(*__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
    __lowerCamelCase    				=     self.model_tester.prepare_config_and_inputs()
    __lowerCamelCase    				=     config_and_inputs[0]
    __lowerCamelCase    				=     UMTaForConditionalGeneration(__UpperCAmelCase					).eval()
    model.to(__UpperCAmelCase					)

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

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

       __lowerCamelCase    				=     model.generate(
           config_and_inputs[1]['''input_ids''']						,					num_beams=1						,					max_length=3						,					output_attentions=__UpperCAmelCase						,					return_dict_in_generate=__UpperCAmelCase						,					**__UpperCAmelCase						,					)
       # We check the state of decoder_attentions and cross_attentions just from the last step
       __lowerCamelCase    				=     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 							lowerCamelCase   (    self					):



    '''simple docstring'''
    pass



@require_torch
@require_sentencepiece
@require_tokenizers
class 			__lowerCAmelCase  (						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 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''						,					return_dict=__UpperCAmelCase					).to(__UpperCAmelCase					)
    __lowerCamelCase    				=     AutoTokenizer.from_pretrained('''google/umt5-small'''						,					use_fast=__UpperCAmelCase						,					legacy=__UpperCAmelCase					)
    __lowerCamelCase    				=     [
        '''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>.''',
    ]
    __lowerCamelCase    				=     tokenizer(__UpperCAmelCase						,					return_tensors='''pt'''						,					padding=__UpperCAmelCase					).input_ids
    # fmt: off
    __lowerCamelCase    				=     torch.tensor(
        [
            [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
            [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
            [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
            [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
            [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
        ]					)
    # fmt: on
    torch.testing.assert_allclose(__UpperCAmelCase						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     model.generate(input_ids.to(__UpperCAmelCase					)					)
    __lowerCamelCase    				=     [
        '''<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>''',
    ]
    __lowerCamelCase    				=     tokenizer.batch_decode(__UpperCAmelCase					)
    self.assertEqual(__UpperCAmelCase						,					__UpperCAmelCase					)






