






"""simple docstring"""





import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


UpperCamelCase					=     logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							None







def 							A(    snake_case_		,  snake_case_=0.9_99		,  snake_case_="cosine"		,  ):
       """simple docstring"""





       if alpha_transform_type == "cosine":

              def alpha_bar_fn(snake_case_						):
                     return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2						) ** 2

       elif alpha_transform_type == "exp":

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

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

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







class 						_a  (		lowercase_							,	lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							[e.name for e in KarrasDiffusionSchedulers]
   UpperCamelCase__     	=							1





   @register_to_config
   def __init__(       self					, UpperCAmelCase_ = 1_000					, UpperCAmelCase_ = 0.00_01					, UpperCAmelCase_ = 0.02					, UpperCAmelCase_ = "linear"					, UpperCAmelCase_ = None					, UpperCAmelCase_ = "epsilon"					, UpperCAmelCase_ = "linspace"					, UpperCAmelCase_ = 0					, )			->   int:




          '''simple docstring'''




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

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

          lowercase__:							str         =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          lowercase__:							Any         =     np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
          lowercase__:							Any         =     torch.from_numpy(UpperCAmelCase_)

          # setable values
          lowercase__:							Any         =     None
          lowercase__:							List[str]         =     np.linspace(0					, num_train_timesteps - 1					, UpperCAmelCase_					, dtype=UpperCAmelCase_)[::-1].copy()
          lowercase__:							Any         =     torch.from_numpy(UpperCAmelCase_)
          lowercase__:							Any         =     False





   @property
   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




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

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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   torch.FloatTensor:




          '''simple docstring'''




          if isinstance(UpperCAmelCase_					, torch.Tensor):
                 lowercase__:							Optional[int]         =     timestep.to(self.timesteps.device)
          lowercase__:							Tuple         =     (self.timesteps == timestep).nonzero().item()
          lowercase__:							List[str]         =     self.sigmas[step_index]
          lowercase__:							Tuple         =     sample / ((sigma**2 + 1) ** 0.5)
          lowercase__:							Optional[Any]         =     True
          return sample





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_ = None)			->   Union[str, Any]:




          '''simple docstring'''




          lowercase__:							int         =     num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
                 lowercase__:							Optional[Any]         =     np.linspace(0					, self.config.num_train_timesteps - 1					, UpperCAmelCase_					, dtype=UpperCAmelCase_)[
                     ::-1
                 ].copy()
          elif self.config.timestep_spacing == "leading":
                 lowercase__:							List[str]         =     self.config.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
                 lowercase__:							List[Any]         =     (np.arange(0					, UpperCAmelCase_) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_)
                 timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
                 lowercase__:							List[Any]         =     self.config.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
                 lowercase__:							Optional[int]         =     (np.arange(self.config.num_train_timesteps					, 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'.""")

          lowercase__:							str         =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          lowercase__:							List[Any]         =     np.interp(UpperCAmelCase_					, np.arange(0					, len(UpperCAmelCase_))					, UpperCAmelCase_)
          lowercase__:							int         =     np.concatenate([sigmas, [0.0]]).astype(np.floataa)
          lowercase__:							List[Any]         =     torch.from_numpy(UpperCAmelCase_).to(device=UpperCAmelCase_)
          if str(UpperCAmelCase_).startswith("mps"):
                 # mps does not support float64
                 lowercase__:							List[Any]         =     torch.from_numpy(UpperCAmelCase_).to(UpperCAmelCase_					, dtype=torch.floataa)
          else:
                 lowercase__:							Union[str, Any]         =     torch.from_numpy(UpperCAmelCase_).to(device=UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_ = None					, UpperCAmelCase_ = True					, )			->   Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:




          '''simple docstring'''




          if (
              isinstance(UpperCAmelCase_					, UpperCAmelCase_)
              or isinstance(UpperCAmelCase_					, torch.IntTensor)
              or isinstance(UpperCAmelCase_					, torch.LongTensor)
          ):
                 raise ValueError(
                     (
                         "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                         " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                         " one of the `scheduler.timesteps` as a timestep."
                     )					, )

          if not self.is_scale_input_called:
                 logger.warning(
                     "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                     "See `StableDiffusionPipeline` for a usage example.")

          if isinstance(UpperCAmelCase_					, torch.Tensor):
                 lowercase__:							Tuple         =     timestep.to(self.timesteps.device)

          lowercase__:							int         =     (self.timesteps == timestep).nonzero().item()
          lowercase__:							List[Any]         =     self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
                 lowercase__:							Optional[Any]         =     sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
                 # * c_out + input * c_skip
                 lowercase__:							Optional[Any]         =     model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`""")

          lowercase__:							Optional[int]         =     self.sigmas[step_index]
          lowercase__:							int         =     self.sigmas[step_index + 1]
          lowercase__:							Optional[int]         =     (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          lowercase__:							Any         =     (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          lowercase__:							List[str]         =     (sample - pred_original_sample) / sigma

          lowercase__:							Union[str, Any]         =     sigma_down - sigma

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

          lowercase__:							Tuple         =     model_output.device
          lowercase__:							Tuple         =     randn_tensor(model_output.shape					, dtype=model_output.dtype					, device=UpperCAmelCase_					, generator=UpperCAmelCase_)

          lowercase__:							Tuple         =     prev_sample + noise * sigma_up

          if not return_dict:
                 return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=UpperCAmelCase_					, pred_original_sample=UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, )			->   torch.FloatTensor:




          '''simple docstring'''




          lowercase__:							List[Any]         =     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
                 lowercase__:							str         =     self.timesteps.to(original_samples.device					, dtype=torch.floataa)
                 lowercase__:							Any         =     timesteps.to(original_samples.device					, dtype=torch.floataa)
          else:
                 lowercase__:							List[Any]         =     self.timesteps.to(original_samples.device)
                 lowercase__:							Optional[Any]         =     timesteps.to(original_samples.device)

          lowercase__:							List[str]         =     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

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





   def __len__(       self)			->   Dict:




          '''simple docstring'''




          return self.config.num_train_timesteps








import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          FunnelBaseModel,
          FunnelForMaskedLM,
          FunnelForMultipleChoice,
          FunnelForPreTraining,
          FunnelForQuestionAnswering,
          FunnelForSequenceClassification,
          FunnelForTokenClassification,
          FunnelModel,
      )






class     a_       :



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase=13				,       _lowerCamelCase=7				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase=99				,       _lowerCamelCase=[1, 1, 2]				,       _lowerCamelCase=1				,       _lowerCamelCase=32				,       _lowerCamelCase=4				,       _lowerCamelCase=8				,       _lowerCamelCase=37				,       _lowerCamelCase="gelu_new"				,       _lowerCamelCase=0.1				,       _lowerCamelCase=0.1				,       _lowerCamelCase=0.0				,       _lowerCamelCase=512				,       _lowerCamelCase=3				,       _lowerCamelCase=0.0_2				,       _lowerCamelCase=3				,       _lowerCamelCase=4				,       _lowerCamelCase=None				,       _lowerCamelCase=False				,       )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Any				      = parent
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = batch_size
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = seq_length
           SCREAMING_SNAKE_CASE				:				Dict				      = is_training
           SCREAMING_SNAKE_CASE				:				int				      = use_input_mask
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = use_token_type_ids
           SCREAMING_SNAKE_CASE				:				Tuple				      = use_labels
           SCREAMING_SNAKE_CASE				:				List[str]				      = vocab_size
           SCREAMING_SNAKE_CASE				:				Tuple				      = block_sizes
           SCREAMING_SNAKE_CASE				:				str				      = num_decoder_layers
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = d_model
           SCREAMING_SNAKE_CASE				:				str				      = n_head
           SCREAMING_SNAKE_CASE				:				List[str]				      = d_head
           SCREAMING_SNAKE_CASE				:				Dict				      = d_inner
           SCREAMING_SNAKE_CASE				:				Any				      = hidden_act
           SCREAMING_SNAKE_CASE				:				Dict				      = hidden_dropout
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = attention_dropout
           SCREAMING_SNAKE_CASE				:				List[Any]				      = activation_dropout
           SCREAMING_SNAKE_CASE				:				str				      = max_position_embeddings
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = type_vocab_size
           SCREAMING_SNAKE_CASE				:				Dict				      = 2
           SCREAMING_SNAKE_CASE				:				Tuple				      = num_labels
           SCREAMING_SNAKE_CASE				:				str				      = num_choices
           SCREAMING_SNAKE_CASE				:				Tuple				      = scope
           SCREAMING_SNAKE_CASE				:				List[str]				      = initializer_std

           # Used in the tests to check the size of the first attention layer
           SCREAMING_SNAKE_CASE				:				str				      = n_head
           # Used in the tests to check the size of the first hidden state
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.d_model
           # Used in the tests to check the number of output hidden states/attentions
           SCREAMING_SNAKE_CASE				:				str				      = sum(self.block_sizes   ) + (0 if base else self.num_decoder_layers)
           # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
           # the last hidden state of the first block (which is the first hidden state of the decoder).
           if not base:
               SCREAMING_SNAKE_CASE				:				Tuple				      = self.num_hidden_layers + 2






       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				str				      = ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size   )

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = None
           if self.use_input_mask:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = ids_tensor([self.batch_size, self.seq_length]				,       vocab_size=2   )

           SCREAMING_SNAKE_CASE				:				List[str]				      = None
           if self.use_token_type_ids:
               SCREAMING_SNAKE_CASE				:				Dict				      = ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size   )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = None
           SCREAMING_SNAKE_CASE				:				Tuple				      = None
           SCREAMING_SNAKE_CASE				:				List[str]				      = None
           if self.use_labels:
               SCREAMING_SNAKE_CASE				:				Tuple				      = ids_tensor([self.batch_size]				,       self.type_sequence_label_size   )
               SCREAMING_SNAKE_CASE				:				str				      = ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels   )
               SCREAMING_SNAKE_CASE				:				Any				      = ids_tensor([self.batch_size]				,       self.num_choices   )
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = ids_tensor([self.batch_size, self.seq_length]				,       1   )

           SCREAMING_SNAKE_CASE				:				Tuple				      = self.get_config()

           return (
               config,
               input_ids,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
               choice_labels,
               fake_token_labels,
           )






       def 	__lowerCAmelCase     (		self   )					->str:
           return FunnelConfig(
               vocab_size=self.vocab_size				,       block_sizes=self.block_sizes				,       num_decoder_layers=self.num_decoder_layers				,       d_model=self.d_model				,       n_head=self.n_head				,       d_head=self.d_head				,       d_inner=self.d_inner				,       hidden_act=self.hidden_act				,       hidden_dropout=self.hidden_dropout				,       attention_dropout=self.attention_dropout				,       activation_dropout=self.activation_dropout				,       max_position_embeddings=self.max_position_embeddings				,       type_vocab_size=self.type_vocab_size				,       initializer_std=self.initializer_std				,       )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = FunnelModel(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Dict				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = model(_lowerCamelCase				,       token_type_ids=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = model(_lowerCamelCase   )
           self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.d_model)   )

           SCREAMING_SNAKE_CASE				:				int				      = False
           SCREAMING_SNAKE_CASE				:				List[Any]				      = model(_lowerCamelCase   )
           self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.d_model)   )

           SCREAMING_SNAKE_CASE				:				str				      = False
           SCREAMING_SNAKE_CASE				:				List[str]				      = model(_lowerCamelCase   )
           self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.d_model)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Any:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = FunnelBaseModel(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Dict				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = model(_lowerCamelCase				,       token_type_ids=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = model(_lowerCamelCase   )
           self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, 2, self.d_model)   )

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = False
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = model(_lowerCamelCase   )
           self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, 3, self.d_model)   )

           SCREAMING_SNAKE_CASE				:				List[Any]				      = False
           SCREAMING_SNAKE_CASE				:				Tuple				      = model(_lowerCamelCase   )
           self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, 2, self.d_model)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->int:
           SCREAMING_SNAKE_CASE				:				int				      = self.num_labels
           SCREAMING_SNAKE_CASE				:				int				      = FunnelForPreTraining(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				str				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       labels=_lowerCamelCase   )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = FunnelForMaskedLM(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       labels=_lowerCamelCase   )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.num_labels
           SCREAMING_SNAKE_CASE				:				Any				      = FunnelForSequenceClassification(_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				int				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       labels=_lowerCamelCase   )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->str:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.num_choices
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = FunnelForMultipleChoice(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Tuple				      = input_ids.unsqueeze(1   ).expand(-1				,       self.num_choices				,       -1   ).contiguous()
           SCREAMING_SNAKE_CASE				:				Dict				      = token_type_ids.unsqueeze(1   ).expand(-1				,       self.num_choices				,       -1   ).contiguous()
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = input_mask.unsqueeze(1   ).expand(-1				,       self.num_choices				,       -1   ).contiguous()
           SCREAMING_SNAKE_CASE				:				str				      = model(
               _lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       labels=_lowerCamelCase				,       )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_choices)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Tuple:
           SCREAMING_SNAKE_CASE				:				str				      = self.num_labels
           SCREAMING_SNAKE_CASE				:				int				      = FunnelForTokenClassification(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Dict				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       labels=_lowerCamelCase   )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Tuple:
           SCREAMING_SNAKE_CASE				:				List[str]				      = FunnelForQuestionAnswering(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				int				      = model(
               _lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase				,       start_positions=_lowerCamelCase				,       end_positions=_lowerCamelCase				,       )
           self.parent.assertEqual(result.start_logits.shape				,       (self.batch_size, self.seq_length)   )
           self.parent.assertEqual(result.end_logits.shape				,       (self.batch_size, self.seq_length)   )






       def 	__lowerCAmelCase     (		self   )					->str:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.prepare_config_and_inputs()
           (
               (
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							
           )				:				Optional[int]				      = config_and_inputs
           SCREAMING_SNAKE_CASE				:				int				      = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict






@require_torch
class     a_       (			a__				,    a__				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					str						=  False
       __SCREAMING_SNAKE_CASE       :					List[str]						=  False
       __SCREAMING_SNAKE_CASE       :					Tuple						=  (
           (
               FunnelModel,
               FunnelForMaskedLM,
               FunnelForPreTraining,
               FunnelForQuestionAnswering,
               FunnelForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       __SCREAMING_SNAKE_CASE       :					Tuple						=  (
           {
               'feature-extraction': (FunnelBaseModel, FunnelModel),
               'fill-mask': FunnelForMaskedLM,
               'question-answering': FunnelForQuestionAnswering,
               'text-classification': FunnelForSequenceClassification,
               'token-classification': FunnelForTokenClassification,
               'zero-shot': FunnelForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase=False   )					->str:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = super()._prepare_for_class(_lowerCamelCase				,       _lowerCamelCase				,       return_labels=_lowerCamelCase   )

           if return_labels:
               if model_class in get_values(_lowerCamelCase   ):
                   SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.seq_length)				,       dtype=torch.long				,       device=_lowerCamelCase   )
           return inputs_dict






       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = FunnelModelTester(self   )
           SCREAMING_SNAKE_CASE				:				Any				      = ConfigTester(self				,       config_class=_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->int:
           self.config_tester.run_common_tests()






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				Any				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Dict:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Any				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->List[Any]:
           if hasattr(_lowerCamelCase				,       '''weight'''   ) and module.weight is not None:
               module.weight.data.fill_(3   )
           if hasattr(_lowerCamelCase				,       '''bias'''   ) and module.bias is not None:
               module.bias.data.fill_(3   )

           for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
               if hasattr(_lowerCamelCase				,       _lowerCamelCase   ) and getattr(_lowerCamelCase				,       _lowerCamelCase   ) is not None:
                   SCREAMING_SNAKE_CASE				:				int				      = getattr(_lowerCamelCase				,       _lowerCamelCase   )
                   weight.data.fill_(3   )






@require_torch
class     a_       (			a__				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					List[str]						=  False
       __SCREAMING_SNAKE_CASE       :					Any						=  False
       __SCREAMING_SNAKE_CASE       :					Dict						=  (
           (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
       )






       def 	__lowerCAmelCase     (		self   )					->Dict:
           SCREAMING_SNAKE_CASE				:				str				      = FunnelModelTester(self				,       base=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = ConfigTester(self				,       config_class=_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           self.config_tester.run_common_tests()






       def 	__lowerCAmelCase     (		self   )					->Any:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_base_model(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Dict:
           SCREAMING_SNAKE_CASE				:				Any				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Tuple				      = self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE				:				int				      = True

           for model_class in self.all_model_classes:
               if model_class.__name__ == "FunnelBaseModel":
                   continue
               SCREAMING_SNAKE_CASE				:				List[str]				      = model_class(_lowerCamelCase   )
               model.to(_lowerCamelCase   )
               model.train()
               SCREAMING_SNAKE_CASE				:				List[str]				      = self._prepare_for_class(_lowerCamelCase				,       _lowerCamelCase				,       return_labels=_lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				int				      = model(**_lowerCamelCase   ).loss
               loss.backward()






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Union[str, Any]:
           if hasattr(_lowerCamelCase				,       '''weight'''   ) and module.weight is not None:
               module.weight.data.fill_(3   )
           if hasattr(_lowerCamelCase				,       '''bias'''   ) and module.bias is not None:
               module.bias.data.fill_(3   )

           for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
               if hasattr(_lowerCamelCase				,       _lowerCamelCase   ) and getattr(_lowerCamelCase				,       _lowerCamelCase   ) is not None:
                   SCREAMING_SNAKE_CASE				:				List[str]				      = getattr(_lowerCamelCase				,       _lowerCamelCase   )
                   weight.data.fill_(3   )






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



       """simple docstring"""






       def 	__lowerCAmelCase     (		self   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = 13
           SCREAMING_SNAKE_CASE				:				List[str]				      = 7
           SCREAMING_SNAKE_CASE				:				Any				      = torch.arange(0				,       batch_size * sequence_length   ).long().reshape(_lowerCamelCase				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
           SCREAMING_SNAKE_CASE				:				Tuple				      = torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]   )

           SCREAMING_SNAKE_CASE				:				Tuple				      = FunnelModel.from_pretrained('''sgugger/funnel-random-tiny'''   )
           SCREAMING_SNAKE_CASE				:				Any				      = model(_lowerCamelCase				,       token_type_ids=_lowerCamelCase   )[0].abs()

           SCREAMING_SNAKE_CASE				:				str				      = torch.tensor(2_3_4_4.8_3_5_2   )
           SCREAMING_SNAKE_CASE				:				Any				      = torch.tensor(0.8_0_5_2   )
           self.assertTrue(torch.allclose(output.sum()				,       _lowerCamelCase				,       atol=1e-4   )   )
           self.assertTrue(torch.allclose(output.mean()				,       _lowerCamelCase				,       atol=1e-4   )   )

           SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase   )[0].abs()

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor(2_3_4_3.8_4_2_5   )
           SCREAMING_SNAKE_CASE				:				Dict				      = torch.tensor(0.8_0_4_9   )
           self.assertTrue(torch.allclose(output.sum()				,       _lowerCamelCase				,       atol=1e-4   )   )
           self.assertTrue(torch.allclose(output.mean()				,       _lowerCamelCase				,       atol=1e-4   )   )






       @slow
       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				str				      = FunnelTokenizer.from_pretrained('''huggingface/funnel-small'''   )
           SCREAMING_SNAKE_CASE				:				Any				      = FunnelModel.from_pretrained('''huggingface/funnel-small'''   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = tokenizer('''Hello! I am the Funnel Transformer model.'''				,       return_tensors='''pt'''   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = model(**_lowerCamelCase   )[0]

           SCREAMING_SNAKE_CASE				:				Dict				      = torch.tensor(2_3_5.7_2_4_6   )
           SCREAMING_SNAKE_CASE				:				Dict				      = torch.tensor(0.0_2_5_6   )
           self.assertTrue(torch.allclose(output.sum()				,       _lowerCamelCase				,       atol=1e-4   )   )
           self.assertTrue(torch.allclose(output.mean()				,       _lowerCamelCase				,       atol=1e-4   )   )
