code: 
  
  import math
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, randn_tensor
  from .scheduling_utils import SchedulerMixin
  @dataclass
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       42
      a__     							=       42
  
  
  
  
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE=0.999       ,   SCREAMING_SNAKE_CASE="cosine"       ,   )      ->     Any:
  
  
  
  
  
  
      '''simple docstring'''
      if alpha_transform_type == "cosine":
  
          def alpha_bar_fn(SCREAMING_SNAKE_CASE      ):
              return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2
  
      elif alpha_transform_type == "exp":
  
          def alpha_bar_fn(SCREAMING_SNAKE_CASE      ):
              return math.exp(t * -12.0      )
  
      else:
          raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''      )
  
      __UpperCAmelCase									=			[]
      for i in range(SCREAMING_SNAKE_CASE      ):
          __UpperCAmelCase									=			i / num_diffusion_timesteps
          __UpperCAmelCase									=			(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 					A_   (      _a ,     _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       1
  
  
      @register_to_config
      def __init__(self     ,      lowercase__ = 1_000     ,      lowercase__ = 0.0001     ,      lowercase__ = 0.02     ,      lowercase__ = "linear"     ,      lowercase__ = 0.0     ,      lowercase__ = None     ,      lowercase__ = True     ,      )     ->      Tuple:
          if trained_betas is not None:
              __UpperCAmelCase									=			torch.from_numpy(lowercase__  )
          elif beta_schedule == "linear":
              __UpperCAmelCase									=			torch.linspace(lowercase__     ,      lowercase__     ,      lowercase__     ,      dtype=torch.floataa  )
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              __UpperCAmelCase									=			(
                  torch.linspace(beta_start**0.5     ,      beta_end**0.5     ,      lowercase__     ,      dtype=torch.floataa  ) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              __UpperCAmelCase									=			betas_for_alpha_bar(lowercase__  )
          elif beta_schedule == "sigmoid":
              # GeoDiff sigmoid schedule
              __UpperCAmelCase									=			torch.linspace(-6     ,      6     ,      lowercase__  )
              __UpperCAmelCase									=			torch.sigmoid(lowercase__  ) * (beta_end - beta_start) + beta_start
          else:
              raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''  )
  
          __UpperCAmelCase									=			1.0 - self.betas
          __UpperCAmelCase									=			torch.cumprod(self.alphas     ,      dim=0  )
          __UpperCAmelCase									=			torch.tensor(1.0  )
  
          __UpperCAmelCase									=			torch.tensor(1.0  )
  
          # standard deviation of the initial noise distribution
          __UpperCAmelCase									=			1.0
  
          # setable values
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			torch.from_numpy(np.arange(0     ,      lowercase__  )[::-1].copy()  )
  
          __UpperCAmelCase									=			eta
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__ = None  )     ->      torch.FloatTensor:
          return sample
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__ = 10     ,      lowercase__ = 10     ,      lowercase__ = None     ,      )     ->      Union[str, Any]:
          __UpperCAmelCase									=			min(self.config.num_train_timesteps     ,      lowercase__  )
          __UpperCAmelCase									=			num_inference_steps
  
          __UpperCAmelCase									=			[]
  
          __UpperCAmelCase									=			{}
          for j in range(0     ,      num_inference_steps - jump_length     ,      lowercase__  ):
              __UpperCAmelCase									=			jump_n_sample - 1
  
          __UpperCAmelCase									=			num_inference_steps
          while t >= 1:
              __UpperCAmelCase									=			t - 1
              timesteps.append(lowercase__  )
  
              if jumps.get(lowercase__     ,      0  ) > 0:
                  __UpperCAmelCase									=			jumps[t] - 1
                  for _ in range(lowercase__  ):
                      __UpperCAmelCase									=			t + 1
                      timesteps.append(lowercase__  )
  
          __UpperCAmelCase									=			np.array(lowercase__  ) * (self.config.num_train_timesteps // self.num_inference_steps)
          __UpperCAmelCase									=			torch.from_numpy(lowercase__  ).to(lowercase__  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      Tuple:
          __UpperCAmelCase									=			t - self.config.num_train_timesteps // self.num_inference_steps
  
          __UpperCAmelCase									=			self.alphas_cumprod[t]
          __UpperCAmelCase									=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
          __UpperCAmelCase									=			1 - alpha_prod_t
          __UpperCAmelCase									=			1 - alpha_prod_t_prev
  
          # For t > 0, compute predicted variance βt (see formula (6) and (7) from
          # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
          # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
          # variance to pred_sample
          # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
          # without eta.
          # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
          __UpperCAmelCase									=			(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
          return variance
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__ = None     ,      lowercase__ = True     ,      )     ->      Union[RePaintSchedulerOutput, Tuple]:
          __UpperCAmelCase									=			timestep
          __UpperCAmelCase									=			timestep - self.config.num_train_timesteps // self.num_inference_steps
  
          # 1. compute alphas, betas
          __UpperCAmelCase									=			self.alphas_cumprod[t]
          __UpperCAmelCase									=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
          __UpperCAmelCase									=			1 - alpha_prod_t
  
          # 2. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
          __UpperCAmelCase									=			(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
  
          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
              __UpperCAmelCase									=			torch.clamp(lowercase__     ,      -1     ,      1  )
  
          # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
          # substitute formula (7) in the algorithm coming from DDPM paper
          # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
          # DDIM schedule gives the same results as DDPM with eta = 1.0
          # Noise is being reused in 7. and 8., but no impact on quality has
          # been observed.
  
          # 5. Add noise
          __UpperCAmelCase									=			model_output.device
          __UpperCAmelCase									=			randn_tensor(model_output.shape     ,      generator=lowercase__     ,      device=lowercase__     ,      dtype=model_output.dtype  )
          __UpperCAmelCase									=			self.eta * self._get_variance(lowercase__  ) ** 0.5
  
          __UpperCAmelCase									=			0
          if t > 0 and self.eta > 0:
              __UpperCAmelCase									=			std_dev_t * noise
  
          # 6. compute "direction pointing to x_t" of formula (12)
          # from https://arxiv.org/pdf/2010.02502.pdf
          __UpperCAmelCase									=			(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
  
          # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          __UpperCAmelCase									=			alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
  
          # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
          __UpperCAmelCase									=			(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
  
          # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
          __UpperCAmelCase									=			mask * prev_known_part + (1.0 - mask) * prev_unknown_part
  
          if not return_dict:
              return (
                  pred_prev_sample,
                  pred_original_sample,
              )
  
          return RePaintSchedulerOutput(prev_sample=lowercase__     ,      pred_original_sample=lowercase__  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__=None  )     ->      List[Any]:
          __UpperCAmelCase									=			self.config.num_train_timesteps // self.num_inference_steps
  
          for i in range(lowercase__  ):
              __UpperCAmelCase									=			self.betas[timestep + i]
              if sample.device.type == "mps":
                  # randn does not work reproducibly on mps
                  __UpperCAmelCase									=			randn_tensor(sample.shape     ,      dtype=sample.dtype     ,      generator=lowercase__  )
                  __UpperCAmelCase									=			noise.to(sample.device  )
              else:
                  __UpperCAmelCase									=			randn_tensor(sample.shape     ,      generator=lowercase__     ,      device=sample.device     ,      dtype=sample.dtype  )
  
              # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
              __UpperCAmelCase									=			(1 - beta) ** 0.5 * sample + beta**0.5 * noise
  
          return sample
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      torch.FloatTensor:
          raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''  )
  
  
  
  
  
  
      def __len__(self  )     ->      List[Any]:
          return self.config.num_train_timesteps
  
  
  
  
  
style_context: 
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
         import tensorflow as tf
  
         from transformers import (
             TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFXLMForMultipleChoice,
             TFXLMForQuestionAnsweringSimple,
             TFXLMForSequenceClassification,
             TFXLMForTokenClassification,
             TFXLMModel,
             TFXLMWithLMHeadModel,
             XLMConfig,
         )
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__     ,      )     ->      Tuple:
          __UpperCAmelCase									=			parent
          __UpperCAmelCase									=			13
          __UpperCAmelCase									=			7
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			False
          __UpperCAmelCase									=			False
          __UpperCAmelCase									=			False
          __UpperCAmelCase									=			2
          __UpperCAmelCase									=			99
          __UpperCAmelCase									=			0
          __UpperCAmelCase									=			32
          __UpperCAmelCase									=			2
          __UpperCAmelCase									=			4
          __UpperCAmelCase									=			0.1
          __UpperCAmelCase									=			0.1
          __UpperCAmelCase									=			512
          __UpperCAmelCase									=			16
          __UpperCAmelCase									=			2
          __UpperCAmelCase									=			0.02
          __UpperCAmelCase									=			3
          __UpperCAmelCase									=			4
          __UpperCAmelCase									=			'''last'''
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			0
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size  )
          __UpperCAmelCase									=			random_attention_mask([self.batch_size, self.seq_length]     ,      dtype=tf.floataa  )
  
          __UpperCAmelCase									=			None
          if self.use_input_lengths:
              __UpperCAmelCase									=			(
                  ids_tensor([self.batch_size]     ,      vocab_size=2  ) + self.seq_length - 2
              )  # small variation of seq_length
  
          __UpperCAmelCase									=			None
          if self.use_token_type_ids:
              __UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length]     ,      self.n_langs  )
  
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			None
          if self.use_labels:
              __UpperCAmelCase									=			ids_tensor([self.batch_size]     ,      self.type_sequence_label_size  )
              __UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels  )
              __UpperCAmelCase									=			ids_tensor([self.batch_size]     ,      2     ,      dtype=tf.floataa  )
              __UpperCAmelCase									=			ids_tensor([self.batch_size]     ,      self.num_choices  )
  
          __UpperCAmelCase									=			XLMConfig(
              vocab_size=self.vocab_size     ,      n_special=self.n_special     ,      emb_dim=self.hidden_size     ,      n_layers=self.num_hidden_layers     ,      n_heads=self.num_attention_heads     ,      dropout=self.hidden_dropout_prob     ,      attention_dropout=self.attention_probs_dropout_prob     ,      gelu_activation=self.gelu_activation     ,      sinusoidal_embeddings=self.sinusoidal_embeddings     ,      asm=self.asm     ,      causal=self.causal     ,      n_langs=self.n_langs     ,      max_position_embeddings=self.max_position_embeddings     ,      initializer_range=self.initializer_range     ,      summary_type=self.summary_type     ,      use_proj=self.use_proj     ,      bos_token_id=self.bos_token_id     ,      )
  
          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      List[Any]:
          __UpperCAmelCase									=			TFXLMModel(config=lowercase__  )
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
          __UpperCAmelCase									=			model(lowercase__  )
  
          __UpperCAmelCase									=			[input_ids, input_mask]
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      List[str]:
          __UpperCAmelCase									=			TFXLMWithLMHeadModel(lowercase__  )
  
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
          __UpperCAmelCase									=			model(lowercase__  )
  
          __UpperCAmelCase									=			outputs
  
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      Optional[Any]:
          __UpperCAmelCase									=			TFXLMForQuestionAnsweringSimple(lowercase__  )
  
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''lengths''': input_lengths}
  
          __UpperCAmelCase									=			model(lowercase__  )
  
          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     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      Dict:
          __UpperCAmelCase									=			TFXLMForSequenceClassification(lowercase__  )
  
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''lengths''': input_lengths}
  
          __UpperCAmelCase									=			model(lowercase__  )
  
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.type_sequence_label_size)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      int:
          __UpperCAmelCase									=			self.num_labels
          __UpperCAmelCase									=			TFXLMForTokenClassification(config=lowercase__  )
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      Tuple:
          __UpperCAmelCase									=			self.num_choices
          __UpperCAmelCase									=			TFXLMForMultipleChoice(config=lowercase__  )
          __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
          __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
          __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
          __UpperCAmelCase									=			{
              '''input_ids''': multiple_choice_inputs_ids,
              '''attention_mask''': multiple_choice_input_mask,
              '''token_type_ids''': multiple_choice_token_type_ids,
          }
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     
          )									=			config_and_inputs
          __UpperCAmelCase									=			{
              '''input_ids''': input_ids,
              '''token_type_ids''': token_type_ids,
              '''langs''': token_type_ids,
              '''lengths''': input_lengths,
          }
          return config, inputs_dict
  
  
  
  
  
  
  @require_tf
  class 					A_   (      _a ,     _a ,     unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       (
          (
              TFXLMModel,
              TFXLMWithLMHeadModel,
              TFXLMForSequenceClassification,
              TFXLMForQuestionAnsweringSimple,
              TFXLMForTokenClassification,
              TFXLMForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      a__     							=       (
          (TFXLMWithLMHeadModel,) if is_tf_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      a__     							=       (
          {
              "feature-extraction": TFXLMModel,
              "fill-mask": TFXLMWithLMHeadModel,
              "question-answering": TFXLMForQuestionAnsweringSimple,
              "text-classification": TFXLMForSequenceClassification,
              "text-generation": TFXLMWithLMHeadModel,
              "token-classification": TFXLMForTokenClassification,
              "zero-shot": TFXLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      a__     							=       False
      a__     							=       False
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith('''Fast'''  )
          ):
              # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
              # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
              # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
              return True
  
          return False
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          __UpperCAmelCase									=			TFXLMModelTester(self  )
          __UpperCAmelCase									=			ConfigTester(self     ,      config_class=lowercase__     ,      emb_dim=37  )
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          self.config_tester.run_common_tests()
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Any:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_token_classification(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*lowercase__  )
  
  
  
  
  
  
      @slow
      def      lowerCAmelCase_						(self  )     ->      int:
          for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCAmelCase									=			TFXLMModel.from_pretrained(lowercase__  )
              self.assertIsNotNone(lowercase__  )
  
  
  
  
  
  
  @require_tf
  class 					A_   (      unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      @slow
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          __UpperCAmelCase									=			TFXLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''  )
          __UpperCAmelCase									=			tf.convert_to_tensor([[14, 447]]     ,      dtype=tf.intaa  )  # the president
          __UpperCAmelCase									=			[
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
          ]  # the president the president the president the president the president the president the president the president the president the president
          # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
          __UpperCAmelCase									=			model.generate(lowercase__     ,      do_sample=lowercase__  )
          self.assertListEqual(output_ids[0].numpy().tolist()     ,      lowercase__  )
  
  
  
  
  