






'''simple docstring'''






from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput




@dataclass
class a_    (    UpperCamelCase_							):
   _snake_case        =							None
   _snake_case        =							None
   _snake_case        =							None
   _snake_case        =							None




class a_    (    UpperCamelCase_							):







   def __init__(self					,   __a=1					,   __a=0					,   __a=2					,   __a=5_1_2					,   __a="cls"					,   __a=False					,   __a=True					,   **__a					,   )			->      Any:
      """simple docstring"""






      super().__init__(pad_token_id=__a					,   bos_token_id=__a					,   eos_token_id=__a					,   **__a)
      __snake_case				:      Optional[int]     							= project_dim
      __snake_case				:      Tuple     							= pooler_fn
      __snake_case				:      Tuple     							= learn_encoder
      __snake_case				:      Union[str, Any]     							= use_attention_mask




class a_    (    UpperCamelCase_							):
   _snake_case        =							[r"""pooler""", r"""logit_scale"""]
   _snake_case        =							[r"""position_ids""", r"""predictions.decoder.bias"""]
   _snake_case        =							"""roberta"""
   _snake_case        =							RobertaSeriesConfig







   def __init__(self					,   __a)			->      Optional[int]:
      """simple docstring"""






      super().__init__(__a)
      __snake_case				:      List[Any]     							= XLMRobertaModel(__a)
      __snake_case				:      Optional[int]     							= nn.Linear(config.hidden_size					,   config.project_dim)
      __snake_case				:      Optional[int]     							= getattr(__a					,   'has_pre_transformation'					,   __a)
      if self.has_pre_transformation:
         __snake_case				:      Optional[int]     							= nn.Linear(config.hidden_size					,   config.project_dim)
         __snake_case				:      Dict     							= nn.LayerNorm(config.hidden_size					,   eps=config.layer_norm_eps)
      self.post_init()







   def     SCREAMING_SNAKE_CASE__					(self					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   )			->      List[str]:
      """simple docstring"""






      __snake_case				:      int     							= return_dict if return_dict is not None else self.config.use_return_dict

      __snake_case				:      Optional[int]     							= self.base_model(
          input_ids=__a					,   attention_mask=__a					,   token_type_ids=__a					,   position_ids=__a					,   head_mask=__a					,   inputs_embeds=__a					,   encoder_hidden_states=__a					,   encoder_attention_mask=__a					,   output_attentions=__a					,   output_hidden_states=True if self.has_pre_transformation else output_hidden_states					,   return_dict=__a					,   )

      if self.has_pre_transformation:
         __snake_case				:      int     							= outputs['hidden_states'][-2]
         __snake_case				:      List[str]     							= self.pre_LN(__a)
         __snake_case				:      Union[str, Any]     							= self.transformation_pre(__a)

         return TransformationModelOutput(
             projection_state=__a					,   last_hidden_state=outputs.last_hidden_state					,   hidden_states=outputs.hidden_states					,   attentions=outputs.attentions					,   )
      else:
         __snake_case				:      Any     							= self.transformation(outputs.last_hidden_state)
         return TransformationModelOutput(
             projection_state=__a					,   last_hidden_state=outputs.last_hidden_state					,   hidden_states=outputs.hidden_states					,   attentions=outputs.attentions					,   )







'''simple docstring'''






import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
      import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin




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







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[str]     							= load_tool('text-to-speech')
      self.tool.setup()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Dict     							= self.tool('hey')
      __snake_case				:      List[Any]     							= result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]					,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485])					,   ))







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Any     							= self.tool('hey')
      __snake_case				:      Any     							= result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]					,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485])					,   ))