

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class     lowercase						(       _UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase					):



 '''simple docstring'''


 @register_to_config
 def __init__(self       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a = False       ,       ) ->					int:






  """simple docstring"""





  super().__init__()

  UpperCAmelCase__							=				nn.Linear(__a       ,       __a       ,       bias=__a )

  UpperCAmelCase__							=				nn.Embedding(__a       ,       __a )
  UpperCAmelCase__							=				False

  UpperCAmelCase__							=				nn.Dropout(p=__a )

  UpperCAmelCase__							=				TaConfig(
      d_model=__a       ,       num_heads=__a       ,       d_kv=__a       ,       d_ff=__a       ,       feed_forward_proj=__a       ,       dropout_rate=__a       ,       is_decoder=__a       ,       is_encoder_decoder=__a       ,       )
  UpperCAmelCase__							=				nn.ModuleList()
  for lyr_num in range(__a ):
   UpperCAmelCase__							=				TaBlock(__a )
   self.encoders.append(__a )

  UpperCAmelCase__							=				TaLayerNorm(__a )
  UpperCAmelCase__							=				nn.Dropout(p=__a )




 def   UpperCamelCase__							(self       ,       __a       ,       __a ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				self.input_proj(__a )

  # terminal relative positional encodings
  UpperCAmelCase__							=				encoder_inputs.shape[1]
  UpperCAmelCase__							=				torch.arange(__a       ,       device=encoder_inputs.device )

  UpperCAmelCase__							=				encoder_inputs_mask.sum(-1 )
  UpperCAmelCase__							=				torch.roll(input_positions.unsqueeze(0 )       ,       tuple(seq_lens.tolist() )       ,       dims=0 )
  x += self.position_encoding(__a )

  UpperCAmelCase__							=				self.dropout_pre(__a )

  # inverted the attention mask
  UpperCAmelCase__							=				encoder_inputs.size()
  UpperCAmelCase__							=				self.get_extended_attention_mask(__a       ,       __a )

  for lyr in self.encoders:
   UpperCAmelCase__							=				lyr(__a       ,       __a )[0]
  UpperCAmelCase__							=				self.layer_norm(__a )

  return self.dropout_post(__a ), encoder_inputs_mask






import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types



class     lowercase						(       unittest.TestCase ,				_UpperCamelCase					):



 '''simple docstring'''


 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				load_tool('translation' )
  self.tool.setup()
  UpperCAmelCase__							=				load_tool('translation'       ,       remote=__a )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tool('Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				self.remote_tool('Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tool(text='Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




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






  """simple docstring"""





  UpperCAmelCase__							=				self.remote_tool(text='Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']
  UpperCAmelCase__							=				self.tool(*__a )

  # There is a single output
  if len(self.tool.outputs ) == 1:
   UpperCAmelCase__							=				[outputs]

  self.assertListEqual(output_types(__a )       ,       self.tool.outputs )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']
  UpperCAmelCase__							=				self.tool(*__a )

  if not isinstance(__a       ,       __a ):
   UpperCAmelCase__							=				[outputs]

  self.assertEqual(len(__a )       ,       len(self.tool.outputs ) )

  for output, output_type in zip(__a       ,       self.tool.outputs ):
   UpperCAmelCase__							=				AGENT_TYPE_MAPPING[output_type]
   self.assertTrue(isinstance(__a       ,       __a ) )




 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']

  UpperCAmelCase__							=				[]

  for _input, input_type in zip(__a       ,       self.tool.inputs ):
   if isinstance(__a       ,       __a ):
    _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
   else:
    _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )

        # Should not raise an error
  UpperCAmelCase__							=				self.tool(*__a )

  if not isinstance(__a       ,       __a ):
   UpperCAmelCase__							=				[outputs]

  self.assertEqual(len(__a )       ,       len(self.tool.outputs ) )



