






'''simple docstring'''






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__A    	=   logging.get_logger(__name__)

__A    	=   {
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}




class a_    (    A_							):
   _snake_case        =							'''umt5'''
   _snake_case        =							['''past_key_values''']







   def __init__(self					,   __a=2_5_0_1_1_2					,   __a=5_1_2					,   __a=6_4					,   __a=1_0_2_4					,   __a=8					,   __a=None					,   __a=6					,   __a=3_2					,   __a=1_2_8					,   __a=0.1					,   __a=1E-6					,   __a=1.0					,   __a="gated-gelu"					,   __a=True					,   __a=True					,   __a="T5Tokenizer"					,   __a=True					,   __a=0					,   __a=1					,   __a=0					,   **__a					,   )			->      Tuple:
      """simple docstring"""






      super().__init__(
          is_encoder_decoder=__a					,   tokenizer_class=__a					,   tie_word_embeddings=__a					,   pad_token_id=__a					,   eos_token_id=__a					,   decoder_start_token_id=__a					,   **__a					,   )
      __snake_case				:      Optional[int]     							= vocab_size
      __snake_case				:      Dict     							= d_model
      __snake_case				:      Any     							= d_kv
      __snake_case				:      Optional[int]     							= d_ff
      __snake_case				:      List[str]     							= num_layers
      __snake_case				:      Any     							= (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      __snake_case				:      List[str]     							= num_heads
      __snake_case				:      Tuple     							= relative_attention_num_buckets
      __snake_case				:      Union[str, Any]     							= relative_attention_max_distance
      __snake_case				:      str     							= dropout_rate
      __snake_case				:      List[str]     							= layer_norm_epsilon
      __snake_case				:      Tuple     							= initializer_factor
      __snake_case				:      Tuple     							= feed_forward_proj
      __snake_case				:      Dict     							= use_cache

      __snake_case				:      Dict     							= self.feed_forward_proj.split('-')
      __snake_case				:      List[str]     							= act_info[-1]
      __snake_case				:      Any     							= act_info[0] == """gated"""

      if len(__a) > 1 and act_info[0] != "gated" or len(__a) > 2:
         raise ValueError(
             F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
             'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
             '\'gated-gelu\' or \'relu\'')

      if feed_forward_proj == "gated-gelu":
         __snake_case				:      Optional[int]     							= """gelu_new"""







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      return self.d_model







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      return self.num_heads







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






      return self.num_layers




class a_    (    A_							):







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      int     							= {
          """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
          """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
      }
      if self.use_past:
         __snake_case				:      Optional[int]     							= """past_encoder_sequence + sequence"""
         __snake_case				:      Optional[int]     							= {0: """batch"""}
         __snake_case				:      Tuple     							= {0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
         __snake_case				:      Any     							= {0: """batch""", 1: """decoder_sequence"""}
         __snake_case				:      List[str]     							= {0: """batch""", 1: """decoder_sequence"""}

      if self.use_past:
         self.fill_with_past_key_values_(__a					,   direction='inputs')

      return common_inputs







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      return 1_3







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






      return 5E-4







'''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])					,   ))