



"""simple docstring"""





from typing import Mapping

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


lowerCamelCase       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''t5'''
       UpperCamelCase						=					['''past_key_values''']
       UpperCamelCase						=					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



       def __init__(							self      :  Optional[Any]		,    _UpperCAmelCase      :  Optional[Any]=32128		,    _UpperCAmelCase      :  Dict=512		,    _UpperCAmelCase      :  int=64		,    _UpperCAmelCase      :  str=2048		,    _UpperCAmelCase      :  Union[str, Any]=6		,    _UpperCAmelCase      :  Tuple=None		,    _UpperCAmelCase      :  str=8		,    _UpperCAmelCase      :  Union[str, Any]=32		,    _UpperCAmelCase      :  Optional[Any]=128		,    _UpperCAmelCase      :  int=0.1		,    _UpperCAmelCase      :  Union[str, Any]=1e-6		,    _UpperCAmelCase      :  str=1.0		,    _UpperCAmelCase      :  List[str]="relu"		,    _UpperCAmelCase      :  List[Any]=True		,    _UpperCAmelCase      :  Optional[int]=True		,    _UpperCAmelCase      :  str=0		,    _UpperCAmelCase      :  Dict=1		,    **_UpperCAmelCase      :  Dict		,    )			->						Union[str, Any]:


            '''simple docstring'''




            UpperCAmelCase_      			=							vocab_size
            UpperCAmelCase_      			=							d_model
            UpperCAmelCase_      			=							d_kv
            UpperCAmelCase_      			=							d_ff
            UpperCAmelCase_      			=							num_layers
            UpperCAmelCase_      			=							(
                num_decoder_layers if num_decoder_layers is not None else self.num_layers
            )  # default = symmetry
            UpperCAmelCase_      			=							num_heads
            UpperCAmelCase_      			=							relative_attention_num_buckets
            UpperCAmelCase_      			=							relative_attention_max_distance
            UpperCAmelCase_      			=							dropout_rate
            UpperCAmelCase_      			=							layer_norm_epsilon
            UpperCAmelCase_      			=							initializer_factor
            UpperCAmelCase_      			=							feed_forward_proj
            UpperCAmelCase_      			=							use_cache

            UpperCAmelCase_      			=							self.feed_forward_proj.split("-"   )
            UpperCAmelCase_      			=							act_info[-1]
            UpperCAmelCase_      			=							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'"   )

            # for backwards compatibility
            if feed_forward_proj == "gated-gelu":
                 UpperCAmelCase_      			=							"gelu_new"

            super().__init__(
                pad_token_id=_a		,    eos_token_id=_a		,    is_encoder_decoder=_a		,    **_a		,    )



class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  List[str]   )			->						List[str]:


            '''simple docstring'''




            UpperCAmelCase_      			=							{
                "input_ids": {0: "batch", 1: "encoder_sequence"},
                "attention_mask": {0: "batch", 1: "encoder_sequence"},
            }
            if self.use_past:
                 UpperCAmelCase_      			=							"past_encoder_sequence + sequence"
                 UpperCAmelCase_      			=							{0: "batch"}
                 UpperCAmelCase_      			=							{0: "batch", 1: "past_decoder_sequence + sequence"}
            else:
                 UpperCAmelCase_      			=							{0: "batch", 1: "decoder_sequence"}
                 UpperCAmelCase_      			=							{0: "batch", 1: "decoder_sequence"}

            if self.use_past:
                 self.fill_with_past_key_values_(_a		,    direction="inputs"   )

            return common_inputs







       @property
       def 					lowercase__    (							self      :  Tuple   )			->						Tuple:


            '''simple docstring'''




            return 13









"""simple docstring"""





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
  import torch

  from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @slow
       def 					lowercase__    (							self      :  Tuple   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							torch.tensor(
                 [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
             #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
             #  xlmr.eval()
             #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
             with torch.no_grad():
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )







       @slow
       def 					lowercase__    (							self      :  List[Any]   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 1024)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							torch.tensor(
                 [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
             #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
             #  xlmr.eval()
             #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
             with torch.no_grad():
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )




