


from typing import Mapping

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


__lowerCAmelCase									=				logging.get_logger(__name__)

__lowerCAmelCase									=				{
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}





class     __a     (       __UpperCamelCase ):
 __lowercase					:				Any  	=					'longt5'
 __lowercase					:				str  	=					['past_key_values']
 __lowercase					:				int  	=					{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
 def __init__(  self							,					lowerCAmelCase__=32_128							,					lowerCAmelCase__=512							,					lowerCAmelCase__=64							,					lowerCAmelCase__=2_048							,					lowerCAmelCase__=6							,					lowerCAmelCase__=None							,					lowerCAmelCase__=8							,					lowerCAmelCase__=127							,					lowerCAmelCase__=16							,					lowerCAmelCase__=32							,					lowerCAmelCase__=128							,					lowerCAmelCase__=0.1							,					lowerCAmelCase__=1E-6							,					lowerCAmelCase__=1.0							,					lowerCAmelCase__="relu"							,					lowerCAmelCase__=True							,					lowerCAmelCase__="local"							,					lowerCAmelCase__=True							,					lowerCAmelCase__=0							,					lowerCAmelCase__=1							,					**lowerCAmelCase__							,					)   ->							Union[str, Any]:




        '''simple docstring'''
        lowercase__:  Tuple         =  vocab_size
        lowercase__:  str         =  d_model
        lowercase__:  Any         =  d_kv
        lowercase__:  List[str]         =  d_ff
        lowercase__:  List[Any]         =  num_layers
        # default = symmetry
        lowercase__:  Optional[int]         =  num_decoder_layers if num_decoder_layers is not None else self.num_layers
        lowercase__:  Optional[int]         =  num_heads
        lowercase__:  Dict         =  local_radius
        lowercase__:  Any         =  global_block_size
        lowercase__:  Dict         =  relative_attention_num_buckets
        lowercase__:  int         =  relative_attention_max_distance
        lowercase__:  int         =  dropout_rate
        lowercase__:  Union[str, Any]         =  layer_norm_epsilon
        lowercase__:  str         =  initializer_factor
        lowercase__:  int         =  feed_forward_proj
        lowercase__:  Optional[Any]         =  encoder_attention_type
        lowercase__:  int         =  use_cache

        lowercase__:  Optional[int]         =  self.feed_forward_proj.split('-'					)
        lowercase__:  Optional[Any]         =  act_info[-1]
        lowercase__:  Tuple         =  act_info[0] == 'gated'

        if len(__lowercase					) > 1 and act_info[0] != "gated" or len(__lowercase					) > 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":
               lowercase__:  Union[str, Any]         =  'gelu_new'

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





class     __a     (       __UpperCamelCase ):
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
               lowercase__:  List[Any]         =  'past_encoder_sequence + sequence'
               lowercase__:  Dict         =  {0: 'batch'}
               lowercase__:  str         =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
               lowercase__:  int         =  {0: 'batch', 1: 'decoder_sequence'}
               lowercase__:  str         =  {0: 'batch', 1: 'decoder_sequence'}

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

        return common_inputs
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        return 13










"""simple docstring"""



import os
import unittest

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


if is_torch_available():
 import torch

 from transformers import GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class 	_UpperCamelCase ( unittest.TestCase							):



    '''simple docstring'''


    @slow
    @unittest.skip("""Model is not available."""			)
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				"""nvidia/megatron-gpt2-345m/"""
     if "MYDIR" in os.environ:
      UpperCAmelCase__ 						=				os.path.join(os.environ["""MYDIR"""]   ,			__lowercase			)
     UpperCAmelCase__ 						=				GPTaLMHeadModel.from_pretrained(__lowercase			)
     model.to(__lowercase			)
     model.half()

     UpperCAmelCase__ 						=				torch.tensor(
         [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]   ,			device=__lowercase   ,			dtype=torch.long   ,			)

     with torch.no_grad():
      UpperCAmelCase__ 						=				model(__lowercase			).logits

     UpperCAmelCase__ 						=				torch.Size((1, 9, 50257)			)
     self.assertEqual(output.shape   ,			__lowercase			)

     UpperCAmelCase__ 						=				torch.tensor(
         [
             4.9_414,
             -0.2_920,
             -1.2_148,
             -4.0_273,
             -0.5_161,
             -5.2_109,
             -1.2_412,
             -1.8_301,
             -1.7_734,
             -4.7_148,
             -0.2_317,
             -1.0_811,
             -2.1_777,
             0.4_141,
             -3.7_969,
             -4.0_586,
             -2.5_332,
             -3.3_809,
             4.3_867,
         ]   ,			device=__lowercase   ,			dtype=torch.half   ,			)

     for i in range(19			):
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				8 * i // 17, 2792 * i  # along the diagonal
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				output[0, r, c], expected_diag[i]
      UpperCAmelCase__ 						=				F'''row={r} col={c} computed={computed} expected={expected}'''
      self.assertAlmostEqual(__lowercase   ,			__lowercase   ,			delta=1e-4   ,			msg=__lowercase			)



