

"""simple docstring"""






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
							import torch
class 	_lowerCAmelCase    :

							def __init__( self       :    Any ,		a       :    Any ,		a       :    Union[str, Any]=14 ,		a       :    Optional[int]=7 ,		a       :    Dict=True ,		a       :    Any=True ,		a       :    List[Any]=False ,		a       :    Optional[Any]=True ,		a       :    Optional[int]=99 ,		a       :    List[Any]=32 ,		a       :    str=5 ,		a       :    Union[str, Any]=4 ,		a       :    Any=37 ,		a       :    List[Any]="gelu" ,		a       :    List[str]=0.1 ,		a       :    List[Any]=0.1 ,		a       :    Optional[Any]=512 ,		a       :    str=0.02 ,		)   ->    str:

									"""simple docstring"""
									lowercase										=							parent
									lowercase										=							batch_size
									lowercase										=							seq_length
									lowercase										=							is_training
									lowercase										=							use_input_mask
									lowercase										=							use_token_type_ids
									lowercase										=							use_labels
									lowercase										=							vocab_size
									lowercase										=							hidden_size
									lowercase										=							num_hidden_layers
									lowercase										=							num_attention_heads
									lowercase										=							intermediate_size
									lowercase										=							hidden_act
									lowercase										=							hidden_dropout_prob
									lowercase										=							attention_probs_dropout_prob
									lowercase										=							max_position_embeddings
									lowercase										=							initializer_range
									lowercase										=							None
									lowercase										=							vocab_size - 1
									lowercase										=							vocab_size - 1
									lowercase										=							vocab_size - 1

							def 					_lowerCAmelCase  ( self       :    Optional[int]    )   ->    str:

									"""simple docstring"""
									lowercase										=							ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size    )

									lowercase										=							None
									if self.use_input_mask:
											lowercase										=							random_attention_mask([self.batch_size, self.seq_length]    )

									lowercase										=							GPTaConfig(
									    vocab_size=self.vocab_size ,		n_embd=self.hidden_size ,		n_layer=self.num_hidden_layers ,		n_head=self.num_attention_heads ,		n_positions=self.max_position_embeddings ,		use_cache=__SCREAMING_SNAKE_CASE ,		bos_token_id=self.bos_token_id ,		eos_token_id=self.eos_token_id ,		pad_token_id=self.pad_token_id ,		)

									return (config, input_ids, input_mask)

							def 					_lowerCAmelCase  ( self       :    str    )   ->    Optional[Any]:

									"""simple docstring"""
									lowercase										=							self.prepare_config_and_inputs()
									lowercase										=							config_and_inputs
									lowercase										=							{'input_ids': input_ids, 'attention_mask': attention_mask}
									return config, inputs_dict

							def 					_lowerCAmelCase  ( self       :    Tuple    )   ->    List[str]:

									"""simple docstring"""
									lowercase										=							self.prepare_config_and_inputs()

									lowercase										=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
									lowercase										=							ids_tensor([self.batch_size, self.seq_length] ,		vocab_size=2    )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)

							def 					_lowerCAmelCase  ( self       :    Optional[int] ,		a       :    List[Any] ,		a       :    Tuple ,		a       :    int ,		a       :    int    )   ->    int:

									"""simple docstring"""
									lowercase										=							20
									lowercase										=							model_class_name(__SCREAMING_SNAKE_CASE    )

									lowercase										=							model.init_cache(input_ids.shape[0] ,		__SCREAMING_SNAKE_CASE    )
									lowercase										=							jnp.ones((input_ids.shape[0], max_decoder_length) ,		dtype='''i4'''    )

									lowercase										=							jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1    )[None, :] ,		(input_ids.shape[0], input_ids.shape[-1] - 1)    )
									lowercase										=							model(
									    input_ids[:, :-1] ,		attention_mask=__SCREAMING_SNAKE_CASE ,		past_key_values=__SCREAMING_SNAKE_CASE ,		position_ids=__SCREAMING_SNAKE_CASE ,		)

									lowercase										=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,		dtype='''i4'''    )
									lowercase										=							model(
									    input_ids[:, -1:] ,		attention_mask=__SCREAMING_SNAKE_CASE ,		past_key_values=outputs_cache.past_key_values ,		position_ids=__SCREAMING_SNAKE_CASE ,		)

									lowercase										=							model(__SCREAMING_SNAKE_CASE    )

									lowercase										=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
									self.parent.assertTrue(diff < 1E-3 ,		msg=f"""Max diff is {diff}"""    )

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Any ,		a       :    Dict ,		a       :    Dict ,		a       :    List[str]    )   ->    Union[str, Any]:

									"""simple docstring"""
									lowercase										=							20
									lowercase										=							model_class_name(__SCREAMING_SNAKE_CASE    )

									lowercase										=							jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])    )] ,		axis=-1 ,		)

									lowercase										=							model.init_cache(input_ids.shape[0] ,		__SCREAMING_SNAKE_CASE    )
									lowercase										=							jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1    )[None, :] ,		(input_ids.shape[0], input_ids.shape[-1] - 1)    )

									lowercase										=							model(
									    input_ids[:, :-1] ,		attention_mask=__SCREAMING_SNAKE_CASE ,		past_key_values=__SCREAMING_SNAKE_CASE ,		position_ids=__SCREAMING_SNAKE_CASE ,		)
									lowercase										=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,		dtype='''i4'''    )
									lowercase										=							model(
									    input_ids[:, -1:] ,		past_key_values=outputs_cache.past_key_values ,		attention_mask=__SCREAMING_SNAKE_CASE ,		position_ids=__SCREAMING_SNAKE_CASE ,		)

									lowercase										=							model(__SCREAMING_SNAKE_CASE ,		attention_mask=__SCREAMING_SNAKE_CASE    )

									lowercase										=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
									self.parent.assertTrue(diff < 1E-3 ,		msg=f"""Max diff is {diff}"""    )




@require_flax
class 	_lowerCAmelCase    (      __snake_case       ,       __snake_case       ,       unittest.TestCase       ):
							__lowerCAmelCase					:    Any     =  (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
							__lowerCAmelCase					:    Any     =  (FlaxGPTaLMHeadModel,) if is_flax_available() else ()

							def 					_lowerCAmelCase  ( self       :    int    )   ->    Tuple:

									"""simple docstring"""
									lowercase										=							FlaxGPTaModelTester(self    )

							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    str:

									"""simple docstring"""
									for model_class_name in self.all_model_classes:
											lowercase										=							self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )

							def 					_lowerCAmelCase  ( self       :    List[str]    )   ->    Tuple:

									"""simple docstring"""
									for model_class_name in self.all_model_classes:
											lowercase										=							self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    __SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )

							@slow
							def 					_lowerCAmelCase  ( self       :    List[str]    )   ->    Tuple:

									"""simple docstring"""
									lowercase										=							GPTaTokenizer.from_pretrained('''gpt2''' ,		pad_token='''</s>''' ,		padding_side='''left'''    )
									lowercase										=							tokenizer(['''Hello this is a long string''', '''Hey'''] ,		return_tensors='''np''' ,		padding=__SCREAMING_SNAKE_CASE ,		truncation=__SCREAMING_SNAKE_CASE    )

									lowercase										=							FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''    )
									lowercase										=							False
									lowercase										=							model.config.eos_token_id

									lowercase										=							jax.jit(model.generate    )

									lowercase										=							jit_generate(inputs['''input_ids'''] ,		attention_mask=inputs['''attention_mask''']    ).sequences

									lowercase										=							tokenizer.batch_decode(__SCREAMING_SNAKE_CASE ,		skip_special_tokens=__SCREAMING_SNAKE_CASE    )

									lowercase										=							[
									    'Hello this is a long string of words. I\'m going to start with the first one.\n',
									    'Hey, I\'m not sure if I\'m going to be able to do',
									]

									self.assertListEqual(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )

							@is_pt_flax_cross_test
							def 					_lowerCAmelCase  ( self       :    str    )   ->    List[Any]:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__    ):
													# prepare inputs
													lowercase										=							self._prepare_for_class(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )
													lowercase										=							{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													lowercase										=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
													lowercase										=							getattr(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )

													lowercase										=							pt_inputs['input_ids'].shape
													lowercase										=							np.random.randint(0 ,		seq_length - 1 ,		size=(batch_size,)    )
													for batch_idx, start_index in enumerate(__SCREAMING_SNAKE_CASE    ):
															lowercase										=							0
															lowercase										=							1
															lowercase										=							0
															lowercase										=							1
													lowercase										=							pt_model_class(__SCREAMING_SNAKE_CASE    ).eval()
													lowercase										=							model_class(__SCREAMING_SNAKE_CASE ,		dtype=jnp.floataa    )

													lowercase										=							convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,		__SCREAMING_SNAKE_CASE    )
													lowercase										=							fx_state

													with torch.no_grad():
															lowercase										=							pt_model(**__SCREAMING_SNAKE_CASE    ).to_tuple()

													lowercase										=							fx_model(**__SCREAMING_SNAKE_CASE    ).to_tuple()
													self.assertEqual(len(__SCREAMING_SNAKE_CASE    ) ,		len(__SCREAMING_SNAKE_CASE    ) ,		'''Output lengths differ between Flax and PyTorch'''    )
													for fx_output, pt_output in zip(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    ):
															self.assert_almost_equals(fx_output[:, -1] ,		pt_output[:, -1].numpy() ,		4E-2    )

													with tempfile.TemporaryDirectory() as tmpdirname:
															pt_model.save_pretrained(__SCREAMING_SNAKE_CASE    )
															lowercase										=							model_class.from_pretrained(__SCREAMING_SNAKE_CASE ,		from_pt=__SCREAMING_SNAKE_CASE    )

													lowercase										=							fx_model_loaded(**__SCREAMING_SNAKE_CASE    ).to_tuple()
													self.assertEqual(
													    len(__SCREAMING_SNAKE_CASE    ) ,		len(__SCREAMING_SNAKE_CASE    ) ,		'''Output lengths differ between Flax and PyTorch'''    )
													for fx_output_loaded, pt_output in zip(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    ):
															self.assert_almost_equals(fx_output_loaded[:, -1] ,		pt_output[:, -1].numpy() ,		4E-2    )

							@is_pt_flax_cross_test
							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    Dict:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__    ):
													# prepare inputs
													lowercase										=							self._prepare_for_class(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )
													lowercase										=							{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													lowercase										=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
													lowercase										=							getattr(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    )

													lowercase										=							pt_model_class(__SCREAMING_SNAKE_CASE    ).eval()
													lowercase										=							model_class(__SCREAMING_SNAKE_CASE ,		dtype=jnp.floataa    )

													lowercase										=							load_flax_weights_in_pytorch_model(__SCREAMING_SNAKE_CASE ,		fx_model.params    )
													lowercase										=							pt_inputs['input_ids'].shape
													lowercase										=							np.random.randint(0 ,		seq_length - 1 ,		size=(batch_size,)    )
													for batch_idx, start_index in enumerate(__SCREAMING_SNAKE_CASE    ):
															lowercase										=							0
															lowercase										=							1
															lowercase										=							0
															lowercase										=							1

													# make sure weights are tied in PyTorch
													pt_model.tie_weights()

													with torch.no_grad():
															lowercase										=							pt_model(**__SCREAMING_SNAKE_CASE    ).to_tuple()

													lowercase										=							fx_model(**__SCREAMING_SNAKE_CASE    ).to_tuple()
													self.assertEqual(len(__SCREAMING_SNAKE_CASE    ) ,		len(__SCREAMING_SNAKE_CASE    ) ,		'''Output lengths differ between Flax and PyTorch'''    )
													for fx_output, pt_output in zip(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    ):
															self.assert_almost_equals(fx_output[:, -1] ,		pt_output[:, -1].numpy() ,		4E-2    )

													with tempfile.TemporaryDirectory() as tmpdirname:
															fx_model.save_pretrained(__SCREAMING_SNAKE_CASE    )
															lowercase										=							pt_model_class.from_pretrained(__SCREAMING_SNAKE_CASE ,		from_flax=__SCREAMING_SNAKE_CASE    )

													with torch.no_grad():
															lowercase										=							pt_model_loaded(**__SCREAMING_SNAKE_CASE    ).to_tuple()

													self.assertEqual(
													    len(__SCREAMING_SNAKE_CASE    ) ,		len(__SCREAMING_SNAKE_CASE    ) ,		'''Output lengths differ between Flax and PyTorch'''    )
													for fx_output, pt_output in zip(__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE    ):
															self.assert_almost_equals(fx_output[:, -1] ,		pt_output[:, -1].numpy() ,		4E-2    )

							@slow
							def 					_lowerCAmelCase  ( self       :    str    )   ->    Optional[int]:

									"""simple docstring"""
									for model_class_name in self.all_model_classes:
											lowercase										=							model_class_name.from_pretrained('''gpt2''' ,		from_pt=__SCREAMING_SNAKE_CASE    )
											lowercase										=							model(np.ones((1, 1)    )    )
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE    )

'''simple docstring'''

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCAmelCase												=   logging.get_logger(__name__)

__UpperCAmelCase												=   {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= 'gpt2'
   A		= ['past_key_values']
   A		= {
       'hidden_size': 'n_embd',
       'max_position_embeddings': 'n_positions',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }






   def __init__(       self    ,__SCREAMING_SNAKE_CASE=50257    ,__SCREAMING_SNAKE_CASE=1024    ,__SCREAMING_SNAKE_CASE=768    ,__SCREAMING_SNAKE_CASE=12    ,__SCREAMING_SNAKE_CASE=12    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE="gelu_new"    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=1e-5    ,__SCREAMING_SNAKE_CASE=0.02    ,__SCREAMING_SNAKE_CASE="cls_index"    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=50256    ,__SCREAMING_SNAKE_CASE=50256    ,__SCREAMING_SNAKE_CASE=False    ,__SCREAMING_SNAKE_CASE=False    ,**__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		vocab_size
    SCREAMING_SNAKE_CASE  :  Tuple								=		n_positions
    SCREAMING_SNAKE_CASE  :  int								=		n_embd
    SCREAMING_SNAKE_CASE  :  Dict								=		n_layer
    SCREAMING_SNAKE_CASE  :  Dict								=		n_head
    SCREAMING_SNAKE_CASE  :  List[Any]								=		n_inner
    SCREAMING_SNAKE_CASE  :  Dict								=		activation_function
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		resid_pdrop
    SCREAMING_SNAKE_CASE  :  str								=		embd_pdrop
    SCREAMING_SNAKE_CASE  :  int								=		attn_pdrop
    SCREAMING_SNAKE_CASE  :  Tuple								=		layer_norm_epsilon
    SCREAMING_SNAKE_CASE  :  Tuple								=		initializer_range
    SCREAMING_SNAKE_CASE  :  Any								=		summary_type
    SCREAMING_SNAKE_CASE  :  List[Any]								=		summary_use_proj
    SCREAMING_SNAKE_CASE  :  int								=		summary_activation
    SCREAMING_SNAKE_CASE  :  int								=		summary_first_dropout
    SCREAMING_SNAKE_CASE  :  List[Any]								=		summary_proj_to_labels
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		scale_attn_weights
    SCREAMING_SNAKE_CASE  :  Any								=		use_cache
    SCREAMING_SNAKE_CASE  :  List[str]								=		scale_attn_by_inverse_layer_idx
    SCREAMING_SNAKE_CASE  :  Any								=		reorder_and_upcast_attn

    SCREAMING_SNAKE_CASE  :  Tuple								=		bos_token_id
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		eos_token_id

    super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE    ,eos_token_id=__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )



class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = "default"    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = False    ,):
    super().__init__(__SCREAMING_SNAKE_CASE    ,task=__SCREAMING_SNAKE_CASE    ,patching_specs=__SCREAMING_SNAKE_CASE    ,use_past=__SCREAMING_SNAKE_CASE      )
    if not getattr(self._config    ,'pad_token_id'    ,__SCREAMING_SNAKE_CASE      ):
     # TODO: how to do that better?
     SCREAMING_SNAKE_CASE  :  int								=		0






   @property
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}      )
    if self.use_past:
     self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE    ,direction='inputs'      )
     SCREAMING_SNAKE_CASE  :  Dict								=		{0: 'batch', 1: 'past_sequence + sequence'}
    else:
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{0: 'batch', 1: 'sequence'}

    return common_inputs






   @property
   def 					__a			(       self      ):
    return self._config.n_layer






   @property
   def 					__a			(       self      ):
    return self._config.n_head






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = -1    ,__SCREAMING_SNAKE_CASE = -1    ,__SCREAMING_SNAKE_CASE = False    ,__SCREAMING_SNAKE_CASE = None    ,):
    SCREAMING_SNAKE_CASE  :  List[str]								=		super(__SCREAMING_SNAKE_CASE    ,self      ).generate_dummy_inputs(
        __SCREAMING_SNAKE_CASE    ,batch_size=__SCREAMING_SNAKE_CASE    ,seq_length=__SCREAMING_SNAKE_CASE    ,is_pair=__SCREAMING_SNAKE_CASE    ,framework=__SCREAMING_SNAKE_CASE      )

    # We need to order the input in the way they appears in the forward()
    SCREAMING_SNAKE_CASE  :  int								=		OrderedDict({'input_ids': common_inputs['input_ids']}      )

    # Need to add the past_keys
    if self.use_past:
     if not is_torch_available():
      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
     else:
      import torch

      SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[str]								=		common_inputs['input_ids'].shape
      # Not using the same length for past_key_values
      SCREAMING_SNAKE_CASE  :  str								=		seqlen + 2
      SCREAMING_SNAKE_CASE  :  Dict								=		(
          batch,
          self.num_attention_heads,
          past_key_values_length,
          self._config.hidden_size // self.num_attention_heads,
      )
      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[
          (torch.zeros(__SCREAMING_SNAKE_CASE      ), torch.zeros(__SCREAMING_SNAKE_CASE      )) for _ in range(self.num_layers      )
      ]

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		common_inputs['attention_mask']
    if self.use_past:
     SCREAMING_SNAKE_CASE  :  Tuple								=		ordered_inputs['attention_mask'].dtype
     SCREAMING_SNAKE_CASE  :  Any								=		torch.cat(
         [ordered_inputs['attention_mask'], torch.ones(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,dtype=__SCREAMING_SNAKE_CASE      )]    ,dim=1      )

    return ordered_inputs






   @property
   def 					__a			(       self      ):
    return 13


