
import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, 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
				import numpy as np

				from transformers.modeling_flax_pytorch_utils import (
				    convert_pytorch_state_dict_to_flax,
				    load_flax_weights_in_pytorch_model,
				)
				from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
				import torch






@require_flax
class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=14  ,						SCREAMING_SNAKE_CASE_=7  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=99  ,						SCREAMING_SNAKE_CASE_=32  ,						SCREAMING_SNAKE_CASE_=5  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=37  ,						SCREAMING_SNAKE_CASE_="gelu"  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=512  ,						SCREAMING_SNAKE_CASE_=0.0_2  ,						SCREAMING_SNAKE_CASE_=None  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      seq_length
													__UpperCamelCase  	=      is_training
													__UpperCamelCase  	=      use_input_mask
													__UpperCamelCase  	=      use_labels
													__UpperCamelCase  	=      vocab_size
													__UpperCamelCase  	=      d_model
													__UpperCamelCase  	=      num_hidden_layers
													__UpperCamelCase  	=      num_attention_heads
													__UpperCamelCase  	=      ffn_dim
													__UpperCamelCase  	=      activation_function
													__UpperCamelCase  	=      activation_dropout
													__UpperCamelCase  	=      attention_dropout
													__UpperCamelCase  	=      max_position_embeddings
													__UpperCamelCase  	=      initializer_range
													__UpperCamelCase  	=      None
													__UpperCamelCase  	=      0
													__UpperCamelCase  	=      2
													__UpperCamelCase  	=      1



							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      np.clip(ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size      )  ,						3  ,						self.vocab_size      )

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

													__UpperCamelCase  	=      XGLMConfig(
													    vocab_size=self.vocab_size  ,						d_model=self.hidden_size  ,						num_layers=self.num_hidden_layers  ,						attention_heads=self.num_attention_heads  ,						ffn_dim=self.ffn_dim  ,						activation_function=self.activation_function  ,						activation_dropout=self.activation_dropout  ,						attention_dropout=self.attention_dropout  ,						max_position_embeddings=self.max_position_embeddings  ,						initializer_range=self.initializer_range  ,						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      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.prepare_config_and_inputs()
													__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      config_and_inputs
													__UpperCamelCase  	=      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
													return config, inputs_dict



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      self.prepare_config_and_inputs()

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

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      20
													__UpperCamelCase  	=      model_class_name(SCREAMING_SNAKE_CASE_      )

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

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

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

													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      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      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      20
													__UpperCamelCase  	=      model_class_name(SCREAMING_SNAKE_CASE_      )

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

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

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

													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      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_sentencepiece
@require_flax
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
							_snake_case  		=	(FlaxXGLMForCausalLM,) if is_flax_available() else ()



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      FlaxXGLMModelTester(self      )



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      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      A__		(	self      )-> List[str]:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      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      A__		(	self      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      XGLMTokenizer.from_pretrained('''XGLM'''  ,						padding_side='''left'''      )
													__UpperCamelCase  	=      tokenizer(['''Hello this is a long string''', '''Hey''']  ,						return_tensors='''np'''  ,						padding=SCREAMING_SNAKE_CASE_  ,						truncation=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''      )
													__UpperCamelCase  	=      1
													__UpperCamelCase  	=      False

													__UpperCamelCase  	=      jax.jit(model.generate      )

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

													__UpperCamelCase  	=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_  ,						skip_special_tokens=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      [
													    '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
													    '''Hey, I\'m a newbie to the forum and I\'''',
													]

													self.assertListEqual(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



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





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      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
																									__UpperCamelCase  	=      self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																									__UpperCamelCase  	=      {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

																									__UpperCamelCase	,				__UpperCamelCase  	=      pt_inputs['''input_ids'''].shape
																									__UpperCamelCase  	=      np.random.randint(0  ,						seq_length - 1  ,						size=(batch_size,)      )
																									for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_      ):
																															__UpperCamelCase  	=      0
																															__UpperCamelCase  	=      1
																															__UpperCamelCase  	=      0
																															__UpperCamelCase  	=      1
																									__UpperCamelCase  	=      pt_model_class(SCREAMING_SNAKE_CASE_      ).eval()
																									# Flax models don't use the `use_cache` option and cache is not returned as a default.
																									# So we disable `use_cache` here for PyTorch model.
																									__UpperCamelCase  	=      False
																									__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_  ,						dtype=jnp.floataa      )
																									__UpperCamelCase  	=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,						SCREAMING_SNAKE_CASE_      )
																									__UpperCamelCase  	=      fx_state

																									with torch.no_grad():
																															__UpperCamelCase  	=      pt_model(**SCREAMING_SNAKE_CASE_      ).to_tuple()

																									__UpperCamelCase  	=      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_      )
																															__UpperCamelCase  	=      model_class.from_pretrained(SCREAMING_SNAKE_CASE_  ,						from_pt=SCREAMING_SNAKE_CASE_      )

																									__UpperCamelCase  	=      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      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      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
																									__UpperCamelCase  	=      self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																									__UpperCamelCase  	=      {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

																									__UpperCamelCase  	=      pt_model_class(SCREAMING_SNAKE_CASE_      ).eval()
																									__UpperCamelCase  	=      False
																									__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_  ,						dtype=jnp.floataa      )

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

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

																									with torch.no_grad():
																															__UpperCamelCase  	=      pt_model(**SCREAMING_SNAKE_CASE_      ).to_tuple()

																									__UpperCamelCase  	=      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_      )
																															__UpperCamelCase  	=      pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_  ,						from_flax=SCREAMING_SNAKE_CASE_      )

																									with torch.no_grad():
																															__UpperCamelCase  	=      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      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase  	=      model_class_name.from_pretrained('''facebook/xglm-564M'''      )
																			__UpperCamelCase  	=      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


lowercase											=      logging.get_logger(__name__)

lowercase											=      {
    '''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 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			str	=   "gpt2"
 __magic_name__       :			str	=   ["past_key_values"]
 __magic_name__       :			Dict	=   {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }




 def __init__( self				,	lowerCAmelCase_=5_0_2_5_7				,	lowerCAmelCase_=1_0_2_4				,	lowerCAmelCase_=7_6_8				,	lowerCAmelCase_=1_2				,	lowerCAmelCase_=1_2				,	lowerCAmelCase_=None				,	lowerCAmelCase_="gelu_new"				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=1e-5				,	lowerCAmelCase_=0.0_2				,	lowerCAmelCase_="cls_index"				,	lowerCAmelCase_=True				,	lowerCAmelCase_=None				,	lowerCAmelCase_=True				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=True				,	lowerCAmelCase_=True				,	lowerCAmelCase_=5_0_2_5_6				,	lowerCAmelCase_=5_0_2_5_6				,	lowerCAmelCase_=False				,	lowerCAmelCase_=False				,	**lowerCAmelCase_				,	)			->   Dict:



      """simple docstring"""







      a_									=vocab_size
      a_									=n_positions
      a_									=n_embd
      a_									=n_layer
      a_									=n_head
      a_									=n_inner
      a_									=activation_function
      a_									=resid_pdrop
      a_									=embd_pdrop
      a_									=attn_pdrop
      a_									=layer_norm_epsilon
      a_									=initializer_range
      a_									=summary_type
      a_									=summary_use_proj
      a_									=summary_activation
      a_									=summary_first_dropout
      a_									=summary_proj_to_labels
      a_									=scale_attn_weights
      a_									=use_cache
      a_									=scale_attn_by_inverse_layer_idx
      a_									=reorder_and_upcast_attn

      a_									=bos_token_id
      a_									=eos_token_id

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







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 def __init__( self				,	lowerCAmelCase_				,	lowerCAmelCase_ = "default"				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = False				,	)			->   List[str]:



      """simple docstring"""







      super().__init__(lowerCAmelCase_				,	task=lowerCAmelCase_				,	patching_specs=lowerCAmelCase_				,	use_past=lowerCAmelCase_)
      if not getattr(self._config				,	"pad_token_id"				,	lowerCAmelCase_):
           # TODO: how to do that better?
           a_									=0




 @property
 def    lowercase_	( self)			->   Mapping[str, Mapping[int, str]]:



      """simple docstring"""







      a_									=OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
      if self.use_past:
           self.fill_with_past_key_values_(lowerCAmelCase_				,	direction="inputs")
           a_									={0: "batch", 1: "past_sequence + sequence"}
      else:
           a_									={0: "batch", 1: "sequence"}

      return common_inputs




 @property
 def    lowercase_	( self)			->   int:



      """simple docstring"""







      return self._config.n_layer




 @property
 def    lowercase_	( self)			->   int:



      """simple docstring"""







      return self._config.n_head




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_ = -1				,	lowerCAmelCase_ = -1				,	lowerCAmelCase_ = False				,	lowerCAmelCase_ = None				,	)			->   Mapping[str, Any]:



      """simple docstring"""







      a_									=super(lowerCAmelCase_				,	self).generate_dummy_inputs(
          lowerCAmelCase_				,	batch_size=lowerCAmelCase_				,	seq_length=lowerCAmelCase_				,	is_pair=lowerCAmelCase_				,	framework=lowerCAmelCase_)

      # We need to order the input in the way they appears in the forward()
      a_									=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

                a_		,    a_									=common_inputs["input_ids"].shape
                # Not using the same length for past_key_values
                a_									=seqlen + 2
                a_									=(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                a_									=[
                    (torch.zeros(lowerCAmelCase_), torch.zeros(lowerCAmelCase_)) for _ in range(self.num_layers)
                ]

      a_									=common_inputs["attention_mask"]
      if self.use_past:
           a_									=ordered_inputs["attention_mask"].dtype
           a_									=torch.cat(
               [ordered_inputs["attention_mask"], torch.ones(lowerCAmelCase_				,	lowerCAmelCase_				,	dtype=lowerCAmelCase_)]				,	dim=1)

      return ordered_inputs




 @property
 def    lowercase_	( self)			->   int:



      """simple docstring"""







      return 1_3


