"""simple docstring"""






import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__  :  Tuple											=     logging.get_logger(__name__)



def   a_						( lowerCamelCase	):
				UpperCAmelCase__					=							tf.image.resize(lowerCamelCase ,      (3_4_6, 3_4_6)	)
				UpperCAmelCase__					=							tf.image.crop_to_bounding_box(lowerCamelCase ,      (3_4_6 - 2_8_9) // 2 ,      (3_4_6 - 2_8_9) // 2 ,      2_8_9 ,      2_8_9	)
				return image



def   a_						( ):
				UpperCAmelCase__					=							EfficientNetConfig.from_pretrained('google/efficientnet-b7'	)
				UpperCAmelCase__					=							2_8_9
				UpperCAmelCase__					=							6_4_0
				UpperCAmelCase__					=							{'0': 'LABEL_0', '1': 'LABEL_1'}
				UpperCAmelCase__					=							{'LABEL_0': 0, 'LABEL_1': 1}
				UpperCAmelCase__					=							[]

				UpperCAmelCase__					=							BertConfig()
				UpperCAmelCase__					=							AlignConfig.from_text_vision_configs(
				    text_config=lowerCamelCase ,      vision_config=lowerCamelCase ,      projection_dim=6_4_0	)
				return config



def   a_						( ):
				UpperCAmelCase__					=							'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__					=							Image.open(requests.get(lowerCamelCase ,      stream=lowerCamelCase	).raw	)
				return im



def   a_						( ):
				UpperCAmelCase__					=							EfficientNetImageProcessor(
				    do_center_crop=lowerCamelCase ,      rescale_factor=1 / 127.5 ,      rescale_offset=lowerCamelCase ,      do_normalize=lowerCamelCase ,      include_top=lowerCamelCase ,      resample=Image.BILINEAR ,      )
				UpperCAmelCase__					=							BertTokenizer.from_pretrained('bert-base-uncased'	)
				UpperCAmelCase__					=							6_4
				UpperCAmelCase__					=							AlignProcessor(image_processor=lowerCamelCase ,      tokenizer=lowerCamelCase	)
				return processor



def   a_						( lowerCamelCase	):
				# EfficientNet image encoder
				UpperCAmelCase__					=							[v.split('_'	)[0].split('block'	)[1] for v in original_param_names if v.startswith('block'	)]
				UpperCAmelCase__					=							list(set(lowerCamelCase	)	)
				UpperCAmelCase__					=							sorted(lowerCamelCase	)
				UpperCAmelCase__					=							len(lowerCamelCase	)
				UpperCAmelCase__					=							{b: str(lowerCamelCase	) for b, i in zip(lowerCamelCase ,      range(lowerCamelCase	)	)}

				UpperCAmelCase__					=							[]
				rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')	)
				rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')	)
				rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')	)
				rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')	)
				rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')	)

				for b in block_names:
								UpperCAmelCase__					=							block_name_mapping[b]
								rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')	)
								rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')	)
								rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')	)
								rename_keys.append(
								    (f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')	)
								rename_keys.append(
								    (f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')	)
								rename_keys.append(
								    (f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')	)
								rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')	)
								rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')	)
								rename_keys.append(
								    (f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')	)
								rename_keys.append(
								    (f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')	)

								rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')	)
								rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')	)
								rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')	)
								rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')	)
								rename_keys.append(
								    (f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''')	)
								rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''')	)
								rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''')	)
								rename_keys.append(
								    (f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')	)
								rename_keys.append(
								    (f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')	)

				UpperCAmelCase__					=							{}
				for item in rename_keys:
								if item[0] in original_param_names:
												UpperCAmelCase__					=							'vision_model.' + item[1]

    # BERT text encoder
				UpperCAmelCase__					=							[]
				UpperCAmelCase__					=							'tf_bert_model/bert'
				UpperCAmelCase__					=							'text_model'
				for i in range(1_2	):
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/query/kernel:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.query.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/query/bias:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.query.bias''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/key/kernel:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.key.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/key/bias:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.key.bias''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/value/kernel:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.value.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/value/bias:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.value.bias''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/output/dense/kernel:0''',
								        f'''{new}.encoder.layer.{i}.attention.output.dense.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/output/dense/bias:0''',
								        f'''{new}.encoder.layer.{i}.attention.output.dense.bias''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0''',
								        f'''{new}.encoder.layer.{i}.attention.output.LayerNorm.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0''',
								        f'''{new}.encoder.layer.{i}.attention.output.LayerNorm.bias''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/intermediate/dense/kernel:0''',
								        f'''{new}.encoder.layer.{i}.intermediate.dense.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/intermediate/dense/bias:0''',
								        f'''{new}.encoder.layer.{i}.intermediate.dense.bias''',
								    )	)
								rename_keys.append(
								    (f'''{old}/encoder/layer_._{i}/output/dense/kernel:0''', f'''{new}.encoder.layer.{i}.output.dense.weight''')	)
								rename_keys.append(
								    (f'''{old}/encoder/layer_._{i}/output/dense/bias:0''', f'''{new}.encoder.layer.{i}.output.dense.bias''')	)
								rename_keys.append(
								    (f'''{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0''', f'''{new}.encoder.layer.{i}.output.LayerNorm.weight''')	)
								rename_keys.append(
								    (f'''{old}/encoder/layer_._{i}/output/LayerNorm/beta:0''', f'''{new}.encoder.layer.{i}.output.LayerNorm.bias''')	)

				rename_keys.append((f'''{old}/embeddings/word_embeddings/weight:0''', f'''{new}.embeddings.word_embeddings.weight''')	)
				rename_keys.append(
				    (f'''{old}/embeddings/position_embeddings/embeddings:0''', f'''{new}.embeddings.position_embeddings.weight''')	)
				rename_keys.append(
				    (f'''{old}/embeddings/token_type_embeddings/embeddings:0''', f'''{new}.embeddings.token_type_embeddings.weight''')	)
				rename_keys.append((f'''{old}/embeddings/LayerNorm/gamma:0''', f'''{new}.embeddings.LayerNorm.weight''')	)
				rename_keys.append((f'''{old}/embeddings/LayerNorm/beta:0''', f'''{new}.embeddings.LayerNorm.bias''')	)

				rename_keys.append((f'''{old}/pooler/dense/kernel:0''', f'''{new}.pooler.dense.weight''')	)
				rename_keys.append((f'''{old}/pooler/dense/bias:0''', f'''{new}.pooler.dense.bias''')	)
				rename_keys.append(('dense/kernel:0', 'text_projection.weight')	)
				rename_keys.append(('dense/bias:0', 'text_projection.bias')	)
				rename_keys.append(('dense/bias:0', 'text_projection.bias')	)
				rename_keys.append(('temperature:0', 'temperature')	)

				for item in rename_keys:
								if item[0] in original_param_names:
												UpperCAmelCase__					=							item[1]
				return key_mapping



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				list(hf_params.keys()	)

				for key, value in tf_params.items():
								if key not in key_mapping:
												continue

								UpperCAmelCase__					=							key_mapping[key]
								if "_conv" in key and "kernel" in key:
												UpperCAmelCase__					=							torch.from_numpy(lowerCamelCase	).permute(3 ,      2 ,      0 ,      1	)
								elif "embeddings" in key:
												UpperCAmelCase__					=							torch.from_numpy(lowerCamelCase	)
								elif "depthwise_kernel" in key:
												UpperCAmelCase__					=							torch.from_numpy(lowerCamelCase	).permute(2 ,      3 ,      0 ,      1	)
								elif "kernel" in key:
												UpperCAmelCase__					=							torch.from_numpy(np.transpose(lowerCamelCase	)	)
								elif "temperature" in key:
												UpperCAmelCase__					=							value
								elif "bn/gamma" or "bn/beta" in key:
												UpperCAmelCase__					=							torch.from_numpy(np.transpose(lowerCamelCase	)	).squeeze()
								else:
												UpperCAmelCase__					=							torch.from_numpy(lowerCamelCase	)

								# Replace HF parameters with original TF model parameters
								hf_params[hf_key].copy_(lowerCamelCase	)



@torch.no_grad()
def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							6_4
				UpperCAmelCase__					=							Tokenizer(lowerCamelCase	)
				UpperCAmelCase__					=							align.Align('efficientnet-b7' ,      'bert-base' ,      6_4_0 ,      lowerCamelCase ,      tok.get_vocab_size()	)
				original_model.compile()
				original_model.load_weights(lowerCamelCase	)

				UpperCAmelCase__					=							original_model.trainable_variables
				UpperCAmelCase__					=							original_model.non_trainable_variables
				UpperCAmelCase__					=							{param.name: param.numpy() for param in tf_params}
				for param in tf_non_train_params:
								UpperCAmelCase__					=							param.numpy()
				UpperCAmelCase__					=							list(tf_params.keys()	)

				# Load HuggingFace model
				UpperCAmelCase__					=							get_align_config()
				UpperCAmelCase__					=							AlignModel(lowerCamelCase	).eval()
				UpperCAmelCase__					=							hf_model.state_dict()

				# Create src-to-dst parameter name mapping dictionary
				print('Converting parameters...'	)
				UpperCAmelCase__					=							rename_keys(lowerCamelCase	)
				replace_params(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	)

				# Initialize processor
				UpperCAmelCase__					=							get_processor()
				UpperCAmelCase__					=							processor(
				    images=prepare_img() ,      text='A picture of a cat' ,      padding='max_length' ,      max_length=6_4 ,      return_tensors='pt'	)

				# HF model inference
				hf_model.eval()
				with torch.no_grad():
								UpperCAmelCase__					=							hf_model(**lowerCamelCase	)

				UpperCAmelCase__					=							outputs.image_embeds.detach().numpy()
				UpperCAmelCase__					=							outputs.text_embeds.detach().numpy()

				# Original model inference
				UpperCAmelCase__					=							False
				UpperCAmelCase__					=							EfficientNetImageProcessor(
				    do_center_crop=lowerCamelCase ,      do_rescale=lowerCamelCase ,      do_normalize=lowerCamelCase ,      include_top=lowerCamelCase ,      resample=Image.BILINEAR ,      )
				UpperCAmelCase__					=							tf_image_processor(images=prepare_img() ,      return_tensors='tf' ,      data_format='channels_last'	)['pixel_values']
				UpperCAmelCase__					=							tok(tf.constant(['A picture of a cat']	)	)

				UpperCAmelCase__					=							original_model.image_encoder(lowerCamelCase ,      training=lowerCamelCase	)
				UpperCAmelCase__					=							original_model.text_encoder(lowerCamelCase ,      training=lowerCamelCase	)

				UpperCAmelCase__					=							tf.nn.la_normalize(lowerCamelCase ,      axis=-1	)
				UpperCAmelCase__					=							tf.nn.la_normalize(lowerCamelCase ,      axis=-1	)

				# Check whether original and HF model outputs match  -> np.allclose
				if not np.allclose(lowerCamelCase ,      lowerCamelCase ,      atol=1e-3	):
								raise ValueError('The predicted image features are not the same.'	)
				if not np.allclose(lowerCamelCase ,      lowerCamelCase ,      atol=1e-3	):
								raise ValueError('The predicted text features are not the same.'	)
				print('Model outputs match!'	)

				if save_model:
								# Create folder to save model
								if not os.path.isdir(lowerCamelCase	):
												os.mkdir(lowerCamelCase	)
								# Save converted model and image processor
								hf_model.save_pretrained(lowerCamelCase	)
								processor.save_pretrained(lowerCamelCase	)

				if push_to_hub:
								# Push model and image processor to hub
								print('Pushing converted ALIGN to the hub...'	)
								processor.push_to_hub('align-base'	)
								hf_model.push_to_hub('align-base'	)


if __name__ == "__main__":
						lowerCAmelCase__  :  List[Any]											=     argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_path',
						    default='./weights/model-weights',
						    type=str,
						    help='Path to the pretrained TF ALIGN checkpoint.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default='hf_model',
						    type=str,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument('--save_model', action='store_true', help='Save model to local')
						parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

						lowerCAmelCase__  :  Dict											=     parser.parse_args()
						convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


"""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


lowerCAmelCase__  :  Any											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    '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     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

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







					def __init__(    self       :     Any   ,lowerCamelCase__       :     List[Any]=50_257   ,lowerCamelCase__       :     Union[str, Any]=1_024   ,lowerCamelCase__       :     Dict=768   ,lowerCamelCase__       :     List[str]=12   ,lowerCamelCase__       :     Dict=12   ,lowerCamelCase__       :     List[str]=None   ,lowerCamelCase__       :     List[str]="gelu_new"   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Dict=0.1   ,lowerCamelCase__       :     str=0.1   ,lowerCamelCase__       :     Union[str, Any]=1e-5   ,lowerCamelCase__       :     Optional[Any]=0.0_2   ,lowerCamelCase__       :     str="cls_index"   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     List[Any]=None   ,lowerCamelCase__       :     List[str]=True   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Tuple=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]=50_256   ,lowerCamelCase__       :     Union[str, Any]=50_256   ,lowerCamelCase__       :     Union[str, Any]=False   ,lowerCamelCase__       :     List[Any]=False   ,**lowerCamelCase__       :     Tuple   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							n_positions
									UpperCAmelCase__					=							n_embd
									UpperCAmelCase__					=							n_layer
									UpperCAmelCase__					=							n_head
									UpperCAmelCase__					=							n_inner
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							resid_pdrop
									UpperCAmelCase__					=							embd_pdrop
									UpperCAmelCase__					=							attn_pdrop
									UpperCAmelCase__					=							layer_norm_epsilon
									UpperCAmelCase__					=							initializer_range
									UpperCAmelCase__					=							summary_type
									UpperCAmelCase__					=							summary_use_proj
									UpperCAmelCase__					=							summary_activation
									UpperCAmelCase__					=							summary_first_dropout
									UpperCAmelCase__					=							summary_proj_to_labels
									UpperCAmelCase__					=							scale_attn_weights
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							scale_attn_by_inverse_layer_idx
									UpperCAmelCase__					=							reorder_and_upcast_attn

									UpperCAmelCase__					=							bos_token_id
									UpperCAmelCase__					=							eos_token_id

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






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					def __init__(    self       :     List[str]   ,lowerCamelCase__       :     PretrainedConfig   ,lowerCamelCase__       :     str = "default"   ,lowerCamelCase__       :     List[PatchingSpec] = None   ,lowerCamelCase__       :     bool = False   ,):
									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?
													UpperCAmelCase__					=							0







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}      )
									if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase__   ,direction='inputs'      )
													UpperCAmelCase__					=							{0: 'batch', 1: 'past_sequence + sequence'}
									else:
													UpperCAmelCase__					=							{0: 'batch', 1: 'sequence'}

									return common_inputs







					@property
					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									return self._config.n_layer







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return self._config.n_head







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     PreTrainedTokenizer   ,lowerCamelCase__       :     int = -1   ,lowerCamelCase__       :     int = -1   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     Optional[TensorType] = None   ,):
									UpperCAmelCase__					=							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()
									UpperCAmelCase__					=							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

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

									UpperCAmelCase__					=							common_inputs['attention_mask']
									if self.use_past:
													UpperCAmelCase__					=							ordered_inputs['attention_mask'].dtype
													UpperCAmelCase__					=							torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__   ,lowerCamelCase__   ,dtype=lowerCamelCase__      )]   ,dim=1      )

									return ordered_inputs







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 13



