'''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()
snake_case__	:     Any      =    logging.get_logger(__name__)


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[Any]  = tf.image.resize(lowerCamelCase_      ,	(346, 346)  )
     UpperCAmelCase_				:  Dict  = tf.image.crop_to_bounding_box(lowerCamelCase_      ,	(346 - 289) // 2      ,	(346 - 289) // 2      ,	289      ,	289  )
     return image


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Union[str, Any]  = EfficientNetConfig.from_pretrained('google/efficientnet-b7'  )
     UpperCAmelCase_				:  List[Any]  = 289
     UpperCAmelCase_				:  Union[str, Any]  = 640
     UpperCAmelCase_				:  Tuple  = {'0': 'LABEL_0', '1': 'LABEL_1'}
     UpperCAmelCase_				:  List[Any]  = {'LABEL_0': 0, 'LABEL_1': 1}
     UpperCAmelCase_				:  List[Any]  = []

     UpperCAmelCase_				:  Optional[Any]  = BertConfig()
     UpperCAmelCase_				:  Optional[int]  = AlignConfig.from_text_vision_configs(
         text_config=lowerCamelCase_      ,	vision_config=lowerCamelCase_      ,	projection_dim=640  )
     return config


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Union[str, Any]  = 'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCAmelCase_				:  Optional[Any]  = Image.open(requests.get(lowerCamelCase_      ,	stream=lowerCamelCase_  ).raw  )
     return im


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Dict  = EfficientNetImageProcessor(
         do_center_crop=lowerCamelCase_      ,	rescale_factor=1 / 127.5      ,	rescale_offset=lowerCamelCase_      ,	do_normalize=lowerCamelCase_      ,	include_top=lowerCamelCase_      ,	resample=Image.BILINEAR      ,	)
     UpperCAmelCase_				:  str  = BertTokenizer.from_pretrained('bert-base-uncased'  )
     UpperCAmelCase_				:  str  = 64
     UpperCAmelCase_				:  Dict  = AlignProcessor(image_processor=lowerCamelCase_      ,	tokenizer=lowerCamelCase_  )
     return processor


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  Tuple  = [v.split('_'  )[0].split('block'  )[1] for v in original_param_names if v.startswith('block'  )]
     UpperCAmelCase_				:  Optional[Any]  = list(set(lowerCamelCase_  )  )
     UpperCAmelCase_				:  str  = sorted(lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[Any]  = len(lowerCamelCase_  )
     UpperCAmelCase_				:  Tuple  = {b: str(lowerCamelCase_  ) for b, i in zip(lowerCamelCase_      ,	range(lowerCamelCase_  )  )}

     UpperCAmelCase_				:  List[str]  = []
     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_				:  List[Any]  = 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_				:  str  = {}
     for item in rename_keys:
          if item[0] in original_param_names:
               UpperCAmelCase_				:  Dict  = 'vision_model.' + item[1]

    # BERT text encoder
     UpperCAmelCase_				:  Dict  = []
     UpperCAmelCase_				:  str  = 'tf_bert_model/bert'
     UpperCAmelCase_				:  Optional[Any]  = 'text_model'
     for i in range(12  ):
          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_				:  Optional[Any]  = item[1]
     return key_mapping


def    _lowerCamelCase       ( lowerCamelCase_   :				Union[str, Any]      ,	lowerCamelCase_   :				Union[str, Any]      ,	lowerCamelCase_   :				str  ):



     """simple docstring"""





     list(hf_params.keys()  )

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

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

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


@torch.no_grad()
def    _lowerCamelCase       ( lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				List[Any]  ):



     """simple docstring"""





     UpperCAmelCase_				:  Dict  = 64
     UpperCAmelCase_				:  Any  = Tokenizer(lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[Any]  = align.Align('efficientnet-b7'      ,	'bert-base'      ,	640      ,	lowerCamelCase_      ,	tok.get_vocab_size()  )
     original_model.compile()
     original_model.load_weights(lowerCamelCase_  )

     UpperCAmelCase_				:  str  = original_model.trainable_variables
     UpperCAmelCase_				:  int  = original_model.non_trainable_variables
     UpperCAmelCase_				:  Tuple  = {param.name: param.numpy() for param in tf_params}
     for param in tf_non_train_params:
          UpperCAmelCase_				:  List[str]  = param.numpy()
     UpperCAmelCase_				:  Union[str, Any]  = list(tf_params.keys()  )

     # Load HuggingFace model
     UpperCAmelCase_				:  Union[str, Any]  = get_align_config()
     UpperCAmelCase_				:  List[str]  = AlignModel(lowerCamelCase_  ).eval()
     UpperCAmelCase_				:  List[Any]  = hf_model.state_dict()

     # Create src-to-dst parameter name mapping dictionary
     print('Converting parameters...'  )
     UpperCAmelCase_				:  Optional[Any]  = rename_keys(lowerCamelCase_  )
     replace_params(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_  )

     # Initialize processor
     UpperCAmelCase_				:  Union[str, Any]  = get_processor()
     UpperCAmelCase_				:  Optional[Any]  = processor(
         images=prepare_img()      ,	text='A picture of a cat'      ,	padding='max_length'      ,	max_length=64      ,	return_tensors='pt'  )

     # HF model inference
     hf_model.eval()
     with torch.no_grad():
          UpperCAmelCase_				:  Optional[Any]  = hf_model(**lowerCamelCase_  )

     UpperCAmelCase_				:  Optional[Any]  = outputs.image_embeds.detach().numpy()
     UpperCAmelCase_				:  str  = outputs.text_embeds.detach().numpy()

     # Original model inference
     UpperCAmelCase_				:  str  = False
     UpperCAmelCase_				:  Tuple  = EfficientNetImageProcessor(
         do_center_crop=lowerCamelCase_      ,	do_rescale=lowerCamelCase_      ,	do_normalize=lowerCamelCase_      ,	include_top=lowerCamelCase_      ,	resample=Image.BILINEAR      ,	)
     UpperCAmelCase_				:  int  = tf_image_processor(images=prepare_img()      ,	return_tensors='tf'      ,	data_format='channels_last'  )['pixel_values']
     UpperCAmelCase_				:  Optional[Any]  = tok(tf.constant(['A picture of a cat']  )  )

     UpperCAmelCase_				:  Optional[int]  = original_model.image_encoder(lowerCamelCase_      ,	training=lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[int]  = original_model.text_encoder(lowerCamelCase_      ,	training=lowerCamelCase_  )

     UpperCAmelCase_				:  Union[str, Any]  = tf.nn.la_normalize(lowerCamelCase_      ,	axis=-1  )
     UpperCAmelCase_				:  Optional[int]  = 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__":
 snake_case__	:     Dict      =    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''')

 snake_case__	:     Tuple      =    parser.parse_args()
 convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)











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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''
						a__ =	"mbart"
						a__ =	["past_key_values"]
						a__ =	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







						def __init__(						self   :      int						,						__lowerCamelCase   :      Optional[int]=5_02_65						,						__lowerCamelCase   :      str=10_24						,						__lowerCamelCase   :      str=12						,						__lowerCamelCase   :      Dict=40_96						,						__lowerCamelCase   :      str=16						,						__lowerCamelCase   :      int=12						,						__lowerCamelCase   :      Optional[Any]=40_96						,						__lowerCamelCase   :      Any=16						,						__lowerCamelCase   :      Optional[Any]=0.0						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      Dict=True						,						__lowerCamelCase   :      List[str]=True						,						__lowerCamelCase   :      Optional[int]="gelu"						,						__lowerCamelCase   :      List[Any]=10_24						,						__lowerCamelCase   :      Dict=0.1						,						__lowerCamelCase   :      Tuple=0.0						,						__lowerCamelCase   :      List[str]=0.0						,						__lowerCamelCase   :      Union[str, Any]=0.02						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      List[Any]=False						,						__lowerCamelCase   :      Optional[Any]=1						,						__lowerCamelCase   :      str=0						,						__lowerCamelCase   :      Optional[Any]=2						,						__lowerCamelCase   :      Dict=2						,						**__lowerCamelCase   :      int						,						)   ->					Union[str, Any]:
									A				:       Union[str, Any]														=    vocab_size
									A				:       List[str]														=    max_position_embeddings
									A				:       Optional[int]														=    d_model
									A				:       Any														=    encoder_ffn_dim
									A				:       Union[str, Any]														=    encoder_layers
									A				:       int														=    encoder_attention_heads
									A				:       Union[str, Any]														=    decoder_ffn_dim
									A				:       Any														=    decoder_layers
									A				:       Tuple														=    decoder_attention_heads
									A				:       Any														=    dropout
									A				:       Union[str, Any]														=    attention_dropout
									A				:       str														=    activation_dropout
									A				:       Dict														=    activation_function
									A				:       Optional[int]														=    init_std
									A				:       Optional[Any]														=    encoder_layerdrop
									A				:       Union[str, Any]														=    decoder_layerdrop
									A				:       Tuple														=    classifier_dropout
									A				:       Union[str, Any]														=    use_cache
									A				:       List[str]														=    encoder_layers
									A				:       List[str]														=    scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__lowerCamelCase						,						bos_token_id=__lowerCamelCase						,						eos_token_id=__lowerCamelCase						,						is_encoder_decoder=__lowerCamelCase						,						forced_eos_token_id=__lowerCamelCase						,						**__lowerCamelCase						,						)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[str]														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)

												if self.use_past:
															A				:       str														=    {0: "batch"}
															A				:       Optional[int]														=    {0: "batch", 1: "past_decoder_sequence + sequence"}
												else:
															A				:       Optional[int]														=    {0: "batch", 1: "decoder_sequence"}
															A				:       Union[str, Any]														=    {0: "batch", 1: "decoder_sequence"}

												if self.use_past:
															self.fill_with_past_key_values_(__lowerCamelCase						,						direction="inputs"			)
									elif self.task == "causal-lm":
												# TODO: figure this case out.
												A				:       Dict														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)
												if self.use_past:
															A							,			A				:       Union[str, Any]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[str]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       int														=    {0: "batch", 2: "past_sequence + sequence"}
									else:
												A				:       Optional[int]														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
												        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
												    ]			)

									return common_inputs







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       str														=    super().outputs
									else:
												A				:       int														=    super(__lowerCamelCase						,						self			).outputs
												if self.use_past:
															A							,			A				:       Optional[int]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[Any]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       Tuple														=    {0: "batch", 2: "past_sequence + sequence"}
									return common_outputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       Tuple														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)

									# Generate decoder inputs
									A				:       Optional[int]														=    seq_length if not self.use_past else 1
									A				:       str														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)
									A				:       Tuple														=    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									A				:       Union[str, Any]														=    dict(**__lowerCamelCase						,						**__lowerCamelCase			)

									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				:       List[Any]														=    common_inputs["input_ids"].shape
												A				:       Any														=    common_inputs["decoder_input_ids"].shape[1]
												A							,			A				:       Union[str, Any]														=    self.num_attention_heads
												A				:       Tuple														=    (
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												A				:       Dict														=    decoder_seq_length + 3
												A				:       Union[str, Any]														=    (
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)

												A				:       int														=    torch.cat(
												    [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase			)]						,						dim=1			)

												A				:       Optional[int]														=    []
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												A							,			A				:       Dict														=    self.num_layers
												A				:       Dict														=    min(__lowerCamelCase						,						__lowerCamelCase			)
												A				:       Optional[Any]														=    max(__lowerCamelCase						,						__lowerCamelCase			) - min_num_layers
												A				:       Tuple														=    "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

												for _ in range(__lowerCamelCase			):
															common_inputs["past_key_values"].append(
															    (
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															    )			)
												# TODO: test this.
												A				:       List[str]														=    encoder_shape if remaining_side_name == "encoder" else decoder_shape
												for _ in range(__lowerCamelCase						,						__lowerCamelCase			):
															common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			))			)
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       List[Any]														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)

									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				:       List[Any]														=    common_inputs["input_ids"].shape
												# Not using the same length for past_key_values
												A				:       Any														=    seqlen + 2
												A							,			A				:       List[Any]														=    self.num_layers
												A							,			A				:       int														=    self.num_attention_heads
												A				:       int														=    (
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												A				:       Tuple														=    common_inputs["attention_mask"].dtype
												A				:       Dict														=    torch.cat(
												    [common_inputs["attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase						,						dtype=__lowerCamelCase			)]						,						dim=1			)
												A				:       Optional[Any]														=    [
												    (torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			)) for _ in range(__lowerCamelCase			)
												]
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									A				:       Any														=    compute_effective_axis_dimension(
									    __lowerCamelCase						,						fixed_dimension=OnnxConfig.default_fixed_batch						,						num_token_to_add=0			)

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									A				:       Any														=    tokenizer.num_special_tokens_to_add(__lowerCamelCase			)
									A				:       Tuple														=    compute_effective_axis_dimension(
									    __lowerCamelCase						,						fixed_dimension=OnnxConfig.default_fixed_sequence						,						num_token_to_add=__lowerCamelCase			)

									# Generate dummy inputs according to compute batch and sequence
									A				:       Dict														=    [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
									A				:       Tuple														=    dict(tokenizer(__lowerCamelCase						,						return_tensors=__lowerCamelCase			)			)
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[Any]														=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)

									elif self.task == "causal-lm":
												A				:       Tuple														=    self._generate_dummy_inputs_for_causal_lm(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)
									else:
												A				:       List[Any]														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)

									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Union[str, Any]						,						__lowerCamelCase   :      Any						,						__lowerCamelCase   :      List[Any]			)   ->					Dict:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       Any														=    super()._flatten_past_key_values_(__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)
									else:
												A				:       Optional[Any]														=    super(__lowerCamelCase						,						self			)._flatten_past_key_values_(
												    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)