



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A       =logging.get_logger(__name__)

def 			a		( _UpperCAmelCase				:   Optional[Any]		):







		'''simple docstring'''





		__UpperCAmelCase       :	Tuple						=      ConvNextConfig()

		if "tiny" in checkpoint_url:
				__UpperCAmelCase       :	Optional[int]						=      [3, 3, 9, 3]
				__UpperCAmelCase       :	Optional[Any]						=      [96, 1_92, 3_84, 7_68]
		if "small" in checkpoint_url:
				__UpperCAmelCase       :	str						=      [3, 3, 27, 3]
				__UpperCAmelCase       :	Optional[int]						=      [96, 1_92, 3_84, 7_68]
		if "base" in checkpoint_url:
				__UpperCAmelCase       :	str						=      [3, 3, 27, 3]
				__UpperCAmelCase       :	str						=      [1_28, 2_56, 5_12, 10_24]
		if "large" in checkpoint_url:
				__UpperCAmelCase       :	str						=      [3, 3, 27, 3]
				__UpperCAmelCase       :	List[Any]						=      [1_92, 3_84, 7_68, 15_36]
		if "xlarge" in checkpoint_url:
				__UpperCAmelCase       :	Optional[Any]						=      [3, 3, 27, 3]
				__UpperCAmelCase       :	List[str]						=      [2_56, 5_12, 10_24, 20_48]

		if "1k" in checkpoint_url:
				__UpperCAmelCase       :	List[str]						=      10_00
				__UpperCAmelCase       :	List[Any]						=      '''imagenet-1k-id2label.json'''
				__UpperCAmelCase       :	str						=      (1, 10_00)
		else:
				__UpperCAmelCase       :	List[Any]						=      2_18_41
				__UpperCAmelCase       :	Tuple						=      '''imagenet-22k-id2label.json'''
				__UpperCAmelCase       :	str						=      (1, 2_18_41)

		__UpperCAmelCase       :	List[Any]						=      '''huggingface/label-files'''
		__UpperCAmelCase       :	Optional[int]						=      num_labels
		__UpperCAmelCase       :	List[Any]						=      json.load(open(hf_hub_download(_UpperCAmelCase					,  _UpperCAmelCase					,  repo_type='''dataset'''		)					,  '''r'''		)		)
		__UpperCAmelCase       :	Dict						=      {int(_UpperCAmelCase		): v for k, v in idalabel.items()}
		if "1k" not in checkpoint_url:
				# this dataset contains 21843 labels but the model only has 21841
				# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
				del idalabel[92_05]
				del idalabel[1_50_27]
		__UpperCAmelCase       :	Tuple						=      idalabel
		__UpperCAmelCase       :	List[Any]						=      {v: k for k, v in idalabel.items()}
		__UpperCAmelCase       :	Dict						=      hidden_sizes
		__UpperCAmelCase       :	Any						=      depths

		return config, expected_shape

def 			a		( _UpperCAmelCase				:   List[Any]		):







		'''simple docstring'''





		if "downsample_layers.0.0" in name:
				__UpperCAmelCase       :	Optional[int]						=      name.replace('''downsample_layers.0.0'''					,  '''embeddings.patch_embeddings'''		)
		if "downsample_layers.0.1" in name:
				__UpperCAmelCase       :	List[Any]						=      name.replace('''downsample_layers.0.1'''					,  '''embeddings.norm'''		)  # we rename to layernorm later on
		if "downsample_layers.1.0" in name:
				__UpperCAmelCase       :	Optional[Any]						=      name.replace('''downsample_layers.1.0'''					,  '''stages.1.downsampling_layer.0'''		)
		if "downsample_layers.1.1" in name:
				__UpperCAmelCase       :	Optional[int]						=      name.replace('''downsample_layers.1.1'''					,  '''stages.1.downsampling_layer.1'''		)
		if "downsample_layers.2.0" in name:
				__UpperCAmelCase       :	Union[str, Any]						=      name.replace('''downsample_layers.2.0'''					,  '''stages.2.downsampling_layer.0'''		)
		if "downsample_layers.2.1" in name:
				__UpperCAmelCase       :	Dict						=      name.replace('''downsample_layers.2.1'''					,  '''stages.2.downsampling_layer.1'''		)
		if "downsample_layers.3.0" in name:
				__UpperCAmelCase       :	Tuple						=      name.replace('''downsample_layers.3.0'''					,  '''stages.3.downsampling_layer.0'''		)
		if "downsample_layers.3.1" in name:
				__UpperCAmelCase       :	Union[str, Any]						=      name.replace('''downsample_layers.3.1'''					,  '''stages.3.downsampling_layer.1'''		)
		if "stages" in name and "downsampling_layer" not in name:
				# stages.0.0. for instance should be renamed to stages.0.layers.0.
				__UpperCAmelCase       :	List[str]						=      name[: len('''stages.0'''		)] + '''.layers''' + name[len('''stages.0'''		) :]
		if "stages" in name:
				__UpperCAmelCase       :	str						=      name.replace('''stages'''					,  '''encoder.stages'''		)
		if "norm" in name:
				__UpperCAmelCase       :	Optional[int]						=      name.replace('''norm'''					,  '''layernorm'''		)
		if "gamma" in name:
				__UpperCAmelCase       :	Any						=      name.replace('''gamma'''					,  '''layer_scale_parameter'''		)
		if "head" in name:
				__UpperCAmelCase       :	Tuple						=      name.replace('''head'''					,  '''classifier'''		)

		return name

def 			a		( ):







		'''simple docstring'''





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

@torch.no_grad()
def 			a		( _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		__UpperCAmelCase				,   __UpperCAmelCase       :	Union[str, Any]						=      get_convnext_config(_UpperCAmelCase		)
		# load original state_dict from URL
		__UpperCAmelCase       :	Any						=      torch.hub.load_state_dict_from_url(_UpperCAmelCase		)['''model''']
		# rename keys
		for key in state_dict.copy().keys():
				__UpperCAmelCase       :	Tuple						=      state_dict.pop(_UpperCAmelCase		)
				__UpperCAmelCase       :	Any						=      val
		# add prefix to all keys expect classifier head
		for key in state_dict.copy().keys():
				__UpperCAmelCase       :	Union[str, Any]						=      state_dict.pop(_UpperCAmelCase		)
				if not key.startswith('''classifier'''		):
						__UpperCAmelCase       :	Optional[int]						=      '''convnext.''' + key
				__UpperCAmelCase       :	Optional[Any]						=      val

		# load HuggingFace model
		__UpperCAmelCase       :	str						=      ConvNextForImageClassification(_UpperCAmelCase		)
		model.load_state_dict(_UpperCAmelCase		)
		model.eval()

		# Check outputs on an image, prepared by ConvNextImageProcessor
		__UpperCAmelCase       :	List[Any]						=      2_24 if '''224''' in checkpoint_url else 3_84
		__UpperCAmelCase       :	Optional[Any]						=      ConvNextImageProcessor(size=_UpperCAmelCase		)
		__UpperCAmelCase       :	List[Any]						=      image_processor(images=prepare_img()					,  return_tensors='''pt'''		).pixel_values

		__UpperCAmelCase       :	str						=      model(_UpperCAmelCase		).logits

		# note: the logits below were obtained without center cropping
		if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
				__UpperCAmelCase       :	Tuple						=      torch.tensor([-0.12_10, -0.66_05, 0.19_18]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
				__UpperCAmelCase       :	Any						=      torch.tensor([-0.44_73, -0.18_47, -0.63_65]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
				__UpperCAmelCase       :	List[str]						=      torch.tensor([0.45_25, 0.75_39, 0.03_08]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
				__UpperCAmelCase       :	int						=      torch.tensor([0.35_61, 0.63_50, -0.03_84]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
				__UpperCAmelCase       :	Optional[int]						=      torch.tensor([0.41_74, -0.09_89, 0.14_89]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
				__UpperCAmelCase       :	int						=      torch.tensor([0.25_13, -0.13_49, -0.16_13]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
				__UpperCAmelCase       :	Union[str, Any]						=      torch.tensor([1.29_80, 0.36_31, -0.11_98]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
				__UpperCAmelCase       :	Union[str, Any]						=      torch.tensor([1.29_63, 0.12_27, 0.17_23]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
				__UpperCAmelCase       :	Union[str, Any]						=      torch.tensor([1.79_56, 0.83_90, 0.28_20]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
				__UpperCAmelCase       :	Tuple						=      torch.tensor([-0.28_22, -0.05_02, -0.08_78]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
				__UpperCAmelCase       :	Tuple						=      torch.tensor([-0.56_72, -0.07_30, -0.43_48]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
				__UpperCAmelCase       :	Union[str, Any]						=      torch.tensor([0.26_81, 0.23_65, 0.62_46]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
				__UpperCAmelCase       :	List[Any]						=      torch.tensor([-0.26_42, 0.39_31, 0.51_16]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
				__UpperCAmelCase       :	Tuple						=      torch.tensor([-0.66_77, -0.18_73, -0.83_79]		)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
				__UpperCAmelCase       :	Dict						=      torch.tensor([-0.77_49, -0.29_67, -0.64_44]		)
		else:
				raise ValueError(f'Unknown URL: {checkpoint_url}'		)

		assert torch.allclose(logits[0, :3]					,  _UpperCAmelCase					,  atol=1e-3		)
		assert logits.shape == expected_shape

		Path(_UpperCAmelCase		).mkdir(exist_ok=_UpperCAmelCase		)
		print(f'Saving model to {pytorch_dump_folder_path}'		)
		model.save_pretrained(_UpperCAmelCase		)
		print(f'Saving image processor to {pytorch_dump_folder_path}'		)
		image_processor.save_pretrained(_UpperCAmelCase		)

		print('''Pushing model to the hub...'''		)
		__UpperCAmelCase       :	Union[str, Any]						=      '''convnext'''
		if "tiny" in checkpoint_url:
				model_name += "-tiny"
		elif "small" in checkpoint_url:
				model_name += "-small"
		elif "base" in checkpoint_url:
				model_name += "-base"
		elif "xlarge" in checkpoint_url:
				model_name += "-xlarge"
		elif "large" in checkpoint_url:
				model_name += "-large"
		if "224" in checkpoint_url:
				model_name += "-224"
		elif "384" in checkpoint_url:
				model_name += "-384"
		if "22k" in checkpoint_url and "1k" not in checkpoint_url:
				model_name += "-22k"
		if "22k" in checkpoint_url and "1k" in checkpoint_url:
				model_name += "-22k-1k"

		model.push_to_hub(
		    repo_path_or_name=Path(_UpperCAmelCase					,  _UpperCAmelCase		)					,  organization='''nielsr'''					,  commit_message='''Add model'''					,  )


if __name__ == "__main__":
				__A       =argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_url",
				    default="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
				    type=str,
				    help="URL of the original ConvNeXT checkpoint you'd like to convert.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    type=str,
				    required=True,
				    help="Path to the output PyTorch model directory.",
				)

				__A       =parser.parse_args()
				convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









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

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


UpperCAmelCase     =       logging.get_logger(__name__)

UpperCAmelCase     =       {
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 __snake_case				=    "blenderbot"
 __snake_case				=    ["past_key_values"]
 __snake_case				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



 def __init__(      self						, _UpperCAmelCase=80_08						, _UpperCAmelCase=1_28						, _UpperCAmelCase=2						, _UpperCAmelCase=1_02_40						, _UpperCAmelCase=32						, _UpperCAmelCase=24						, _UpperCAmelCase=1_02_40						, _UpperCAmelCase=32						, _UpperCAmelCase=0.0						, _UpperCAmelCase=0.0						, _UpperCAmelCase=True						, _UpperCAmelCase=True						, _UpperCAmelCase="gelu"						, _UpperCAmelCase=25_60						, _UpperCAmelCase=0.1						, _UpperCAmelCase=0.0						, _UpperCAmelCase=0.0						, _UpperCAmelCase=0.02						, _UpperCAmelCase=1						, _UpperCAmelCase=False						, _UpperCAmelCase=0						, _UpperCAmelCase=1						, _UpperCAmelCase=2						, _UpperCAmelCase=3						, _UpperCAmelCase=2						, **_UpperCAmelCase						, ):
       snake_case_			= vocab_size
       snake_case_			= max_position_embeddings
       snake_case_			= d_model
       snake_case_			= encoder_ffn_dim
       snake_case_			= encoder_layers
       snake_case_			= encoder_attention_heads
       snake_case_			= decoder_ffn_dim
       snake_case_			= decoder_layers
       snake_case_			= decoder_attention_heads
       snake_case_			= dropout
       snake_case_			= attention_dropout
       snake_case_			= activation_dropout
       snake_case_			= activation_function
       snake_case_			= init_std
       snake_case_			= encoder_layerdrop
       snake_case_			= decoder_layerdrop
       snake_case_			= use_cache
       snake_case_			= encoder_layers
       snake_case_			= scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=_UpperCAmelCase						, bos_token_id=_UpperCAmelCase						, eos_token_id=_UpperCAmelCase						, is_encoder_decoder=_UpperCAmelCase						, decoder_start_token_id=_UpperCAmelCase						, encoder_no_repeat_ngram_size=_UpperCAmelCase						, forced_eos_token_id=_UpperCAmelCase						, **_UpperCAmelCase						, )







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 @property
 def 		UpperCamelCase__	(      self   ):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_			= OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]   )
             if self.use_past:
                   snake_case_			= {0: '''batch'''}
                   snake_case_			= {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
             else:
                   snake_case_			= {0: '''batch''', 1: '''decoder_sequence'''}
                   snake_case_			= {0: '''batch''', 1: '''decoder_sequence'''}
             if self.use_past:
                   self.fill_with_past_key_values_(_UpperCAmelCase						, direction='''inputs'''   )
       elif self.task == "causal-lm":
             snake_case_			= OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]   )
             if self.use_past:
                   snake_case_			,       snake_case_			= self.num_layers
                   for i in range(_UpperCAmelCase   ):
                         snake_case_			= {0: '''batch''', 2: '''past_sequence + sequence'''}
                         snake_case_			= {0: '''batch''', 2: '''past_sequence + sequence'''}
       else:
             snake_case_			= 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
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 		UpperCamelCase__	(      self   ):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_			= super().outputs
       else:
             snake_case_			= super(_UpperCAmelCase						, self   ).outputs
             if self.use_past:
                   snake_case_			,       snake_case_			= self.num_layers
                   for i in range(_UpperCAmelCase   ):
                         snake_case_			= {0: '''batch''', 2: '''past_sequence + sequence'''}
                         snake_case_			= {0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, ):
       snake_case_			= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   )
       # Generate decoder inputs
       snake_case_			= seq_length if not self.use_past else 1
       snake_case_			= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   )
       snake_case_			= {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       snake_case_			= dict(**_UpperCAmelCase						, **_UpperCAmelCase   )

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
             else:
                   import torch
             snake_case_			,       snake_case_			= common_inputs['''input_ids'''].shape
             snake_case_			= common_inputs['''decoder_input_ids'''].shape[1]
             snake_case_			,       snake_case_			= self.num_attention_heads
             snake_case_			= (
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             snake_case_			= decoder_seq_length
             snake_case_			= (
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             snake_case_			= torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(_UpperCAmelCase						, _UpperCAmelCase   )]						, dim=1   )
             snake_case_			= []
             snake_case_			,       snake_case_			= self.num_layers

             for _ in range(_UpperCAmelCase   ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(_UpperCAmelCase   ),
                           torch.zeros(_UpperCAmelCase   ),
                           torch.zeros(_UpperCAmelCase   ),
                           torch.zeros(_UpperCAmelCase   ),
                       )   )
       return common_inputs



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, ):
       snake_case_			= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   )

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
             else:
                   import torch
             snake_case_			,       snake_case_			= common_inputs['''input_ids'''].shape
             snake_case_			= seqlen
             snake_case_			,       snake_case_			= self.num_layers
             snake_case_			,       snake_case_			= self.num_attention_heads
             snake_case_			= (
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             snake_case_			= common_inputs['''attention_mask'''].dtype
             snake_case_			= torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(_UpperCAmelCase						, _UpperCAmelCase						, dtype=_UpperCAmelCase   )]						, dim=1   )
             snake_case_			= [
                 (torch.zeros(_UpperCAmelCase   ), torch.zeros(_UpperCAmelCase   )) for _ in range(_UpperCAmelCase   )
             ]
       return common_inputs



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, ):
       # 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
       snake_case_			= compute_effective_axis_dimension(
           _UpperCAmelCase						, 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
       snake_case_			= tokenizer.num_special_tokens_to_add(_UpperCAmelCase   )
       snake_case_			= compute_effective_axis_dimension(
           _UpperCAmelCase						, fixed_dimension=OnnxConfig.default_fixed_sequence						, num_token_to_add=_UpperCAmelCase   )

       # Generate dummy inputs according to compute batch and sequence
       snake_case_			= [''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
       snake_case_			= dict(tokenizer(_UpperCAmelCase						, return_tensors=_UpperCAmelCase   )   )
       return common_inputs



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, ):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_			= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _UpperCAmelCase						, batch_size=_UpperCAmelCase						, seq_length=_UpperCAmelCase						, is_pair=_UpperCAmelCase						, framework=_UpperCAmelCase   )

       elif self.task == "causal-lm":
             snake_case_			= self._generate_dummy_inputs_for_causal_lm(
                 _UpperCAmelCase						, batch_size=_UpperCAmelCase						, seq_length=_UpperCAmelCase						, is_pair=_UpperCAmelCase						, framework=_UpperCAmelCase   )
       else:
             snake_case_			= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _UpperCAmelCase						, batch_size=_UpperCAmelCase						, seq_length=_UpperCAmelCase						, is_pair=_UpperCAmelCase						, framework=_UpperCAmelCase   )

       return common_inputs



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   ):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_			= super()._flatten_past_key_values_(_UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   )
       else:
             snake_case_			= super(_UpperCAmelCase						, self   )._flatten_past_key_values_(
                 _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   )





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       if direction not in ["inputs", "outputs"]:
             raise ValueError(F'''direction must either be "inputs" or "outputs", but {direction} was given'''   )

       snake_case_			= '''past_key_values''' if direction == '''inputs''' else '''present'''
       snake_case_			,       snake_case_			= self.num_layers

       snake_case_			= '''past_encoder_sequence'''
       snake_case_			= '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

       for i in range(_UpperCAmelCase   ):
             snake_case_			= {0: '''batch''', 2: decoder_sequence}
             snake_case_			= {0: '''batch''', 2: decoder_sequence}
             snake_case_			= {0: '''batch''', 2: encoder_sequence}
             snake_case_			= {0: '''batch''', 2: encoder_sequence}