


import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			: List[str]				   =					logging.get_logger(__name__)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    _lowercase			:				List[str] 						=						OrderedDict()
    for key, value in state_dict.items():
        if key.startswith('module.encoder'					):
            _lowercase			:				List[Any] 						=						key.replace('module.encoder'				,					'glpn.encoder'					)
        if key.startswith('module.decoder'					):
            _lowercase			:				Optional[int] 						=						key.replace('module.decoder'				,					'decoder.stages'					)
        if "patch_embed" in key:
            # replace for example patch_embed1 by patch_embeddings.0
            _lowercase			:				Tuple 						=						key[key.find('patch_embed'					) + len('patch_embed'					)]
            _lowercase			:				Union[str, Any] 						=						key.replace(F'''patch_embed{idx}'''				,					F'''patch_embeddings.{int(lowerCamelCase_					)-1}'''					)
        if "norm" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('norm'				,					'layer_norm'					)
        if "glpn.encoder.layer_norm" in key:
            # replace for example layer_norm1 by layer_norm.0
            _lowercase			:				Dict 						=						key[key.find('glpn.encoder.layer_norm'					) + len('glpn.encoder.layer_norm'					)]
            _lowercase			:				Dict 						=						key.replace(F'''layer_norm{idx}'''				,					F'''layer_norm.{int(lowerCamelCase_					)-1}'''					)
        if "layer_norm1" in key:
            _lowercase			:				Dict 						=						key.replace('layer_norm1'				,					'layer_norm_1'					)
        if "layer_norm2" in key:
            _lowercase			:				Dict 						=						key.replace('layer_norm2'				,					'layer_norm_2'					)
        if "block" in key:
            # replace for example block1 by block.0
            _lowercase			:				Optional[int] 						=						key[key.find('block'					) + len('block'					)]
            _lowercase			:				str 						=						key.replace(F'''block{idx}'''				,					F'''block.{int(lowerCamelCase_					)-1}'''					)
        if "attn.q" in key:
            _lowercase			:				List[Any] 						=						key.replace('attn.q'				,					'attention.self.query'					)
        if "attn.proj" in key:
            _lowercase			:				Tuple 						=						key.replace('attn.proj'				,					'attention.output.dense'					)
        if "attn" in key:
            _lowercase			:				Optional[int] 						=						key.replace('attn'				,					'attention.self'					)
        if "fc1" in key:
            _lowercase			:				Dict 						=						key.replace('fc1'				,					'dense1'					)
        if "fc2" in key:
            _lowercase			:				Optional[int] 						=						key.replace('fc2'				,					'dense2'					)
        if "linear_pred" in key:
            _lowercase			:				str 						=						key.replace('linear_pred'				,					'classifier'					)
        if "linear_fuse" in key:
            _lowercase			:				List[Any] 						=						key.replace('linear_fuse.conv'				,					'linear_fuse'					)
            _lowercase			:				str 						=						key.replace('linear_fuse.bn'				,					'batch_norm'					)
        if "linear_c" in key:
            # replace for example linear_c4 by linear_c.3
            _lowercase			:				int 						=						key[key.find('linear_c'					) + len('linear_c'					)]
            _lowercase			:				str 						=						key.replace(F'''linear_c{idx}'''				,					F'''linear_c.{int(lowerCamelCase_					)-1}'''					)
        if "bot_conv" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('bot_conv'				,					'0.convolution'					)
        if "skip_conv1" in key:
            _lowercase			:				int 						=						key.replace('skip_conv1'				,					'1.convolution'					)
        if "skip_conv2" in key:
            _lowercase			:				List[Any] 						=						key.replace('skip_conv2'				,					'2.convolution'					)
        if "fusion1" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('fusion1'				,					'1.fusion'					)
        if "fusion2" in key:
            _lowercase			:				Tuple 						=						key.replace('fusion2'				,					'2.fusion'					)
        if "fusion3" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('fusion3'				,					'3.fusion'					)
        if "fusion" in key and "conv" in key:
            _lowercase			:				Tuple 						=						key.replace('conv'				,					'convolutional_layer'					)
        if key.startswith('module.last_layer_depth'					):
            _lowercase			:				Dict 						=						key.replace('module.last_layer_depth'				,					'head.head'					)
        _lowercase			:				str 						=						value

    return new_state_dict



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[Any]:
    # for each of the encoder blocks:
    for i in range(config.num_encoder_blocks					):
        for j in range(config.depths[i]					):
            # read in weights + bias of keys and values (which is a single matrix in the original implementation)
            _lowercase			:				Tuple 						=						state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''					)
            _lowercase			:				str 						=						state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''					)
            # next, add keys and values (in that order) to the state dict
            _lowercase			:				int 						=						kv_weight[
                : config.hidden_sizes[i], :
            ]
            _lowercase			:				int 						=						kv_bias[: config.hidden_sizes[i]]
            _lowercase			:				str 						=						kv_weight[
                config.hidden_sizes[i] :, :
            ]
            _lowercase			:				Any 						=						kv_bias[config.hidden_sizes[i] :]



def 							UpperCamelCase_( )       ->		Tuple:
    _lowercase			:				int 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowercase			:				Optional[Any] 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)

    return image



@torch.no_grad()
def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False				,					lowerCamelCase_=None					)       ->		Optional[int]:
    _lowercase			:				List[Any] 						=						GLPNConfig(hidden_sizes=[64, 128, 320, 512]				,					decoder_hidden_size=64				,					depths=[3, 8, 27, 3]					)

    # load image processor (only resize + rescale)
    _lowercase			:				Optional[Any] 						=						GLPNImageProcessor()

    # prepare image
    _lowercase			:				List[Any] 						=						prepare_img()
    _lowercase			:				Union[str, Any] 						=						image_processor(images=lowerCamelCase_				,					return_tensors='pt'					).pixel_values

    logger.info('Converting model...'					)

    # load original state dict
    _lowercase			:				Tuple 						=						torch.load(lowerCamelCase_				,					map_location=torch.device('cpu'					)					)

    # rename keys
    _lowercase			:				Any 						=						rename_keys(lowerCamelCase_					)

    # key and value matrices need special treatment
    read_in_k_v(lowerCamelCase_				,					lowerCamelCase_					)

    # create HuggingFace model and load state dict
    _lowercase			:				Tuple 						=						GLPNForDepthEstimation(lowerCamelCase_					)
    model.load_state_dict(lowerCamelCase_					)
    model.eval()

    # forward pass
    _lowercase			:				Optional[Any] 						=						model(lowerCamelCase_					)
    _lowercase			:				Union[str, Any] 						=						outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            _lowercase			:				List[str] 						=						torch.tensor(
                [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]					)
        elif "kitti" in model_name:
            _lowercase			:				List[str] 						=						torch.tensor(
                [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]					)
        else:
            raise ValueError(F'''Unknown model name: {model_name}'''					)

        _lowercase			:				List[Any] 						=						torch.Size([1, 480, 640]					)

        assert predicted_depth.shape == expected_shape
        assert torch.allclose(predicted_depth[0, :3, :3]				,					lowerCamelCase_				,					atol=1e-4					)
        print('Looks ok!'					)

    # finally, push to hub if required
    if push_to_hub:
        logger.info('Pushing model and image processor to the hub...'					)
        model.push_to_hub(
            repo_path_or_name=Path(lowerCamelCase_				,					lowerCamelCase_					)				,					organization='nielsr'				,					commit_message='Add model'				,					use_temp_dir=lowerCamelCase_				,					)
        image_processor.push_to_hub(
            repo_path_or_name=Path(lowerCamelCase_				,					lowerCamelCase_					)				,					organization='nielsr'				,					commit_message='Add image processor'				,					use_temp_dir=lowerCamelCase_				,					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_path",
     default=None,
     type=str,
     help="Path to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
 )
 parser.add_argument(
     "--model_name",
     default="glpn-kitti",
     type=str,
     help="Name of the model in case you're pushing to the hub.",
 )
 SCREAMING_SNAKE_CASE			: str				   =					parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






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


lowerCAmelCase__    :List[Any]       =				logging.get_logger(__name__)

lowerCAmelCase__    :int       =				{
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Dict           =      'blenderbot-small'
		_a      :    Tuple           =      ['past_key_values']
		_a      :    List[str]           =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=50265     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=2     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     max_position_embeddings
									_UpperCAmelCase											=     d_model
									_UpperCAmelCase											=     encoder_ffn_dim
									_UpperCAmelCase											=     encoder_layers
									_UpperCAmelCase											=     encoder_attention_heads
									_UpperCAmelCase											=     decoder_ffn_dim
									_UpperCAmelCase											=     decoder_layers
									_UpperCAmelCase											=     decoder_attention_heads
									_UpperCAmelCase											=     dropout
									_UpperCAmelCase											=     attention_dropout
									_UpperCAmelCase											=     activation_dropout
									_UpperCAmelCase											=     activation_function
									_UpperCAmelCase											=     init_std
									_UpperCAmelCase											=     encoder_layerdrop
									_UpperCAmelCase											=     decoder_layerdrop
									_UpperCAmelCase											=     use_cache
									_UpperCAmelCase											=     encoder_layers
									_UpperCAmelCase											=     scale_embedding  # scale factor will be sqrt(d_model) if True

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



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]		)

																if self.use_past:
																							_UpperCAmelCase											=     {0: 'batch'}
																							_UpperCAmelCase											=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
																else:
																							_UpperCAmelCase											=     {0: 'batch', 1: 'decoder_sequence'}
																							_UpperCAmelCase											=     {0: 'batch', 1: 'decoder_sequence'}

																if self.use_past:
																							self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE     ,       direction='inputs'		)
									elif self.task == "causal-lm":
																# TODO: figure this case out.
																_UpperCAmelCase											=     OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]		)
																if self.use_past:
																							_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																							for i in range(_SCREAMING_SNAKE_CASE		):
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
									else:
																_UpperCAmelCase											=     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       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     super().outputs
									else:
																_UpperCAmelCase											=     super(_SCREAMING_SNAKE_CASE     ,       self		).outputs
																if self.use_past:
																							_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																							for i in range(_SCREAMING_SNAKE_CASE		):
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
									return common_outputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									# Generate decoder inputs
									_UpperCAmelCase											=     seq_length if not self.use_past else 1
									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
									_UpperCAmelCase											=     dict(**_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

									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
																_UpperCAmelCase											=     common_inputs['decoder_input_ids'].shape[1]
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_attention_heads
																_UpperCAmelCase											=     (
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																_UpperCAmelCase											=     decoder_seq_length + 3
																_UpperCAmelCase											=     (
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																_UpperCAmelCase											=     torch.cat(
																    [common_inputs['decoder_attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)]     ,       dim=1		)

																_UpperCAmelCase											=     []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																_UpperCAmelCase											=     min(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     max(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) - min_num_layers
																_UpperCAmelCase											=     'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

																for _ in range(_SCREAMING_SNAKE_CASE		):
																							common_inputs["past_key_values"].append(
																							    (
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							    )		)
																# TODO: test this.
																_UpperCAmelCase											=     encoder_shape if remaining_side_name == 'encoder' else decoder_shape
																for _ in range(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																							common_inputs["past_key_values"].append((torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		))		)
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									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						,			_UpperCAmelCase											=     self.num_layers
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_attention_heads
																_UpperCAmelCase											=     (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																_UpperCAmelCase											=     common_inputs['attention_mask'].dtype
																_UpperCAmelCase											=     torch.cat(
																    [common_inputs['attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE		)]     ,       dim=1		)
																_UpperCAmelCase											=     [
																    (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(_SCREAMING_SNAKE_CASE		)
																]
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     compute_effective_axis_dimension(
									    _SCREAMING_SNAKE_CASE     ,       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
									_UpperCAmelCase											=     tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     compute_effective_axis_dimension(
									    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_sequence     ,       num_token_to_add=_SCREAMING_SNAKE_CASE		)

									# Generate dummy inputs according to compute batch and sequence
									_UpperCAmelCase											=     [' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
									_UpperCAmelCase											=     dict(tokenizer(_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    _SCREAMING_SNAKE_CASE     ,       batch_size=_SCREAMING_SNAKE_CASE     ,       seq_length=_SCREAMING_SNAKE_CASE     ,       is_pair=_SCREAMING_SNAKE_CASE     ,       framework=_SCREAMING_SNAKE_CASE		)

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

									return common_inputs






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





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