
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase					: Union[str, Any]      =       logging.get_logger(__name__)


def       A       (					lowercase )   ->  List[str]:
		'''simple docstring'''

		UpperCamelCase		=     DPTConfig()

		if "large" in checkpoint_url:
				UpperCamelCase		=     1_024
				UpperCamelCase		=     4_096
				UpperCamelCase		=     24
				UpperCamelCase		=     16
				UpperCamelCase		=     [5, 11, 17, 23]
				UpperCamelCase		=     [256, 512, 1_024, 1_024]
				UpperCamelCase		=     (1, 384, 384)

		if "ade" in checkpoint_url:
				UpperCamelCase		=     True

				UpperCamelCase		=     150
				UpperCamelCase		=     'huggingface/label-files'
				UpperCamelCase		=     'ade20k-id2label.json'
				UpperCamelCase		=     json.load(open(cached_download(hf_hub_url(lowercase    ,       lowercase    ,       repo_type='dataset' ) )    ,       'r' ) )
				UpperCamelCase		=     {int(lowercase ): v for k, v in idalabel.items()}
				UpperCamelCase		=     idalabel
				UpperCamelCase		=     {v: k for k, v in idalabel.items()}
				UpperCamelCase		=     [1, 150, 480, 480]

		return config, expected_shape


def       A       (					lowercase )   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase		=     ['pretrained.model.head.weight', 'pretrained.model.head.bias']
		for k in ignore_keys:
				state_dict.pop(lowercase    ,       lowercase )


def       A       (					lowercase )   ->  List[Any]:
		'''simple docstring'''

		if (
		    "pretrained.model" in name
		    and "cls_token" not in name
		    and "pos_embed" not in name
		    and "patch_embed" not in name
		):
				UpperCamelCase		=     name.replace('pretrained.model'    ,       'dpt.encoder' )
		if "pretrained.model" in name:
				UpperCamelCase		=     name.replace('pretrained.model'    ,       'dpt.embeddings' )
		if "patch_embed" in name:
				UpperCamelCase		=     name.replace('patch_embed'    ,       'patch_embeddings' )
		if "pos_embed" in name:
				UpperCamelCase		=     name.replace('pos_embed'    ,       'position_embeddings' )
		if "attn.proj" in name:
				UpperCamelCase		=     name.replace('attn.proj'    ,       'attention.output.dense' )
		if "proj" in name and "project" not in name:
				UpperCamelCase		=     name.replace('proj'    ,       'projection' )
		if "blocks" in name:
				UpperCamelCase		=     name.replace('blocks'    ,       'layer' )
		if "mlp.fc1" in name:
				UpperCamelCase		=     name.replace('mlp.fc1'    ,       'intermediate.dense' )
		if "mlp.fc2" in name:
				UpperCamelCase		=     name.replace('mlp.fc2'    ,       'output.dense' )
		if "norm1" in name:
				UpperCamelCase		=     name.replace('norm1'    ,       'layernorm_before' )
		if "norm2" in name:
				UpperCamelCase		=     name.replace('norm2'    ,       'layernorm_after' )
		if "scratch.output_conv" in name:
				UpperCamelCase		=     name.replace('scratch.output_conv'    ,       'head' )
		if "scratch" in name:
				UpperCamelCase		=     name.replace('scratch'    ,       'neck' )
		if "layer1_rn" in name:
				UpperCamelCase		=     name.replace('layer1_rn'    ,       'convs.0' )
		if "layer2_rn" in name:
				UpperCamelCase		=     name.replace('layer2_rn'    ,       'convs.1' )
		if "layer3_rn" in name:
				UpperCamelCase		=     name.replace('layer3_rn'    ,       'convs.2' )
		if "layer4_rn" in name:
				UpperCamelCase		=     name.replace('layer4_rn'    ,       'convs.3' )
		if "refinenet" in name:
				UpperCamelCase		=     int(name[len('neck.refinenet' ) : len('neck.refinenet' ) + 1] )
				# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
				UpperCamelCase		=     name.replace(f'''refinenet{layer_idx}'''    ,       f'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
		if "out_conv" in name:
				UpperCamelCase		=     name.replace('out_conv'    ,       'projection' )
		if "resConfUnit1" in name:
				UpperCamelCase		=     name.replace('resConfUnit1'    ,       'residual_layer1' )
		if "resConfUnit2" in name:
				UpperCamelCase		=     name.replace('resConfUnit2'    ,       'residual_layer2' )
		if "conv1" in name:
				UpperCamelCase		=     name.replace('conv1'    ,       'convolution1' )
		if "conv2" in name:
				UpperCamelCase		=     name.replace('conv2'    ,       'convolution2' )
		# readout blocks
		if "pretrained.act_postprocess1.0.project.0" in name:
				UpperCamelCase		=     name.replace('pretrained.act_postprocess1.0.project.0'    ,       'neck.reassemble_stage.readout_projects.0.0' )
		if "pretrained.act_postprocess2.0.project.0" in name:
				UpperCamelCase		=     name.replace('pretrained.act_postprocess2.0.project.0'    ,       'neck.reassemble_stage.readout_projects.1.0' )
		if "pretrained.act_postprocess3.0.project.0" in name:
				UpperCamelCase		=     name.replace('pretrained.act_postprocess3.0.project.0'    ,       'neck.reassemble_stage.readout_projects.2.0' )
		if "pretrained.act_postprocess4.0.project.0" in name:
				UpperCamelCase		=     name.replace('pretrained.act_postprocess4.0.project.0'    ,       'neck.reassemble_stage.readout_projects.3.0' )
		# resize blocks
		if "pretrained.act_postprocess1.3" in name:
				UpperCamelCase		=     name.replace('pretrained.act_postprocess1.3'    ,       'neck.reassemble_stage.layers.0.projection' )
		if "pretrained.act_postprocess1.4" in name:
				UpperCamelCase		=     name.replace('pretrained.act_postprocess1.4'    ,       'neck.reassemble_stage.layers.0.resize' )
		if "pretrained.act_postprocess2.3" in name:
				UpperCamelCase		=     name.replace('pretrained.act_postprocess2.3'    ,       'neck.reassemble_stage.layers.1.projection' )
		if "pretrained.act_postprocess2.4" in name:
				UpperCamelCase		=     name.replace('pretrained.act_postprocess2.4'    ,       'neck.reassemble_stage.layers.1.resize' )
		if "pretrained.act_postprocess3.3" in name:
				UpperCamelCase		=     name.replace('pretrained.act_postprocess3.3'    ,       'neck.reassemble_stage.layers.2.projection' )
		if "pretrained.act_postprocess4.3" in name:
				UpperCamelCase		=     name.replace('pretrained.act_postprocess4.3'    ,       'neck.reassemble_stage.layers.3.projection' )
		if "pretrained.act_postprocess4.4" in name:
				UpperCamelCase		=     name.replace('pretrained.act_postprocess4.4'    ,       'neck.reassemble_stage.layers.3.resize' )
		if "pretrained" in name:
				UpperCamelCase		=     name.replace('pretrained'    ,       'dpt' )
		if "bn" in name:
				UpperCamelCase		=     name.replace('bn'    ,       'batch_norm' )
		if "head" in name:
				UpperCamelCase		=     name.replace('head'    ,       'head.head' )
		if "encoder.norm" in name:
				UpperCamelCase		=     name.replace('encoder.norm'    ,       'layernorm' )
		if "auxlayer" in name:
				UpperCamelCase		=     name.replace('auxlayer'    ,       'auxiliary_head.head' )

		return name


def       A       (					lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		for i in range(config.num_hidden_layers ):
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				UpperCamelCase		=     state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
				UpperCamelCase		=     state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
				# next, add query, keys and values (in that order) to the state dict
				UpperCamelCase		=     in_proj_weight[: config.hidden_size, :]
				UpperCamelCase		=     in_proj_bias[: config.hidden_size]
				UpperCamelCase		=     in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				UpperCamelCase		=     in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				UpperCamelCase		=     in_proj_weight[
				    -config.hidden_size :, :
				]
				UpperCamelCase		=     in_proj_bias[-config.hidden_size :]


def       A       (					)   ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase		=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase		=     Image.open(requests.get(lowercase    ,       stream=lowercase ).raw )
		return im


@torch.no_grad()
def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase )   ->  Optional[int]:
		'''simple docstring'''

		UpperCamelCase	,  UpperCamelCase		=     get_dpt_config(lowercase )
		# load original state_dict from URL
		UpperCamelCase		=     torch.hub.load_state_dict_from_url(lowercase    ,       map_location='cpu' )
		# remove certain keys
		remove_ignore_keys_(lowercase )
		# rename keys
		for key in state_dict.copy().keys():
				UpperCamelCase		=     state_dict.pop(lowercase )
				UpperCamelCase		=     val
		# read in qkv matrices
		read_in_q_k_v(lowercase    ,       lowercase )

		# load HuggingFace model
		UpperCamelCase		=     DPTForSemanticSegmentation(lowercase ) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowercase )
		model.load_state_dict(lowercase )
		model.eval()

		# Check outputs on an image
		UpperCamelCase		=     480 if 'ade' in checkpoint_url else 384
		UpperCamelCase		=     DPTImageProcessor(size=lowercase )

		UpperCamelCase		=     prepare_img()
		UpperCamelCase		=     image_processor(lowercase    ,       return_tensors='pt' )

		# forward pass
		UpperCamelCase		=     model(**lowercase ).logits if 'ade' in checkpoint_url else model(**lowercase ).predicted_depth

		# Assert logits
		UpperCamelCase		=     torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]] )
		if "ade" in checkpoint_url:
				UpperCamelCase		=     torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]] )
		assert outputs.shape == torch.Size(lowercase )
		assert (
		    torch.allclose(outputs[0, 0, :3, :3]    ,       lowercase    ,       atol=1e-4 )
		    if "ade" in checkpoint_url
		    else torch.allclose(outputs[0, :3, :3]    ,       lowercase )
		)

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

		if push_to_hub:
				print('Pushing model to hub...' )
				model.push_to_hub(
				    repo_path_or_name=Path(lowercase    ,       lowercase )    ,       organization='nielsr'    ,       commit_message='Add model'    ,       use_temp_dir=lowercase    ,       )
				image_processor.push_to_hub(
				    repo_path_or_name=Path(lowercase    ,       lowercase )    ,       organization='nielsr'    ,       commit_message='Add image processor'    ,       use_temp_dir=lowercase    ,       )


if __name__ == "__main__":
					_UpperCAmelCase					: Optional[int]      =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--checkpoint_url",
					    default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
					    type=str,
					    help="URL of the original DPT 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.",
					)
					parser.add_argument(
					    "--push_to_hub",
					    action="store_true",
					)
					parser.add_argument(
					    "--model_name",
					    default="dpt-large",
					    type=str,
					    help="Name of the model, in case you're pushing to the hub.",
					)

					_UpperCAmelCase					: Tuple      =       parser.parse_args()
					convert_dpt_checkpoint(args.checkpoint_url, 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 ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


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

_UpperCAmelCase					: Optional[Any]      =       {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       int	     =				"marian"
		__lowercase       :       List[str]	     =				["past_key_values"]
		__lowercase       :       Any	     =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



		def __init__( self				,      A_=58_101				,      A_=None				,      A_=1_024				,      A_=12				,      A_=4_096				,      A_=16				,      A_=12				,      A_=4_096				,      A_=16				,      A_=0.0				,      A_=0.0				,      A_=True				,      A_=True				,      A_="gelu"				,      A_=1_024				,      A_=0.1				,      A_=0.0				,      A_=0.0				,      A_=0.02				,      A_=58_100				,      A_=False				,      A_=58_100				,      A_=0				,      A_=0				,      A_=True				,      **A_				,      )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     vocab_size
				UpperCamelCase		=     decoder_vocab_size or 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
				UpperCamelCase		=     share_encoder_decoder_embeddings
				super().__init__(
				    pad_token_id=A_				,      eos_token_id=A_				,      is_encoder_decoder=A_				,      decoder_start_token_id=A_				,      forced_eos_token_id=A_				,      **A_				,      )






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
		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_(A_				,      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(A_       ):
										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
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
		def __UpperCamelCase ( self       )  ->					Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     super().outputs
				else:
						UpperCamelCase		=     super(A_				,      self       ).outputs
						if self.use_past:
								UpperCamelCase	,  UpperCamelCase		=     self.num_layers
								for i in range(A_       ):
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
				return common_outputs



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )

				# Generate decoder inputs
				UpperCamelCase		=     seq_length if not self.use_past else 1
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )
				UpperCamelCase		=     {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
				UpperCamelCase		=     dict(**A_				,      **A_       )

				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(A_				,      A_       )]				,      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(A_				,      A_       )
						UpperCamelCase		=     max(A_				,      A_       ) - min_num_layers
						UpperCamelCase		=     'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )

				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(A_				,      A_				,      dtype=A_       )]				,      dim=1       )
						UpperCamelCase		=     [
						    (torch.zeros(A_       ), torch.zeros(A_       )) for _ in range(A_       )
						]
				return common_inputs



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				# 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
				UpperCamelCase		=     compute_effective_axis_dimension(
				    A_				,      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(A_       )
				UpperCamelCase		=     compute_effective_axis_dimension(
				    A_				,      fixed_dimension=OnnxConfig.default_fixed_sequence				,      num_token_to_add=A_       )

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



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    A_				,      batch_size=A_				,      seq_length=A_				,      is_pair=A_				,      framework=A_       )

				else:
						UpperCamelCase		=     self._generate_dummy_inputs_for_causal_lm(
						    A_				,      batch_size=A_				,      seq_length=A_				,      is_pair=A_				,      framework=A_       )

				return common_inputs



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_       )  ->					int:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     super()._flatten_past_key_values_(A_				,      A_				,      A_				,      A_       )
				else:
						UpperCamelCase		=     super(A_				,      self       )._flatten_past_key_values_(
						    A_				,      A_				,      A_				,      A_       )



		@property
		def __UpperCamelCase ( self       )  ->					float:
				"""simple docstring"""
				return 1e-4
