



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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Dict:
				# initialize config
				if "resnet-50" in model_name:
								a__	:							Optional[int]      						= ResNetConfig.from_pretrained("microsoft/resnet-50" )
				elif "resnet-101" in model_name:
								a__	:							Union[str, Any]      						= ResNetConfig.from_pretrained("microsoft/resnet-101" )
				else:
								raise ValueError("Model name should include either resnet50 or resnet101" )

				a__	:							Union[str, Any]      						= DetrConfig(use_timm_backbone=__UpperCamelCase	,				backbone_config=__UpperCamelCase )

				# set label attributes
				a__	:							Dict      						= "panoptic" in model_name
				if is_panoptic:
								a__	:							Tuple      						= 2_50
				else:
								a__	:							Any      						= 91
								a__	:							Tuple      						= "huggingface/label-files"
								a__	:							str      						= "coco-detection-id2label.json"
								a__	:							Optional[Any]      						= json.load(open(hf_hub_download(__UpperCamelCase	,				__UpperCamelCase	,				repo_type="dataset" )	,				"r" ) )
								a__	:							Any      						= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
								a__	:							int      						= idalabel
								a__	:							Optional[int]      						= {v: k for k, v in idalabel.items()}

				return config, is_panoptic






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						int:
				# here we list all keys to be renamed (original name on the left, our name on the right)
				a__	:							Tuple      						= []

				# stem
				# fmt: off
				rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight") )
				rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight") )
				rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias") )
				rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean") )
				rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var") )
				# stages
				for stage_idx in range(len(config.backbone_config.depths ) ):
								for layer_idx in range(config.backbone_config.depths[stage_idx] ):
												# shortcut
												if layer_idx == 0:
																rename_keys.append(
																    (
																        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
																        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
																    ) )
																rename_keys.append(
																    (
																        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
																        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
																    ) )
																rename_keys.append(
																    (
																        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
																        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
																    ) )
																rename_keys.append(
																    (
																        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
																        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
																    ) )
																rename_keys.append(
																    (
																        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
																        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
																    ) )
												# 3 convs
												for i in range(3 ):
																rename_keys.append(
																    (
																        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
																        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
																    ) )
																rename_keys.append(
																    (
																        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
																        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
																    ) )
																rename_keys.append(
																    (
																        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
																        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
																    ) )
																rename_keys.append(
																    (
																        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
																        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
																    ) )
																rename_keys.append(
																    (
																        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
																        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
																    ) )
    # fmt: on

				for i in range(config.encoder_layers ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (
								        F'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
								        F'encoder.layers.{i}.self_attn.out_proj.weight',
								    ) )
								rename_keys.append(
								    (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias') )
								rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight') )
								rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias') )
								rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight') )
								rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias') )
								rename_keys.append(
								    (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight') )
								rename_keys.append(
								    (F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias') )
								rename_keys.append(
								    (F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight') )
								rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias') )
								# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
								rename_keys.append(
								    (
								        F'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
								        F'decoder.layers.{i}.self_attn.out_proj.weight',
								    ) )
								rename_keys.append(
								    (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias') )
								rename_keys.append(
								    (
								        F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
								        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
								    ) )
								rename_keys.append(
								    (
								        F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
								        F'decoder.layers.{i}.encoder_attn.out_proj.bias',
								    ) )
								rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight') )
								rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias') )
								rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight') )
								rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias') )
								rename_keys.append(
								    (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight') )
								rename_keys.append(
								    (F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias') )
								rename_keys.append(
								    (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight') )
								rename_keys.append(
								    (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias') )
								rename_keys.append(
								    (F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight') )
								rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias') )

				# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
				rename_keys.extend(
				    [
				        ("input_proj.weight", "input_projection.weight"),
				        ("input_proj.bias", "input_projection.bias"),
				        ("query_embed.weight", "query_position_embeddings.weight"),
				        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
				        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
				        ("class_embed.weight", "class_labels_classifier.weight"),
				        ("class_embed.bias", "class_labels_classifier.bias"),
				        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
				        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
				        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
				        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
				        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
				        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
				    ] )

				return rename_keys






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Tuple:
				a__	:							Union[str, Any]      						= state_dict.pop(__UpperCamelCase )
				a__	:							int      						= val






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=False )	->						Tuple:
				a__	:							Optional[int]      						= ""
				if is_panoptic:
								a__	:							List[str]      						= "detr."

				# first: transformer encoder
				for i in range(6 ):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								a__	:							Optional[Any]      						= state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
								a__	:							Dict      						= state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
								# next, add query, keys and values (in that order) to the state dict
								a__	:							Tuple      						= in_proj_weight[:2_56, :]
								a__	:							List[Any]      						= in_proj_bias[:2_56]
								a__	:							Tuple      						= in_proj_weight[2_56:5_12, :]
								a__	:							Tuple      						= in_proj_bias[2_56:5_12]
								a__	:							int      						= in_proj_weight[-2_56:, :]
								a__	:							Union[str, Any]      						= in_proj_bias[-2_56:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6 ):
								# read in weights + bias of input projection layer of self-attention
								a__	:							Optional[int]      						= state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
								a__	:							str      						= state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
								# next, add query, keys and values (in that order) to the state dict
								a__	:							Optional[Any]      						= in_proj_weight[:2_56, :]
								a__	:							List[Any]      						= in_proj_bias[:2_56]
								a__	:							Any      						= in_proj_weight[2_56:5_12, :]
								a__	:							Any      						= in_proj_bias[2_56:5_12]
								a__	:							Dict      						= in_proj_weight[-2_56:, :]
								a__	:							List[Any]      						= in_proj_bias[-2_56:]
								# read in weights + bias of input projection layer of cross-attention
								a__	:							List[Any]      						= state_dict.pop(
								    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
								a__	:							Dict      						= state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								a__	:							Any      						= in_proj_weight_cross_attn[:2_56, :]
								a__	:							List[Any]      						= in_proj_bias_cross_attn[:2_56]
								a__	:							List[str]      						= in_proj_weight_cross_attn[2_56:5_12, :]
								a__	:							int      						= in_proj_bias_cross_attn[2_56:5_12]
								a__	:							Dict      						= in_proj_weight_cross_attn[-2_56:, :]
								a__	:							str      						= in_proj_bias_cross_attn[-2_56:]






def     SCREAMING_SNAKE_CASE(      )	->						Dict:
				a__	:							Union[str, Any]      						= "http://images.cocodataset.org/val2017/000000039769.jpg"
				a__	:							List[str]      						= Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase ).raw )

				return im






@torch.no_grad()
def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=None	,				__UpperCamelCase=False )	->						Union[str, Any]:
				a__						,   a__	:							Tuple      						= get_detr_config(__UpperCamelCase )

				# load original model from torch hub
				a__	:							Tuple      						= {
				    "detr-resnet-50": "detr_resnet50",
				    "detr-resnet-101": "detr_resnet101",
				}
				logger.info(F'Converting model {model_name}...' )
				a__	:							Optional[int]      						= torch.hub.load("facebookresearch/detr"	,				model_name_to_original_name[model_name]	,				pretrained=__UpperCamelCase ).eval()
				a__	:							Any      						= detr.state_dict()
				# rename keys
				for src, dest in create_rename_keys(__UpperCamelCase ):
								if is_panoptic:
												a__	:							Optional[int]      						= "detr." + src
								rename_key(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
				# query, key and value matrices need special treatment
				read_in_q_k_v(__UpperCamelCase	,				is_panoptic=__UpperCamelCase )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				a__	:							List[Any]      						= "detr.model." if is_panoptic else "model."
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith("detr" )
												    and not key.startswith("class_labels_classifier" )
												    and not key.startswith("bbox_predictor" )
												):
																a__	:							int      						= state_dict.pop(__UpperCamelCase )
																a__	:							Any      						= val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																a__	:							List[Any]      						= state_dict.pop(__UpperCamelCase )
																a__	:							Dict      						= val
												elif key.startswith("bbox_attention" ) or key.startswith("mask_head" ):
																continue
												else:
																a__	:							List[Any]      						= state_dict.pop(__UpperCamelCase )
																a__	:							Optional[Any]      						= val
								else:
												if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
																a__	:							Union[str, Any]      						= state_dict.pop(__UpperCamelCase )
																a__	:							Tuple      						= val

    # finally, create HuggingFace model and load state dict
				a__	:							Tuple      						= DetrForSegmentation(__UpperCamelCase ) if is_panoptic else DetrForObjectDetection(__UpperCamelCase )
				model.load_state_dict(__UpperCamelCase )
				model.eval()

				# verify our conversion on an image
				a__	:							Optional[int]      						= "coco_panoptic" if is_panoptic else "coco_detection"
				a__	:							int      						= DetrImageProcessor(format=__UpperCamelCase )

				a__	:							Optional[Any]      						= processor(images=prepare_img()	,				return_tensors="pt" )
				a__	:							str      						= encoding["pixel_values"]

				a__	:							List[str]      						= detr(__UpperCamelCase )
				a__	:							Tuple      						= model(__UpperCamelCase )

				assert torch.allclose(outputs.logits	,				original_outputs["pred_logits"]	,				atol=1e-3 )
				assert torch.allclose(outputs.pred_boxes	,				original_outputs["pred_boxes"]	,				atol=1e-3 )
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks	,				original_outputs["pred_masks"]	,				atol=1e-4 )
				print("Looks ok!" )

				if pytorch_dump_folder_path is not None:
								# Save model and image processor
								logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
								Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
								model.save_pretrained(__UpperCamelCase )
								processor.save_pretrained(__UpperCamelCase )

				if push_to_hub:
								# Upload model and image processor to the hub
								logger.info("Uploading PyTorch model and image processor to the hub..." )
								model.push_to_hub(F'nielsr/{model_name}' )
								processor.push_to_hub(F'nielsr/{model_name}' )


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()

						parser.add_argument(
						    """--model_name""",
						    default="""detr-resnet-50""",
						    type=str,
						    choices=["""detr-resnet-50""", """detr-resnet-101"""],
						    help="""Name of the DETR model you'd like to convert.""",
						)
						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 push the model to the hub or not.""")
						lowerCamelCase		       =      parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCamelCase		       =      logging.get_logger(__name__)


lowerCamelCase		       =      {
    """Salesforce/codegen-350M-nl""": """https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json""",
    """Salesforce/codegen-350M-multi""": """https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json""",
    """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json""",
    """Salesforce/codegen-2B-nl""": """https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json""",
    """Salesforce/codegen-2B-multi""": """https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json""",
    """Salesforce/codegen-2B-mono""": """https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json""",
    """Salesforce/codegen-6B-nl""": """https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json""",
    """Salesforce/codegen-6B-multi""": """https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json""",
    """Salesforce/codegen-6B-mono""": """https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json""",
    """Salesforce/codegen-16B-nl""": """https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json""",
    """Salesforce/codegen-16B-multi""": """https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json""",
    """Salesforce/codegen-16B-mono""": """https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json""",
}


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:str =       "codegen"
							A				:Tuple =       {
							    "max_position_embeddings": "n_positions",
							    "hidden_size": "n_embd",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}



							def __init__(							self ,					__UpperCAmelCase=5_0400 ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=4096 ,					__UpperCAmelCase=28 ,					__UpperCAmelCase=16 ,					__UpperCAmelCase=64 ,					__UpperCAmelCase=None ,					__UpperCAmelCase="gelu_new" ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=1E-5 ,					__UpperCAmelCase=0.0_2 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=5_0256 ,					__UpperCAmelCase=5_0256 ,					__UpperCAmelCase=False ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Dict      						= vocab_size
											a__	:							int      						= n_ctx
											a__	:							str      						= n_positions
											a__	:							Union[str, Any]      						= n_embd
											a__	:							Union[str, Any]      						= n_layer
											a__	:							Optional[int]      						= n_head
											a__	:							Dict      						= n_inner
											a__	:							Optional[Any]      						= rotary_dim
											a__	:							str      						= activation_function
											a__	:							List[Any]      						= resid_pdrop
											a__	:							str      						= embd_pdrop
											a__	:							List[str]      						= attn_pdrop
											a__	:							str      						= layer_norm_epsilon
											a__	:							Any      						= initializer_range
											a__	:							List[str]      						= use_cache

											a__	:							Tuple      						= bos_token_id
											a__	:							List[Any]      						= eos_token_id

											super().__init__(
											    bos_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					tie_word_embeddings=__UpperCAmelCase ,					**__UpperCAmelCase				)





class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = "default" ,					__UpperCAmelCase = None ,					__UpperCAmelCase = False ,					):







											"""simple docstring"""



											super().__init__(__UpperCAmelCase ,					task=__UpperCAmelCase ,					patching_specs=__UpperCAmelCase ,					use_past=__UpperCAmelCase				)
											if not getattr(self._config ,					"pad_token_id" ,					__UpperCAmelCase				):
															# TODO: how to do that better?
															a__	:							Union[str, Any]      						= 0



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
											if self.use_past:
															self.fill_with_past_key_values_(__UpperCAmelCase ,					direction="inputs"				)
															a__	:							Any      						= {0: "batch", 1: "past_sequence + sequence"}
											else:
															a__	:							List[Any]      						= {0: "batch", 1: "sequence"}

											return common_inputs



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self._config.n_layer



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self._config.n_head



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = -1 ,					__UpperCAmelCase = -1 ,					__UpperCAmelCase = False ,					__UpperCAmelCase = None ,					):







											"""simple docstring"""



											a__	:							Dict      						= super(__UpperCAmelCase ,					self				).generate_dummy_inputs(
											    __UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					seq_length=__UpperCAmelCase ,					is_pair=__UpperCAmelCase ,					framework=__UpperCAmelCase				)

											# We need to order the input in the way they appears in the forward()
											a__	:							Union[str, Any]      						= OrderedDict({"input_ids": common_inputs["input_ids"]}				)

											# Need to add the past_keys
											if self.use_past:
															if not is_torch_available():
																			raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
															else:
																			import torch

																			a__						,   a__	:							Union[str, Any]      						= common_inputs["input_ids"].shape
																			# Not using the same length for past_key_values
																			a__	:							Any      						= seqlen + 2
																			a__	:							str      						= (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			a__	:							str      						= [
																			    (torch.zeros(__UpperCAmelCase				), torch.zeros(__UpperCAmelCase				)) for _ in range(self.num_layers				)
																			]

											a__	:							Union[str, Any]      						= common_inputs["attention_mask"]
											if self.use_past:
															a__	:							str      						= ordered_inputs["attention_mask"].dtype
															a__	:							Union[str, Any]      						= torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(__UpperCAmelCase ,					__UpperCAmelCase ,					dtype=__UpperCAmelCase				)] ,					dim=1				)

											return ordered_inputs




							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return 13


