




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   :						Tuple				=			logging.get_logger(__name__)







def 		lowercase__   (		__A:	Tuple				):





					'''simple docstring'''






					if "resnet-50" in model_name:
										__magic_name__							:	int    							=							ResNetConfig.from_pretrained('''microsoft/resnet-50'''				)
					elif "resnet-101" in model_name:
										__magic_name__							:	Dict    							=							ResNetConfig.from_pretrained('''microsoft/resnet-101'''				)
					else:
										raise ValueError('''Model name should include either resnet50 or resnet101'''				)

					__magic_name__							:	Union[str, Any]    							=							DetrConfig(use_timm_backbone=__A							,backbone_config=__A				)

					# set label attributes
					__magic_name__							:	Tuple    							=							'''panoptic''' in model_name
					if is_panoptic:
										__magic_name__							:	Optional[Any]    							=							2_5_0
					else:
										__magic_name__							:	int    							=							9_1
										__magic_name__							:	List[Any]    							=							'''huggingface/label-files'''
										__magic_name__							:	Any    							=							'''coco-detection-id2label.json'''
										__magic_name__							:	Dict    							=							json.load(open(hf_hub_download(__A							,__A							,repo_type='''dataset'''				)							,'''r'''				)				)
										__magic_name__							:	int    							=							{int(__A				): v for k, v in idalabel.items()}
										__magic_name__							:	List[Any]    							=							idalabel
										__magic_name__							:	List[Any]    							=							{v: k for k, v in idalabel.items()}

					return config, is_panoptic







def 		lowercase__   (		__A:	int				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							[]

					# 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 		lowercase__   (		__A:	List[str]							,__A:	str							,__A:	Dict				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							state_dict.pop(__A				)
					__magic_name__							:	Tuple    							=							val







def 		lowercase__   (		__A:	List[str]							,__A:	Union[str, Any]=False				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							''''''
					if is_panoptic:
										__magic_name__							:	List[Any]    							=							'''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)
										__magic_name__							:	Dict    							=							state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
										__magic_name__							:	str    							=							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
										__magic_name__							:	Optional[int]    							=							in_proj_weight[:2_5_6, :]
										__magic_name__							:	List[Any]    							=							in_proj_bias[:2_5_6]
										__magic_name__							:	int    							=							in_proj_weight[2_5_6:5_1_2, :]
										__magic_name__							:	str    							=							in_proj_bias[2_5_6:5_1_2]
										__magic_name__							:	Dict    							=							in_proj_weight[-2_5_6:, :]
										__magic_name__							:	Tuple    							=							in_proj_bias[-2_5_6:]
					# 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
										__magic_name__							:	Dict    							=							state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''				)
										__magic_name__							:	List[Any]    							=							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
										__magic_name__							:	str    							=							in_proj_weight[:2_5_6, :]
										__magic_name__							:	Optional[Any]    							=							in_proj_bias[:2_5_6]
										__magic_name__							:	Optional[int]    							=							in_proj_weight[2_5_6:5_1_2, :]
										__magic_name__							:	str    							=							in_proj_bias[2_5_6:5_1_2]
										__magic_name__							:	Union[str, Any]    							=							in_proj_weight[-2_5_6:, :]
										__magic_name__							:	Dict    							=							in_proj_bias[-2_5_6:]
										# read in weights + bias of input projection layer of cross-attention
										__magic_name__							:	Optional[int]    							=							state_dict.pop(
										    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''				)
										__magic_name__							:	List[str]    							=							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
										__magic_name__							:	List[str]    							=							in_proj_weight_cross_attn[:2_5_6, :]
										__magic_name__							:	Optional[Any]    							=							in_proj_bias_cross_attn[:2_5_6]
										__magic_name__							:	Tuple    							=							in_proj_weight_cross_attn[2_5_6:5_1_2, :]
										__magic_name__							:	Tuple    							=							in_proj_bias_cross_attn[2_5_6:5_1_2]
										__magic_name__							:	List[Any]    							=							in_proj_weight_cross_attn[-2_5_6:, :]
										__magic_name__							:	List[str]    							=							in_proj_bias_cross_attn[-2_5_6:]







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__magic_name__							:	Dict    							=							Image.open(requests.get(__A							,stream=__A				).raw				)

					return im







@torch.no_grad()
def 		lowercase__   (		__A:	Optional[int]							,__A:	Optional[Any]=None							,__A:	Dict=False				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							get_detr_config(__A				)

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

    # finally, create HuggingFace model and load state dict
					__magic_name__							:	Any    							=							DetrForSegmentation(__A				) if is_panoptic else DetrForObjectDetection(__A				)
					model.load_state_dict(__A				)
					model.eval()

					# verify our conversion on an image
					__magic_name__							:	Optional[Any]    							=							'''coco_panoptic''' if is_panoptic else '''coco_detection'''
					__magic_name__							:	Optional[Any]    							=							DetrImageProcessor(format=__A				)

					__magic_name__							:	Tuple    							=							processor(images=prepare_img()							,return_tensors='''pt'''				)
					__magic_name__							:	Union[str, Any]    							=							encoding['''pixel_values''']

					__magic_name__							:	Tuple    							=							detr(__A				)
					__magic_name__							:	Optional[int]    							=							model(__A				)

					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(__A				).mkdir(exist_ok=__A				)
										model.save_pretrained(__A				)
										processor.save_pretrained(__A				)

					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   :						List[str]				=			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   :						List[str]				=			parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def 		lowercase__   (		__A:	str							,__A:	Optional[Any]							,__A:	List[Any]							,__A:	str							,__A:	List[str]=True							,__A:	List[Any]="pt"				):





					'''simple docstring'''






					__magic_name__							:	Optional[Any]    							=							{'''add_prefix_space''': True} if isinstance(__A							,__A				) and not line.startswith(''' '''				) else {}
					__magic_name__							:	List[str]    							=							padding_side
					return tokenizer(
					    [line]							,max_length=__A							,padding='''max_length''' if pad_to_max_length else None							,truncation=__A							,return_tensors=__A							,add_special_tokens=__A							,**__A							,)







def 		lowercase__   (		__A:	int							,__A:	int							,__A:	Optional[Any]=None							,):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							input_ids.ne(__A				).any(dim=0				)
					if attention_mask is None:
										return input_ids[:, keep_column_mask]
					else:
										return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[str]       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Any="train"       ,						lowerCamelCase_			:   Optional[int]=None       ,						lowerCamelCase_			:   List[Any]=None       ,						lowerCamelCase_			:   Optional[int]=None       ,						lowerCamelCase_			:   List[Any]=""       ,						)						->   Union[str, Any]:
								super().__init__()
								__magic_name__							:	Any    							=							Path(lowerCamelCase_  ).joinpath(type_path + '''.source'''  )
								__magic_name__							:	Tuple    							=							Path(lowerCamelCase_  ).joinpath(type_path + '''.target'''  )
								__magic_name__							:	str    							=							self.get_char_lens(self.src_file  )
								__magic_name__							:	Dict    							=							max_source_length
								__magic_name__							:	Any    							=							max_target_length
								assert min(self.src_lens  ) > 0, F'''found empty line in {self.src_file}'''
								__magic_name__							:	Union[str, Any]    							=							tokenizer
								__magic_name__							:	str    							=							prefix
								if n_obs is not None:
													__magic_name__							:	Optional[int]    							=							self.src_lens[:n_obs]
								__magic_name__							:	Optional[Any]    							=							src_lang
								__magic_name__							:	Tuple    							=							tgt_lang

			def __len__( self			:   Optional[int]  )						->   List[Any]:
								return len(self.src_lens  )

			def __getitem__( self			:   Optional[int]       ,						lowerCamelCase_			:   Optional[int]  )						->   Dict[str, torch.Tensor]:
								__magic_name__							:	Optional[int]    							=							index + 1  # linecache starts at 1
								__magic_name__							:	Optional[int]    							=							self.prefix + linecache.getline(str(self.src_file  )       ,						lowerCamelCase_  ).rstrip('''\n'''  )
								__magic_name__							:	Dict    							=							linecache.getline(str(self.tgt_file  )       ,						lowerCamelCase_  ).rstrip('''\n'''  )
								assert source_line, F'''empty source line for index {index}'''
								assert tgt_line, F'''empty tgt line for index {index}'''

								# Need to add eos token manually for T5
								if isinstance(self.tokenizer       ,						lowerCamelCase_  ):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								__magic_name__							:	int    							=							(
								    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,						lowerCamelCase_  ) else self.tokenizer
								)
								__magic_name__							:	Optional[Any]    							=							self.tokenizer.generator if isinstance(self.tokenizer       ,						lowerCamelCase_  ) else self.tokenizer

								__magic_name__							:	Tuple    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_source_length       ,						'''right'''  )
								__magic_name__							:	int    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_target_length       ,						'''right'''  )

								__magic_name__							:	Optional[Any]    							=							source_inputs['''input_ids'''].squeeze()
								__magic_name__							:	List[str]    							=							target_inputs['''input_ids'''].squeeze()
								__magic_name__							:	Optional[int]    							=							source_inputs['''attention_mask'''].squeeze()
								return {
								    "input_ids": source_ids,
								    "attention_mask": src_mask,
								    "decoder_input_ids": target_ids,
								}

			@staticmethod
			def 							UpperCAmelCase__       ( lowerCamelCase_			:   List[str]  )						->   Dict:
								return [len(lowerCamelCase_  ) for x in Path(lowerCamelCase_  ).open().readlines()]


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Optional[Any]  )						->   Dict[str, torch.Tensor]:
								__magic_name__							:	str    							=							torch.stack([x['''input_ids'''] for x in batch]  )
								__magic_name__							:	Any    							=							torch.stack([x['''attention_mask'''] for x in batch]  )
								__magic_name__							:	Tuple    							=							torch.stack([x['''decoder_input_ids'''] for x in batch]  )
								__magic_name__							:	Tuple    							=							(
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer       ,						lowerCamelCase_  )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__							:	Optional[Any]    							=							(
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer       ,						lowerCamelCase_  )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__							:	Dict    							=							trim_batch(lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							,    __magic_name__							:	Optional[int]    							=							trim_batch(lowerCamelCase_       ,						lowerCamelCase_       ,						attention_mask=lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							{
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch






__lowerCamelCase   :						Dict				=			getLogger(__name__)







def 		lowercase__   (		__A:	List[List]				):





					'''simple docstring'''






					return list(itertools.chain.from_iterable(__A				)				)







def 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							get_git_info()
					save_json(__A							,os.path.join(__A							,'''git_log.json'''				)				)







def 		lowercase__   (		__A:	Optional[int]							,__A:	Union[str, Any]							,__A:	Union[str, Any]=4							,**__A:	Optional[Any]				):





					'''simple docstring'''






					with open(__A							,'''w'''				) as f:
										json.dump(__A							,__A							,indent=__A							,**__A				)







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






					with open(__A				) as f:
										return json.load(__A				)







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	int    							=							git.Repo(search_parent_directories=__A				)
					__magic_name__							:	str    							=							{
					    '''repo_id''': str(__A				),
					    '''repo_sha''': str(repo.head.object.hexsha				),
					    '''repo_branch''': str(repo.active_branch				),
					    '''hostname''': str(socket.gethostname()				),
					}
					return repo_infos







def 		lowercase__   (		__A:	Callable							,__A:	Iterable				):





					'''simple docstring'''






					return list(map(__A							,__A				)				)







def 		lowercase__   (		__A:	int							,__A:	Dict				):





					'''simple docstring'''






					with open(__A							,'''wb'''				) as f:
										return pickle.dump(__A							,__A				)







def 		lowercase__   (		__A:	Any				):





					'''simple docstring'''






					def remove_articles(__A:	Any				):
										return re.sub(r'''\b(a|an|the)\b'''							,''' '''							,__A				)

					def white_space_fix(__A:	Optional[int]				):
										return " ".join(text.split()				)

					def remove_punc(__A:	Any				):
										__magic_name__							:	Union[str, Any]    							=							set(string.punctuation				)
										return "".join(ch for ch in text if ch not in exclude				)

					def lower(__A:	Optional[int]				):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(__A				)				)				)				)







def 		lowercase__   (		__A:	int							,__A:	Optional[Any]				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							normalize_answer(__A				).split()
					__magic_name__							:	Optional[int]    							=							normalize_answer(__A				).split()
					__magic_name__							:	Dict    							=							Counter(__A				) & Counter(__A				)
					__magic_name__							:	Tuple    							=							sum(common.values()				)
					if num_same == 0:
										return 0
					__magic_name__							:	int    							=							1.0 * num_same / len(__A				)
					__magic_name__							:	Tuple    							=							1.0 * num_same / len(__A				)
					__magic_name__							:	str    							=							(2 * precision * recall) / (precision + recall)
					return fa







def 		lowercase__   (		__A:	List[Any]							,__A:	Dict				):





					'''simple docstring'''






					return normalize_answer(__A				) == normalize_answer(__A				)







def 		lowercase__   (		__A:	List[str]							,__A:	List[str]				):





					'''simple docstring'''






					assert len(__A				) == len(__A				)
					__magic_name__							:	Optional[int]    							=							0
					for hypo, pred in zip(__A							,__A				):
										em += exact_match_score(__A							,__A				)
					if len(__A				) > 0:
										em /= len(__A				)
					return {"em": em}







def 		lowercase__   (		__A:	int				):





					'''simple docstring'''






					return model_prefix.startswith('''rag'''				)







def 		lowercase__   (		__A:	Optional[Any]							,__A:	List[str]							,__A:	Dict				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							{p: p for p in extra_params}
					# T5 models don't have `dropout` param, they have `dropout_rate` instead
					__magic_name__							:	Any    							=							'''dropout_rate'''
					for p in extra_params:
										if getattr(__A							,__A							,__A				):
															if not hasattr(__A							,__A				) and not hasattr(__A							,equivalent_param[p]				):
																				logger.info('''config doesn\'t have a `{}` attribute'''.format(__A				)				)
																				delattr(__A							,__A				)
																				continue
															__magic_name__							:	str    							=							p if hasattr(__A							,__A				) else equivalent_param[p]
															setattr(__A							,__A							,getattr(__A							,__A				)				)
															delattr(__A							,__A				)
					return hparams, config



