'''simple docstring'''

import argparse
import json
from pathlib import Path

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

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def 				UpperCamelCase__      (      a__      ,   a__=False      ,   a__=False   ):


						'''simple docstring'''






						_lowerCAmelCase					    ='backbone.' if is_semantic else ''

						_lowerCAmelCase					    =[]
						for i in range(config.num_hidden_layers   ):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')   )
												rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')   )
												rename_keys.append(
												    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')   )
												rename_keys.append(
												    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')   )
												rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')   )
												rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')   )
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')   )
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')   )
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')   )
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')   )

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        (F'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
						        (F'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
						        (F'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
						    ]   )

						if has_lm_head:
												# mask token + shared relative position bias + layernorm
												rename_keys.extend(
												    [
												        ('mask_token', 'beit.embeddings.mask_token'),
												        (
												            'rel_pos_bias.relative_position_bias_table',
												            'beit.encoder.relative_position_bias.relative_position_bias_table',
												        ),
												        (
												            'rel_pos_bias.relative_position_index',
												            'beit.encoder.relative_position_bias.relative_position_index',
												        ),
												        ('norm.weight', 'layernorm.weight'),
												        ('norm.bias', 'layernorm.bias'),
												    ]   )
						elif is_semantic:
												# semantic segmentation classification heads
												rename_keys.extend(
												    [
												        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
												        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
												        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
												        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
												    ]   )
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
												        ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
												        ('head.weight', 'classifier.weight'),
												        ('head.bias', 'classifier.bias'),
												    ]   )

						return rename_keys



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__=False      ,   a__=False   ):


						'''simple docstring'''






						for i in range(config.num_hidden_layers   ):
												_lowerCAmelCase					    ='backbone.' if is_semantic else ''
												# queries, keys and values
												_lowerCAmelCase					    =state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''   )
												_lowerCAmelCase					    =state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''   )
												_lowerCAmelCase					    =state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''   )

												_lowerCAmelCase					    =in_proj_weight[
												    : config.hidden_size, :
												]
												_lowerCAmelCase					    =q_bias
												_lowerCAmelCase					    =in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_lowerCAmelCase					    =in_proj_weight[
												    -config.hidden_size :, :
												]
												_lowerCAmelCase					    =v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												_lowerCAmelCase					    =state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''   )
												_lowerCAmelCase					    =state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''   )

												_lowerCAmelCase					    =gamma_a
												_lowerCAmelCase					    =gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		_lowerCAmelCase					    =state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''   )
																		_lowerCAmelCase					    =state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''   )

																		_lowerCAmelCase					    =table
																		_lowerCAmelCase					    =index



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__   ):


						'''simple docstring'''






						_lowerCAmelCase					    =dct.pop(a__   )
						_lowerCAmelCase					    =val



def 				UpperCamelCase__      (      ):


						'''simple docstring'''






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



@torch.no_grad()
def 				UpperCamelCase__      (      a__      ,   a__   ):


						'''simple docstring'''






						_lowerCAmelCase					    =BeitConfig()
						_lowerCAmelCase					    =False
						_lowerCAmelCase					    =False
						_lowerCAmelCase					    ='huggingface/label-files'
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												_lowerCAmelCase					    =True
												_lowerCAmelCase					    =True
												_lowerCAmelCase					    =True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												_lowerCAmelCase					    =True
												_lowerCAmelCase					    =2_1_8_4_1
												_lowerCAmelCase					    ='imagenet-22k-id2label.json'
												_lowerCAmelCase					    =json.load(open(hf_hub_download(a__      ,   a__      ,   repo_type='dataset'   )      ,   'r'   )   )
												_lowerCAmelCase					    ={int(a__   ): v for k, v in idalabel.items()}
												# this dataset contains 21843 labels but the model only has 21841
												# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
												del idalabel[9_2_0_5]
												del idalabel[1_5_0_2_7]
												_lowerCAmelCase					    =idalabel
												_lowerCAmelCase					    ={v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												_lowerCAmelCase					    =True
												_lowerCAmelCase					    =1_0_0_0
												_lowerCAmelCase					    ='imagenet-1k-id2label.json'
												_lowerCAmelCase					    =json.load(open(hf_hub_download(a__      ,   a__      ,   repo_type='dataset'   )      ,   'r'   )   )
												_lowerCAmelCase					    ={int(a__   ): v for k, v in idalabel.items()}
												_lowerCAmelCase					    =idalabel
												_lowerCAmelCase					    ={v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		_lowerCAmelCase					    =3_8_4
												if "512" in checkpoint_url:
																		_lowerCAmelCase					    =5_1_2
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												_lowerCAmelCase					    =True
												_lowerCAmelCase					    =1_5_0
												_lowerCAmelCase					    ='ade20k-id2label.json'
												_lowerCAmelCase					    =json.load(open(hf_hub_download(a__      ,   a__      ,   repo_type='dataset'   )      ,   'r'   )   )
												_lowerCAmelCase					    ={int(a__   ): v for k, v in idalabel.items()}
												_lowerCAmelCase					    =idalabel
												_lowerCAmelCase					    ={v: k for k, v in idalabel.items()}
												_lowerCAmelCase					    =6_4_0
												_lowerCAmelCase					    =True
						else:
												raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''   )

						# size of the architecture
						if "base" in checkpoint_url:
												pass
						elif "large" in checkpoint_url:
												_lowerCAmelCase					    =1_0_2_4
												_lowerCAmelCase					    =4_0_9_6
												_lowerCAmelCase					    =2_4
												_lowerCAmelCase					    =1_6
												if "ade20k" in checkpoint_url:
																		_lowerCAmelCase					    =6_4_0
																		_lowerCAmelCase					    =[7, 1_1, 1_5, 2_3]
						else:
												raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'   )

						# load state_dict of original model, remove and rename some keys
						_lowerCAmelCase					    =torch.hub.load_state_dict_from_url(a__      ,   map_location='cpu'      ,   check_hash=a__   )
						_lowerCAmelCase					    =state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

						_lowerCAmelCase					    =create_rename_keys(a__      ,   has_lm_head=a__      ,   is_semantic=a__   )
						for src, dest in rename_keys:
												rename_key(a__      ,   a__      ,   a__   )
						read_in_q_k_v(a__      ,   a__      ,   has_lm_head=a__      ,   is_semantic=a__   )
						if is_semantic:
												# add prefix to decoder keys
												for key, val in state_dict.copy().items():
																		_lowerCAmelCase					    =state_dict.pop(a__   )
																		if key.startswith('backbone.fpn'   ):
																								_lowerCAmelCase					    =key.replace('backbone.fpn'      ,   'fpn'   )
																		_lowerCAmelCase					    =val

    # load HuggingFace model
						if checkpoint_url[-9:-4] == "pt22k":
												_lowerCAmelCase					    =BeitForMaskedImageModeling(a__   )
						elif "ade20k" in checkpoint_url:
												_lowerCAmelCase					    =BeitForSemanticSegmentation(a__   )
						else:
												_lowerCAmelCase					    =BeitForImageClassification(a__   )
						model.eval()
						model.load_state_dict(a__   )

						# Check outputs on an image
						if is_semantic:
												_lowerCAmelCase					    =BeitImageProcessor(size=config.image_size      ,   do_center_crop=a__   )
												_lowerCAmelCase					    =load_dataset('hf-internal-testing/fixtures_ade20k'      ,   split='test'   )
												_lowerCAmelCase					    =Image.open(ds[0]['file']   )
						else:
												_lowerCAmelCase					    =BeitImageProcessor(
												    size=config.image_size      ,   resample=PILImageResampling.BILINEAR      ,   do_center_crop=a__   )
												_lowerCAmelCase					    =prepare_img()

						_lowerCAmelCase					    =image_processor(images=a__      ,   return_tensors='pt'   )
						_lowerCAmelCase					    =encoding['pixel_values']

						_lowerCAmelCase					    =model(a__   )
						_lowerCAmelCase					    =outputs.logits

						# verify logits
						_lowerCAmelCase					    =torch.Size([1, 1_0_0_0]   )
						if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'   ):
												_lowerCAmelCase					    =torch.Size([1, 1_9_6, 8_1_9_2]   )
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'   ):
												_lowerCAmelCase					    =torch.Size([1, 1_9_6, 8_1_9_2]   )
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'   ):
												_lowerCAmelCase					    =torch.Size([1, 2_1_8_4_1]   )
												_lowerCAmelCase					    =torch.tensor([2.2_288, 2.4_671, 0.7_395]   )
												_lowerCAmelCase					    =2_3_9_7
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'   ):
												_lowerCAmelCase					    =torch.Size([1, 2_1_8_4_1]   )
												_lowerCAmelCase					    =torch.tensor([1.6_881, -0.2_787, 0.5_901]   )
												_lowerCAmelCase					    =2_3_9_6
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'   ):
												_lowerCAmelCase					    =torch.tensor([0.1_241, 0.0_798, -0.6_569]   )
												_lowerCAmelCase					    =2_8_5
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'   ):
												_lowerCAmelCase					    =torch.tensor([-1.2_385, -1.0_987, -1.0_108]   )
												_lowerCAmelCase					    =2_8_1
						elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'   ):
												_lowerCAmelCase					    =torch.tensor([-1.5_303, -0.9_484, -0.3_147]   )
												_lowerCAmelCase					    =7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'   ):
												_lowerCAmelCase					    =torch.tensor([0.4_610, -0.0_928, 0.2_086]   )
												_lowerCAmelCase					    =7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'   ):
												_lowerCAmelCase					    =torch.tensor([-0.4_804, 0.6_257, -0.1_837]   )
												_lowerCAmelCase					    =7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'   ):
												_lowerCAmelCase					    =torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]   )
												_lowerCAmelCase					    =7_6_1
						elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'   ):
												_lowerCAmelCase					    =torch.tensor([-0.3_062, 0.7_261, 0.4_852]   )
												_lowerCAmelCase					    =7_6_1
						elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'   ):
												_lowerCAmelCase					    =(1, 1_5_0, 1_6_0, 1_6_0)
												_lowerCAmelCase					    =torch.tensor(
												    [
												        [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
												        [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
												        [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
												    ]   )
						elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'   ):
												_lowerCAmelCase					    =(1, 1_5_0, 1_6_0, 1_6_0)
												_lowerCAmelCase					    =torch.tensor(
												    [
												        [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
												        [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
												        [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
												    ]   )
						else:
												raise ValueError('Can\'t verify logits as model is not supported'   )

						assert logits.shape == expected_shape, "Shape of logits not as expected"
						if not has_lm_head:
												if is_semantic:
																		assert torch.allclose(
																		    logits[0, :3, :3, :3]      ,   a__      ,   atol=1E-3   ), "First elements of logits not as expected"
												else:
																		print('Predicted class idx:'      ,   logits.argmax(-1   ).item()   )
																		assert torch.allclose(
																		    logits[0, :3]      ,   a__      ,   atol=1E-3   ), "First elements of logits not as expected"
																		assert logits.argmax(-1   ).item() == expected_class_idx, "Predicted class index not as expected"

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


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

							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
							    type=str,
							    help='''URL 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.'''
							)
							lowercase_  		=			parser.parse_args()
							convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










import argparse
import json
import re
import string
import sys
from collections import Counter


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					def remove_articles(snake_case     ):
										return re.sub(R"""\b(a|an|the)\b"""      ,		""" """      ,		snake_case     )

					def white_space_fix(snake_case     ):
										return " ".join(text.split()     )

					def remove_punc(snake_case     ):
										_lowerCAmelCase         =       set(string.punctuation     )
										return "".join(ch for ch in text if ch not in exclude     )

					def lower(snake_case     ):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(snake_case     )     )     )     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       normalize_answer(snake_case     ).split()
					_lowerCAmelCase         =       normalize_answer(snake_case     ).split()
					_lowerCAmelCase         =       Counter(snake_case     ) & Counter(snake_case     )
					_lowerCAmelCase         =       sum(common.values()     )
					if num_same == 0:
										return 0
					_lowerCAmelCase         =       1.0 * num_same / len(snake_case     )
					_lowerCAmelCase         =       1.0 * num_same / len(snake_case     )
					_lowerCAmelCase         =       (2 * precision * recall) / (precision + recall)
					return fa


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					return normalize_answer(snake_case     ) == normalize_answer(snake_case     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       []
					for ground_truth in ground_truths:
										_lowerCAmelCase         =       metric_fn(snake_case      ,		snake_case     )
										scores_for_ground_truths.append(snake_case     )
					return max(snake_case     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       _lowerCAmelCase         =       _lowerCAmelCase         =       0
					for article in dataset:
										for paragraph in article["paragraphs"]:
															for qa in paragraph["qas"]:
																				total += 1
																				if qa["id"] not in predictions:
																									_lowerCAmelCase         =       """Unanswered question """ + qa["""id"""] + """ will receive score 0."""
																									print(snake_case      ,		file=sys.stderr     )
																									continue
																				_lowerCAmelCase         =       [x["""text"""] for x in qa["""answers"""]]
																				_lowerCAmelCase         =       predictions[qa["""id"""]]
																				exact_match += metric_max_over_ground_truths(snake_case      ,		snake_case      ,		snake_case     )
																				fa += metric_max_over_ground_truths(snake_case      ,		snake_case      ,		snake_case     )

					_lowerCAmelCase         =       100.0 * exact_match / total
					_lowerCAmelCase         =       100.0 * fa / total

					return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
	A__       =			"""1.1"""
	A__       =			argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
	parser.add_argument("""dataset_file""", help="""Dataset file""")
	parser.add_argument("""prediction_file""", help="""Prediction File""")
	A__       =			parser.parse_args()
	with open(args.dataset_file) as dataset_file:
		A__       =			json.load(dataset_file)
		if dataset_json["version"] != expected_version:
			print(
			    """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
			    file=sys.stderr,
			)
		A__       =			dataset_json["""data"""]
	with open(args.prediction_file) as prediction_file:
		A__       =			json.load(prediction_file)
	print(json.dumps(evaluate(dataset, predictions)))



