



import os

import jsonlines
import numpy as np
from tqdm import tqdm


snake_case__					:     Dict    		=   20_48
snake_case__					:     str    		=   40_96
snake_case__					:     List[str]    		=   42
snake_case__					:     Dict    		=   os.environ.pop("""PROCESS_TRAIN""", """false""")
snake_case__					:     Optional[Any]    		=   {"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	def choose_first(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
		assert isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		if len(_SCREAMING_SNAKE_CASE   ) == 1:
			__lowercase		       =			answer[0]
			return {k: [answer[k]] for k in answer} if is_long_answer else answer
		for a in answer:
			if is_long_answer:
				__lowercase		       =			{k: [a[k]] for k in a}
			if len(a["start_token"]   ) > 0:
				break
		return a

	__lowercase		       =			{"id": example["id"]}
	__lowercase		       =			example["annotations"]
	__lowercase		       =			annotation["yes_no_answer"]
	if 0 in yes_no_answer or 1 in yes_no_answer:
		__lowercase		       =			["yes"] if 1 in yes_no_answer else ["no"]
		__lowercase		       =			__lowercase		       =			[]
		__lowercase		       =			__lowercase		       =			[]
		__lowercase		       =			["<cls>"]
	else:
		__lowercase		       =			["short"]
		__lowercase		       =			choose_first(annotation["short_answers"]   )
		if len(out["start_token"]   ) == 0:
			# answer will be long if short is not available
			__lowercase		       =			["long"]
			__lowercase		       =			choose_first(annotation["long_answer"]  ,	is_long_answer=_SCREAMING_SNAKE_CASE   )
			__lowercase		       =			[]
		answer.update(_SCREAMING_SNAKE_CASE   )

	# disregard some samples
	if len(answer["start_token"]   ) > 1 or answer["start_token"] == answer["end_token"]:
		__lowercase		       =			True
	else:
		__lowercase		       =			False

	__lowercase		       =			["start_token", "end_token", "start_byte", "end_byte", "text"]
	if not all(isinstance(answer[k]  ,	_SCREAMING_SNAKE_CASE   ) for k in cols   ):
		raise ValueError("Issue in ID"  ,	example["id"]   )

	return answer



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			_get_single_answer(_SCREAMING_SNAKE_CASE   )
	# bytes are of no use
	del answer["start_byte"]
	del answer["end_byte"]

	# handle yes_no answers explicitly
	if answer["category"][0] in ["yes", "no"]:  # category is list with one element
		__lowercase		       =			example["document"]["tokens"]
		__lowercase		       =			[]
		for i in range(len(doc["token"]   )   ):
			if not doc["is_html"][i]:
				context.append(doc["token"][i]   )
		return {
		    "context": " ".join(_SCREAMING_SNAKE_CASE   ),
		    "answer": {
		        "start_token": -1_0_0,  # ignore index in cross-entropy
		        "end_token": -1_0_0,  # ignore index in cross-entropy
		        "category": answer["category"],
		        "span": answer["category"],  # extra
		    },
		}

	# later, help in removing all no answers
	if answer["start_token"] == [-1]:
		return {
		    "context": "None",
		    "answer": {
		        "start_token": -1,
		        "end_token": -1,
		        "category": "null",
		        "span": "None",  # extra
		    },
		}

	# handling normal samples

	__lowercase		       =			["start_token", "end_token"]
	answer.update({k: answer[k][0] if len(answer[k]   ) > 0 else answer[k] for k in cols}   )  # e.g. [10] == 10

	__lowercase		       =			example["document"]["tokens"]
	__lowercase		       =			answer["start_token"]
	__lowercase		       =			answer["end_token"]

	__lowercase		       =			[]
	for i in range(len(doc["token"]   )   ):
		if not doc["is_html"][i]:
			context.append(doc["token"][i]   )
		else:
			if answer["start_token"] > i:
				start_token -= 1
			if answer["end_token"] > i:
				end_token -= 1
	__lowercase		       =			" ".join(context[start_token:end_token]   )

	# checking above code
	if assertion:
		__lowercase		       =			doc["is_html"][answer["start_token"] : answer["end_token"]]
		__lowercase		       =			doc["token"][answer["start_token"] : answer["end_token"]]
		__lowercase		       =			" ".join([old[i] for i in range(len(_SCREAMING_SNAKE_CASE   )   ) if not is_html[i]]   )
		if new != old:
			print("ID:"  ,	example["id"]   )
			print("New:"  ,	_SCREAMING_SNAKE_CASE  ,	end="\n"   )
			print("Old:"  ,	_SCREAMING_SNAKE_CASE  ,	end="\n\n"   )

	return {
	    "context": " ".join(_SCREAMING_SNAKE_CASE   ),
	    "answer": {
	        "start_token": start_token,
	        "end_token": end_token - 1,  # this makes it inclusive
	        "category": answer["category"],  # either long or short
	        "span": new,  # extra
	    },
	}



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=2_0_4_8  ,	_SCREAMING_SNAKE_CASE=4_0_9_6  ,	_SCREAMING_SNAKE_CASE=True   ):
	# overlap will be of doc_stride - q_len

	__lowercase		       =			get_context_and_ans(_SCREAMING_SNAKE_CASE  ,	assertion=_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			out["answer"]

	# later, removing these samples
	if answer["start_token"] == -1:
		return {
		    "example_id": example["id"],
		    "input_ids": [[-1]],
		    "labels": {
		        "start_token": [-1],
		        "end_token": [-1],
		        "category": ["null"],
		    },
		}

	__lowercase		       =			tokenizer(example["question"]["text"]  ,	out["context"]   ).input_ids
	__lowercase		       =			input_ids.index(tokenizer.sep_token_id   ) + 1

	# return yes/no
	if answer["category"][0] in ["yes", "no"]:  # category is list with one element
		__lowercase		       =			[]
		__lowercase		       =			[]
		__lowercase		       =			input_ids[:q_len]
		__lowercase		       =			range(_SCREAMING_SNAKE_CASE  ,	len(_SCREAMING_SNAKE_CASE   )  ,	max_length - doc_stride   )
		for i in doc_start_indices:
			__lowercase		       =			i + max_length - q_len
			__lowercase		       =			input_ids[i:end_index]
			inputs.append(q_indices + slice   )
			category.append(answer["category"][0]   )
			if slice[-1] == tokenizer.sep_token_id:
				break

		return {
		    "example_id": example["id"],
		    "input_ids": inputs,
		    "labels": {
		        "start_token": [-1_0_0] * len(_SCREAMING_SNAKE_CASE   ),
		        "end_token": [-1_0_0] * len(_SCREAMING_SNAKE_CASE   ),
		        "category": category,
		    },
		}

	__lowercase		       =			out["context"].split()
	__lowercase		       =			splitted_context[answer["end_token"]]
	__lowercase		       =			len(
	    tokenizer(
	        " ".join(splitted_context[: answer["start_token"]]   )  ,	add_special_tokens=_SCREAMING_SNAKE_CASE  ,	).input_ids   )
	__lowercase		       =			len(
	    tokenizer(" ".join(splitted_context[: answer["end_token"]]   )  ,	add_special_tokens=_SCREAMING_SNAKE_CASE   ).input_ids   )

	answer["start_token"] += q_len
	answer["end_token"] += q_len

	# fixing end token
	__lowercase		       =			len(tokenizer(_SCREAMING_SNAKE_CASE  ,	add_special_tokens=_SCREAMING_SNAKE_CASE   ).input_ids   )
	if num_sub_tokens > 1:
		answer["end_token"] += num_sub_tokens - 1

	__lowercase		       =			input_ids[answer["start_token"] : answer["end_token"] + 1]  # right & left are inclusive
	__lowercase		       =			answer["start_token"]
	__lowercase		       =			answer["end_token"]

	if assertion:
		__lowercase		       =			tokenizer.decode(_SCREAMING_SNAKE_CASE   )
		if answer["span"] != new:
			print("ISSUE IN TOKENIZATION"   )
			print("OLD:"  ,	answer["span"]   )
			print("NEW:"  ,	_SCREAMING_SNAKE_CASE  ,	end="\n\n"   )

	if len(_SCREAMING_SNAKE_CASE   ) <= max_length:
		return {
		    "example_id": example["id"],
		    "input_ids": [input_ids],
		    "labels": {
		        "start_token": [answer["start_token"]],
		        "end_token": [answer["end_token"]],
		        "category": answer["category"],
		    },
		}

	__lowercase		       =			input_ids[:q_len]
	__lowercase		       =			range(_SCREAMING_SNAKE_CASE  ,	len(_SCREAMING_SNAKE_CASE   )  ,	max_length - doc_stride   )

	__lowercase		       =			[]
	__lowercase		       =			[]
	__lowercase		       =			[]
	__lowercase		       =			[]  # null, yes, no, long, short
	for i in doc_start_indices:
		__lowercase		       =			i + max_length - q_len
		__lowercase		       =			input_ids[i:end_index]
		inputs.append(q_indices + slice   )
		assert len(inputs[-1]   ) <= max_length, "Issue in truncating length"

		if start_token >= i and end_token <= end_index - 1:
			__lowercase		       =			start_token - i + q_len
			__lowercase		       =			end_token - i + q_len
			answers_category.append(answer["category"][0]   )  # ["short"] -> "short"
		else:
			__lowercase		       =			-1_0_0
			__lowercase		       =			-1_0_0
			answers_category.append("null"   )
		__lowercase		       =			inputs[-1][start_token : end_token + 1]

		answers_start_token.append(_SCREAMING_SNAKE_CASE   )
		answers_end_token.append(_SCREAMING_SNAKE_CASE   )
		if assertion:
			if new != old and new != [tokenizer.cls_token_id]:
				print("ISSUE in strided for ID:"  ,	example["id"]   )
				print("New:"  ,	tokenizer.decode(_SCREAMING_SNAKE_CASE   )   )
				print("Old:"  ,	tokenizer.decode(_SCREAMING_SNAKE_CASE   )  ,	end="\n\n"   )
		if slice[-1] == tokenizer.sep_token_id:
			break

	return {
	    "example_id": example["id"],
	    "input_ids": inputs,
	    "labels": {
	        "start_token": answers_start_token,
	        "end_token": answers_end_token,
	        "category": answers_category,
	    },
	}



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=2_0_4_8  ,	_SCREAMING_SNAKE_CASE=4_0_9_6  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			get_strided_contexts_and_ans(
	    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	doc_stride=_SCREAMING_SNAKE_CASE  ,	max_length=_SCREAMING_SNAKE_CASE  ,	assertion=_SCREAMING_SNAKE_CASE  ,	)

	return example



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	with jsonlines.open(_SCREAMING_SNAKE_CASE  ,	"a"   ) as writer:
		for example in tqdm(_SCREAMING_SNAKE_CASE  ,	total=len(_SCREAMING_SNAKE_CASE   )  ,	desc="Saving samples ... "   ):
			__lowercase		       =			example["labels"]
			for ids, start, end, cat in zip(
			    example["input_ids"]  ,	labels["start_token"]  ,	labels["end_token"]  ,	labels["category"]  ,	):
				if start == -1 and end == -1:
					continue  # leave waste samples with no answer
				if cat == "null" and np.random.rand() < 0.6:
					continue  # removing 50 % samples
				writer.write(
				    {
				        "input_ids": ids,
				        "start_token": start,
				        "end_token": end,
				        "category": CATEGORY_MAPPING[cat],
				    }   )


if __name__ == "__main__":
	from datasets import load_dataset

	from transformers import BigBirdTokenizer

	snake_case__					:     Union[str, Any]    		=   load_dataset("""natural_questions""")
	snake_case__					:     Union[str, Any]    		=   BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

	snake_case__					:     List[Any]    		=   data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

	snake_case__					:     List[str]    		=   {
	    """tokenizer""": tokenizer,
	    """doc_stride""": DOC_STRIDE,
	    """max_length""": MAX_LENGTH,
	    """assertion""": False,
	}
	snake_case__					:     str    		=   data.map(prepare_inputs, fn_kwargs=fn_kwargs)
	snake_case__					:     Dict    		=   data.remove_columns(["""annotations""", """document""", """id""", """question"""])
	print(data)

	np.random.seed(SEED)
	snake_case__					:     str    		=   """nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
	save_to_disk(data, file_name=cache_file_name)








import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			SwinvaConfig()
	__lowercase		       =			swinva_name.split("_"   )

	__lowercase		       =			name_split[1]
	if "to" in name_split[3]:
		__lowercase		       =			int(name_split[3][-3:]   )
	else:
		__lowercase		       =			int(name_split[3]   )
	if "to" in name_split[2]:
		__lowercase		       =			int(name_split[2][-2:]   )
	else:
		__lowercase		       =			int(name_split[2][6:]   )

	if model_size == "tiny":
		__lowercase		       =			9_6
		__lowercase		       =			(2, 2, 6, 2)
		__lowercase		       =			(3, 6, 1_2, 2_4)
	elif model_size == "small":
		__lowercase		       =			9_6
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(3, 6, 1_2, 2_4)
	elif model_size == "base":
		__lowercase		       =			1_2_8
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(4, 8, 1_6, 3_2)
	else:
		__lowercase		       =			1_9_2
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(6, 1_2, 2_4, 4_8)

	if "to" in swinva_name:
		__lowercase		       =			(1_2, 1_2, 1_2, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		__lowercase		       =			2_1_8_4_1
		__lowercase		       =			"huggingface/label-files"
		__lowercase		       =			"imagenet-22k-id2label.json"
		__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
		__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
		__lowercase		       =			idalabel
		__lowercase		       =			{v: k for k, v in idalabel.items()}

	else:
		__lowercase		       =			1_0_0_0
		__lowercase		       =			"huggingface/label-files"
		__lowercase		       =			"imagenet-1k-id2label.json"
		__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
		__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
		__lowercase		       =			idalabel
		__lowercase		       =			{v: k for k, v in idalabel.items()}

	__lowercase		       =			img_size
	__lowercase		       =			num_classes
	__lowercase		       =			embed_dim
	__lowercase		       =			depths
	__lowercase		       =			num_heads
	__lowercase		       =			window_size

	return config



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	if "patch_embed.proj" in name:
		__lowercase		       =			name.replace("patch_embed.proj"  ,	"embeddings.patch_embeddings.projection"   )
	if "patch_embed.norm" in name:
		__lowercase		       =			name.replace("patch_embed.norm"  ,	"embeddings.norm"   )
	if "layers" in name:
		__lowercase		       =			"encoder." + name
	if "attn.proj" in name:
		__lowercase		       =			name.replace("attn.proj"  ,	"attention.output.dense"   )
	if "attn" in name:
		__lowercase		       =			name.replace("attn"  ,	"attention.self"   )
	if "norm1" in name:
		__lowercase		       =			name.replace("norm1"  ,	"layernorm_before"   )
	if "norm2" in name:
		__lowercase		       =			name.replace("norm2"  ,	"layernorm_after"   )
	if "mlp.fc1" in name:
		__lowercase		       =			name.replace("mlp.fc1"  ,	"intermediate.dense"   )
	if "mlp.fc2" in name:
		__lowercase		       =			name.replace("mlp.fc2"  ,	"output.dense"   )
	if "q_bias" in name:
		__lowercase		       =			name.replace("q_bias"  ,	"query.bias"   )
	if "k_bias" in name:
		__lowercase		       =			name.replace("k_bias"  ,	"key.bias"   )
	if "v_bias" in name:
		__lowercase		       =			name.replace("v_bias"  ,	"value.bias"   )
	if "cpb_mlp" in name:
		__lowercase		       =			name.replace("cpb_mlp"  ,	"continuous_position_bias_mlp"   )
	if name == "norm.weight":
		__lowercase		       =			"layernorm.weight"
	if name == "norm.bias":
		__lowercase		       =			"layernorm.bias"

	if "head" in name:
		__lowercase		       =			name.replace("head"  ,	"classifier"   )
	else:
		__lowercase		       =			"swinv2." + name

	return name



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	for key in orig_state_dict.copy().keys():
		__lowercase		       =			orig_state_dict.pop(_SCREAMING_SNAKE_CASE   )

		if "mask" in key:
			continue
		elif "qkv" in key:
			__lowercase		       =			key.split("."   )
			__lowercase		       =			int(key_split[1]   )
			__lowercase		       =			int(key_split[3]   )
			__lowercase		       =			model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				__lowercase		       =			val[:dim, :]
				__lowercase		       =			val[dim : dim * 2, :]
				__lowercase		       =			val[-dim:, :]
			else:
				__lowercase		       =			val[:dim]
				__lowercase		       =			val[
				    dim : dim * 2
				]
				__lowercase		       =			val[-dim:]
		else:
			__lowercase		       =			val

	return orig_state_dict



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			timm.create_model(_SCREAMING_SNAKE_CASE  ,	pretrained=_SCREAMING_SNAKE_CASE   )
	timm_model.eval()

	__lowercase		       =			get_swinva_config(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			SwinvaForImageClassification(_SCREAMING_SNAKE_CASE   )
	model.eval()

	__lowercase		       =			convert_state_dict(timm_model.state_dict()  ,	_SCREAMING_SNAKE_CASE   )
	model.load_state_dict(_SCREAMING_SNAKE_CASE   )

	__lowercase		       =			"http://images.cocodataset.org/val2017/000000039769.jpg"

	__lowercase		       =			AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"  ,	"-"   )   )   )
	__lowercase		       =			Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE   ).raw   )
	__lowercase		       =			image_processor(images=_SCREAMING_SNAKE_CASE  ,	return_tensors="pt"   )

	__lowercase		       =			timm_model(inputs["pixel_values"]   )
	__lowercase		       =			model(**_SCREAMING_SNAKE_CASE   ).logits

	assert torch.allclose(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	atol=1E-3   )

	print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(_SCREAMING_SNAKE_CASE   )

	print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
	image_processor.save_pretrained(_SCREAMING_SNAKE_CASE   )

	model.push_to_hub(
	    repo_path_or_name=Path(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	organization="nandwalritik"  ,	commit_message="Add model"  ,	)


if __name__ == "__main__":
	snake_case__					:     Union[str, Any]    		=   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--swinv2_name""",
	    default="""swinv2_tiny_patch4_window8_256""",
	    type=str,
	    help="""Name of the Swinv2 timm model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)

	snake_case__					:     str    		=   parser.parse_args()
	convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)



