
"""simple docstring"""

import os

import jsonlines
import numpy as np
from tqdm import tqdm


_A						=			2_0_4_8
_A						=			4_0_9_6
_A						=			4_2
_A						=			os.environ.pop('PROCESS_TRAIN', 'false')
_A						=			{'null': 0, 'short': 1, 'long': 2, 'yes': 3, 'no': 4}
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		def choose_first(a_,						a_=False      ):
				assert isinstance(a_,						a_      )
				if len(a_      ) == 1:
						lowerCamelCase					:      Dict          =   answer[0]
						return {k: [answer[k]] for k in answer} if is_long_answer else answer
				for a in answer:
						if is_long_answer:
								lowerCamelCase					:      Optional[Any]          =   {k: [a[k]] for k in a}
						if len(a['start_token']      ) > 0:
								break
				return a

		lowerCamelCase					:      str          =   {'id': example['id']}
		lowerCamelCase					:      Dict          =   example['annotations']
		lowerCamelCase					:      int          =   annotation['yes_no_answer']
		if 0 in yes_no_answer or 1 in yes_no_answer:
				lowerCamelCase					:      Union[str, Any]          =   ['yes'] if 1 in yes_no_answer else ['no']
				lowerCamelCase					:      Tuple          =   []
				lowerCamelCase					:      Dict          =   []
				lowerCamelCase					:      int          =   ['<cls>']
		else:
				lowerCamelCase					:      Tuple          =   ['short']
				lowerCamelCase					:      List[str]          =   choose_first(annotation['short_answers']      )
				if len(out['start_token']      ) == 0:
						# answer will be long if short is not available
						lowerCamelCase					:      Tuple          =   ['long']
						lowerCamelCase					:      Optional[int]          =   choose_first(annotation['long_answer'],						is_long_answer=a_      )
						lowerCamelCase					:      Dict          =   []
				answer.update(a_      )

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

		lowerCamelCase					:      int          =   ['start_token', 'end_token', 'start_byte', 'end_byte', 'text']
		if not all(isinstance(answer[k],						a_      ) for k in cols      ):
				raise ValueError('Issue in ID',						example['id']      )

		return answer
def 		UpperCAmelCase (		a_,						a_=False      ):





		'''simple docstring'''


		lowerCamelCase					:      Optional[Any]          =   _get_single_answer(a_      )
		# 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
				lowerCamelCase					:      Union[str, Any]          =   example['document']['tokens']
				lowerCamelCase					:      Any          =   []
				for i in range(len(doc['token']      )      ):
						if not doc["is_html"][i]:
								context.append(doc['token'][i]      )
				return {
				    "context": " ".join(a_      ),
				    "answer": {
				        "start_token": -100,  # ignore index in cross-entropy
				        "end_token": -100,  # 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

		lowerCamelCase					:      Dict          =   ['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

		lowerCamelCase					:      List[str]          =   example['document']['tokens']
		lowerCamelCase					:      Tuple          =   answer['start_token']
		lowerCamelCase					:      int          =   answer['end_token']

		lowerCamelCase					:      Optional[int]          =   []
		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
		lowerCamelCase					:      Union[str, Any]          =   ' '.join(context[start_token:end_token]      )

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

		return {
		    "context": " ".join(a_      ),
		    "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 		UpperCAmelCase (		a_,						a_,						a_=2048,						a_=4096,						a_=True      ):





		'''simple docstring'''


		lowerCamelCase					:      List[Any]          =   get_context_and_ans(a_,						assertion=a_      )
		lowerCamelCase					:      Tuple          =   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"],
				    },
				}

		lowerCamelCase					:      Any          =   tokenizer(example['question']['text'],						out['context']      ).input_ids
		lowerCamelCase					:      Any          =   input_ids.index(tokenizer.sep_token_id      ) + 1

		# return yes/no
		if answer["category"][0] in ["yes", "no"]:  # category is list with one element
				lowerCamelCase					:      Union[str, Any]          =   []
				lowerCamelCase					:      Tuple          =   []
				lowerCamelCase					:      Tuple          =   input_ids[:q_len]
				lowerCamelCase					:      Dict          =   range(a_,						len(a_      ),						max_length - doc_stride      )
				for i in doc_start_indices:
						lowerCamelCase					:      Optional[int]          =   i + max_length - q_len
						lowerCamelCase					:      str          =   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": [-100] * len(a_      ),
				        "end_token": [-100] * len(a_      ),
				        "category": category,
				    },
				}

		lowerCamelCase					:      str          =   out['context'].split()
		lowerCamelCase					:      Union[str, Any]          =   splitted_context[answer['end_token']]
		lowerCamelCase					:      List[Any]          =   len(
		    tokenizer(
		        ' '.join(splitted_context[: answer['start_token']]      ),						add_special_tokens=a_,						).input_ids      )
		lowerCamelCase					:      int          =   len(
		    tokenizer(' '.join(splitted_context[: answer['end_token']]      ),						add_special_tokens=a_      ).input_ids      )

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

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

		lowerCamelCase					:      Dict          =   input_ids[answer['start_token'] : answer['end_token'] + 1]  # right & left are inclusive
		lowerCamelCase					:      Tuple          =   answer['start_token']
		lowerCamelCase					:      Dict          =   answer['end_token']

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

		if len(a_      ) <= 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"],
				    },
				}

		lowerCamelCase					:      Dict          =   input_ids[:q_len]
		lowerCamelCase					:      int          =   range(a_,						len(a_      ),						max_length - doc_stride      )

		lowerCamelCase					:      Dict          =   []
		lowerCamelCase					:      List[Any]          =   []
		lowerCamelCase					:      Optional[int]          =   []
		lowerCamelCase					:      Any          =   []  # null, yes, no, long, short
		for i in doc_start_indices:
				lowerCamelCase					:      List[str]          =   i + max_length - q_len
				lowerCamelCase					:      Any          =   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:
						lowerCamelCase					:      str          =   start_token - i + q_len
						lowerCamelCase					:      List[Any]          =   end_token - i + q_len
						answers_category.append(answer['category'][0]      )  # ["short"] -> "short"
				else:
						lowerCamelCase					:      str          =   -100
						lowerCamelCase					:      Optional[Any]          =   -100
						answers_category.append('null'      )
				lowerCamelCase					:      Union[str, Any]          =   inputs[-1][start_token : end_token + 1]

				answers_start_token.append(a_      )
				answers_end_token.append(a_      )
				if assertion:
						if new != old and new != [tokenizer.cls_token_id]:
								print('ISSUE in strided for ID:',						example['id']      )
								print('New:',						tokenizer.decode(a_      )      )
								print('Old:',						tokenizer.decode(a_      ),						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 		UpperCAmelCase (		a_,						a_,						a_=2048,						a_=4096,						a_=False      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   get_strided_contexts_and_ans(
		    a_,						a_,						doc_stride=a_,						max_length=a_,						assertion=a_,						)

		return example
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		with jsonlines.open(a_,						'a'      ) as writer:
				for example in tqdm(a_,						total=len(a_      ),						desc='Saving samples ... '      ):
						lowerCamelCase					:      str          =   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

					_A						=			load_dataset('natural_questions')
					_A						=			BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')

					_A						=			data['train' if PROCESS_TRAIN == 'true' else 'validation']

					_A						=			{
					    'tokenizer': tokenizer,
					    'doc_stride': DOC_STRIDE,
					    'max_length': MAX_LENGTH,
					    'assertion': False,
					}
					_A						=			data.map(prepare_inputs, fn_kwargs=fn_kwargs)
					_A						=			data.remove_columns(['annotations', 'document', 'id', 'question'])
					print(data)

					np.random.seed(SEED)
					_A						=			'nq-training.jsonl' if PROCESS_TRAIN == 'true' else 'nq-validation.jsonl'
					save_to_disk(data, file_name=cache_file_name)







"""simple docstring"""

import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


_A						=			{
    'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt',
    'tiny': 'https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt',
    'base.en': 'https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt',
    'base': 'https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt',
    'small.en': 'https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt',
    'small': 'https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt',
    'medium.en': 'https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt',
    'medium': 'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt',
    'large': 'https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt',
    'large-v2': 'https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt',
}
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   ['layers', 'blocks']
		for k in ignore_keys:
				state_dict.pop(a_,						a_      )


_A						=			{
    'blocks': 'layers',
    'mlp.0': 'fc1',
    'mlp.2': 'fc2',
    'mlp_ln': 'final_layer_norm',
    '.attn.query': '.self_attn.q_proj',
    '.attn.key': '.self_attn.k_proj',
    '.attn.value': '.self_attn.v_proj',
    '.attn_ln': '.self_attn_layer_norm',
    '.attn.out': '.self_attn.out_proj',
    '.cross_attn.query': '.encoder_attn.q_proj',
    '.cross_attn.key': '.encoder_attn.k_proj',
    '.cross_attn.value': '.encoder_attn.v_proj',
    '.cross_attn_ln': '.encoder_attn_layer_norm',
    '.cross_attn.out': '.encoder_attn.out_proj',
    'decoder.ln.': 'decoder.layer_norm.',
    'encoder.ln.': 'encoder.layer_norm.',
    'token_embedding': 'embed_tokens',
    'encoder.positional_embedding': 'encoder.embed_positions.weight',
    'decoder.positional_embedding': 'decoder.embed_positions.weight',
    'ln_post': 'layer_norm',
}
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   list(s_dict.keys()      )
		for key in keys:
				lowerCamelCase					:      List[Any]          =   key
				for k, v in WHISPER_MAPPING.items():
						if k in key:
								lowerCamelCase					:      Optional[int]          =   new_key.replace(a_,						a_      )

				print(F"""{key} -> {new_key}"""      )

				lowerCamelCase					:      Any          =   s_dict.pop(a_      )
		return s_dict
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase     ,   lowerCamelCase					:      int          =   emb.weight.shape
		lowerCamelCase					:      Dict          =   nn.Linear(a_,						a_,						bias=a_      )
		lowerCamelCase					:      Union[str, Any]          =   emb.weight.data
		return lin_layer
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		os.makedirs(a_,						exist_ok=a_      )
		lowerCamelCase					:      Union[str, Any]          =   os.path.basename(a_      )

		lowerCamelCase					:      Any          =   url.split('/'      )[-2]
		lowerCamelCase					:      Tuple          =   os.path.join(a_,						a_      )

		if os.path.exists(a_      ) and not os.path.isfile(a_      ):
				raise RuntimeError(F"""{download_target} exists and is not a regular file"""      )

		if os.path.isfile(a_      ):
				lowerCamelCase					:      Union[str, Any]          =   open(a_,						'rb'      ).read()
				if hashlib.shaaaa(a_      ).hexdigest() == expected_shaaaa:
						return model_bytes
				else:
						warnings.warn(F"""{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"""      )

		with urllib.request.urlopen(a_      ) as source, open(a_,						'wb'      ) as output:
				with tqdm(
				    total=int(source.info().get('Content-Length'      )      ),						ncols=80,						unit='iB',						unit_scale=a_,						unit_divisor=1024      ) as loop:
						while True:
								lowerCamelCase					:      Union[str, Any]          =   source.read(8192      )
								if not buffer:
										break

								output.write(a_      )
								loop.update(len(a_      )      )

		lowerCamelCase					:      int          =   open(a_,						'rb'      ).read()
		if hashlib.shaaaa(a_      ).hexdigest() != expected_shaaaa:
				raise RuntimeError(
				    'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.'      )

		return model_bytes
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		if ".pt" not in checkpoint_path:
				lowerCamelCase					:      str          =   _download(_MODELS[checkpoint_path]      )
		else:
				lowerCamelCase					:      Any          =   torch.load(a_,						map_location='cpu'      )
		lowerCamelCase					:      List[str]          =   original_checkpoint['dims']
		lowerCamelCase					:      Any          =   original_checkpoint['model_state_dict']
		lowerCamelCase					:      Tuple          =   state_dict['decoder.token_embedding.weight']
		remove_ignore_keys_(a_      )
		rename_keys(a_      )
		lowerCamelCase					:      List[Any]          =   True
		lowerCamelCase					:      str          =   state_dict['decoder.layers.0.fc1.weight'].shape[0]

		lowerCamelCase					:      Optional[int]          =   WhisperConfig(
		    vocab_size=dimensions['n_vocab'],						encoder_ffn_dim=a_,						decoder_ffn_dim=a_,						num_mel_bins=dimensions['n_mels'],						d_model=dimensions['n_audio_state'],						max_target_positions=dimensions['n_text_ctx'],						encoder_layers=dimensions['n_audio_layer'],						encoder_attention_heads=dimensions['n_audio_head'],						decoder_layers=dimensions['n_text_layer'],						decoder_attention_heads=dimensions['n_text_state'],						max_source_positions=dimensions['n_audio_ctx'],						)

		lowerCamelCase					:      Union[str, Any]          =   WhisperForConditionalGeneration(a_      )
		lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   model.model.load_state_dict(a_,						strict=a_      )
		if len(a_      ) > 0 and not set(a_      ) <= {
		    "encoder.embed_positions.weights",
		    "decoder.embed_positions.weights",
		}:
				raise ValueError(
				    'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
				    F""" but all the following weights are missing {missing}"""      )

		if tie_embeds:
				lowerCamelCase					:      List[Any]          =   make_linear_from_emb(model.model.decoder.embed_tokens      )
		else:
				lowerCamelCase					:      Tuple          =   proj_out_weights

		model.save_pretrained(a_      )


if __name__ == "__main__":
					_A						=			argparse.ArgumentParser()
					# # Required parameters
					parser.add_argument('--checkpoint_path', type=str, help='Patht to the downloaded checkpoints')
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					_A						=			parser.parse_args()

					convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)






