





import os

import jsonlines
import numpy as np
from tqdm import tqdm


snake_case_     =	20_48
snake_case_     =	40_96
snake_case_     =	42
snake_case_     =	os.environ.pop("""PROCESS_TRAIN""", """false""")
snake_case_     =	{"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  List[str]:
    def choose_first(UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : Dict=False		):
        assert isinstance(UpperCamelCase__    ,       UpperCamelCase__		)
        if len(UpperCamelCase__		) == 1:
            A       :       List[str]							=						answer[0]
            return {k: [answer[k]] for k in answer} if is_long_answer else answer
        for a in answer:
            if is_long_answer:
                A       :       Any							=						{k: [a[k]] for k in a}
            if len(a['''start_token''']		) > 0:
                break
        return a

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

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

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

    return answer
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Tuple=False		)		->  Optional[int]:
    A       :       Dict							=						_get_single_answer(UpperCamelCase__		)
    # 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
        A       :       Optional[Any]							=						example['''document''']['''tokens''']
        A       :       Dict							=						[]
        for i in range(len(doc['''token''']		)		):
            if not doc["is_html"][i]:
                context.append(doc['''token'''][i]		)
        return {
            "context": " ".join(UpperCamelCase__		),
            "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

    A       :       List[str]							=						['''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

    A       :       Tuple							=						example['''document''']['''tokens''']
    A       :       Optional[Any]							=						answer['''start_token''']
    A       :       Any							=						answer['''end_token''']

    A       :       List[str]							=						[]
    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
    A       :       Any							=						''' '''.join(context[start_token:end_token]		)

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

    return {
        "context": " ".join(UpperCamelCase__		),
        "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 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : Union[str, Any]=2_048    ,       UpperCamelCase__    : Tuple=4_096    ,       UpperCamelCase__    : Dict=True		)		->  str:
    # overlap will be of doc_stride - q_len

    A       :       Any							=						get_context_and_ans(UpperCamelCase__    ,       assertion=UpperCamelCase__		)
    A       :       Any							=						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"],
            },
        }

    A       :       Optional[int]							=						tokenizer(example['''question''']['''text''']    ,       out['''context''']		).input_ids
    A       :       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
        A       :       Dict							=						[]
        A       :       Tuple							=						[]
        A       :       Optional[Any]							=						input_ids[:q_len]
        A       :       int							=						range(UpperCamelCase__    ,       len(UpperCamelCase__		)    ,       max_length - doc_stride		)
        for i in doc_start_indices:
            A       :       int							=						i + max_length - q_len
            A       :       Dict							=						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(UpperCamelCase__		),
                "end_token": [-100] * len(UpperCamelCase__		),
                "category": category,
            },
        }

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

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

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

    A       :       List[Any]							=						input_ids[answer['''start_token'''] : answer['''end_token'''] + 1]  # right & left are inclusive
    A       :       Union[str, Any]							=						answer['''start_token''']
    A       :       Optional[Any]							=						answer['''end_token''']

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

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

    A       :       int							=						input_ids[:q_len]
    A       :       Any							=						range(UpperCamelCase__    ,       len(UpperCamelCase__		)    ,       max_length - doc_stride		)

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

        answers_start_token.append(UpperCamelCase__		)
        answers_end_token.append(UpperCamelCase__		)
        if assertion:
            if new != old and new != [tokenizer.cls_token_id]:
                print('''ISSUE in strided for ID:'''    ,       example['''id''']		)
                print('''New:'''    ,       tokenizer.decode(UpperCamelCase__		)		)
                print('''Old:'''    ,       tokenizer.decode(UpperCamelCase__		)    ,       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 							_lowerCamelCase(  UpperCamelCase__    : str    ,       UpperCamelCase__    : List[Any]    ,       UpperCamelCase__    : str=2_048    ,       UpperCamelCase__    : str=4_096    ,       UpperCamelCase__    : List[str]=False		)		->  List[str]:
    A       :       List[str]							=						get_strided_contexts_and_ans(
        UpperCamelCase__    ,       UpperCamelCase__    ,       doc_stride=UpperCamelCase__    ,       max_length=UpperCamelCase__    ,       assertion=UpperCamelCase__    ,       )

    return example
def 							_lowerCamelCase(  UpperCamelCase__    : Any    ,       UpperCamelCase__    : Any		)		->  Any:
    with jsonlines.open(UpperCamelCase__    ,       '''a'''		) as writer:
        for example in tqdm(UpperCamelCase__    ,       total=len(UpperCamelCase__		)    ,       desc='''Saving samples ... '''		):
            A       :       Optional[int]							=						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_     =	load_dataset("""natural_questions""")
     snake_case_     =	BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

     snake_case_     =	data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

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

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







'''simple docstring'''






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

snake_case_     =	"""tiny-wmt19-en-ru"""

# Build

# borrowed from a test
snake_case_     =	[
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
snake_case_     =	dict(zip(vocab, range(len(vocab))))
snake_case_     =	["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     snake_case_     =	Path(tmpdirname)
     snake_case_     =	build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     snake_case_     =	build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     snake_case_     =	build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     snake_case_     =	FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

snake_case_     =	FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=10_00,
    tgt_vocab_size=10_00,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

snake_case_     =	FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
snake_case_     =	tokenizer(["""Making tiny model"""], return_tensors="""pt""")
snake_case_     =	tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
