

import os

import jsonlines
import numpy as np
from tqdm import tqdm


lowercase_       		=  2048
lowercase_       		=  4096
lowercase_       		=  42
lowercase_       		=  os.environ.pop("PROCESS_TRAIN", "false")
lowercase_       		=  {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str      )							->      List[str]:





    '''simple docstring'''






    def choose_first(SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=False      ):
        assert isinstance(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        if len(SCREAMING_SNAKE_CASE__      ) == 1:
            A__          =   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__          =   {k: [a[k]] for k in a}
            if len(a['start_token']      ) > 0:
                break
        return a

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

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

    A__          =   ['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__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=False      )							->      int:





    '''simple docstring'''






    A__          =   _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
        A__          =   example['document']['tokens']
        A__          =   []
        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": -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__          =   ['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__          =   example['document']['tokens']
    A__          =   answer['start_token']
    A__          =   answer['end_token']

    A__          =   []
    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__          =   ' '.join(context[start_token:end_token]      )

    # checking above code
    if assertion:
        A__          =   doc['is_html'][answer['start_token'] : answer['end_token']]
        A__          =   doc['token'][answer['start_token'] : answer['end_token']]
        A__          =   ' '.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__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=2048    ,			SCREAMING_SNAKE_CASE__			:	str=4096    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=True      )							->      Union[str, Any]:





    '''simple docstring'''






    A__          =   get_context_and_ans(SCREAMING_SNAKE_CASE__    ,			assertion=SCREAMING_SNAKE_CASE__      )
    A__          =   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__          =   tokenizer(example['question']['text']    ,			out['context']      ).input_ids
    A__          =   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__          =   []
        A__          =   []
        A__          =   input_ids[:q_len]
        A__          =   range(SCREAMING_SNAKE_CASE__    ,			len(SCREAMING_SNAKE_CASE__      )    ,			max_length - doc_stride      )
        for i in doc_start_indices:
            A__          =   i + max_length - q_len
            A__          =   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(SCREAMING_SNAKE_CASE__      ),
                "end_token": [-100] * len(SCREAMING_SNAKE_CASE__      ),
                "category": category,
            },
        }

    A__          =   out['context'].split()
    A__          =   splitted_context[answer['end_token']]
    A__          =   len(
        tokenizer(
            ' '.join(splitted_context[: answer['start_token']]      )    ,			add_special_tokens=SCREAMING_SNAKE_CASE__    ,			).input_ids      )
    A__          =   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
    A__          =   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

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

    if assertion:
        A__          =   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"],
            },
        }

    A__          =   input_ids[:q_len]
    A__          =   range(SCREAMING_SNAKE_CASE__    ,			len(SCREAMING_SNAKE_CASE__      )    ,			max_length - doc_stride      )

    A__          =   []
    A__          =   []
    A__          =   []
    A__          =   []  # null, yes, no, long, short
    for i in doc_start_indices:
        A__          =   i + max_length - q_len
        A__          =   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__          =   start_token - i + q_len
            A__          =   end_token - i + q_len
            answers_category.append(answer['category'][0]      )  # ["short"] -> "short"
        else:
            A__          =   -100
            A__          =   -100
            answers_category.append('null'      )
        A__          =   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__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=2048    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=4096    ,			SCREAMING_SNAKE_CASE__			:	Tuple=False      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   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__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      Optional[Any]:





    '''simple docstring'''






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

     lowercase_       		=  load_dataset("natural_questions")
     lowercase_       		=  BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")

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

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

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





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class     UpperCAmelCase__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Dict      =  GPTaTokenizer
    UpperCAmelCase__						:		Any      =  GPTaTokenizerFast
    UpperCAmelCase__						:		Tuple      =  True
    UpperCAmelCase__						:		int      =  {"add_prefix_space": True}
    UpperCAmelCase__						:		Any      =  False






    def _a					(			self    )  ->       Optional[int]:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __UpperCamelCase    							=[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
             '<|endoftext|>',
         ]
         __UpperCamelCase    							=dict(zip(A_					,   range(len(A_    )    )    )    )
         __UpperCamelCase    							=['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         __UpperCamelCase    							={'unk_token': '<unk>'}

         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['merges_file']    )
         with open(self.vocab_file					,   'w'					,   encoding='utf-8'    ) as fp:
              fp.write(json.dumps(A_    ) + '\n'    )
         with open(self.merges_file					,   'w'					,   encoding='utf-8'    ) as fp:
              fp.write('\n'.join(A_    )    )






    def _a					(			self					,   **A_    )  ->       str:
         kwargs.update(self.special_tokens_map    )
         return GPTaTokenizer.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   **A_    )  ->       Optional[Any]:
         kwargs.update(self.special_tokens_map    )
         return GPTaTokenizerFast.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   A_    )  ->       Tuple:
         __UpperCamelCase    							='lower newer'
         __UpperCamelCase    							='lower newer'
         return input_text, output_text






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=GPTaTokenizer(self.vocab_file					,   self.merges_file					,   **self.special_tokens_map    )
         __UpperCamelCase    							='lower newer'
         __UpperCamelCase    							=['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
         __UpperCamelCase    							=tokenizer.tokenize(A_					,   add_prefix_space=A_    )
         self.assertListEqual(A_					,   A_    )

         __UpperCamelCase    							=tokens + [tokenizer.unk_token]
         __UpperCamelCase    							=[14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )					,   A_    )






    def _a					(			self    )  ->       int:
         if not self.test_rust_tokenizer:
              return

         __UpperCamelCase    							=self.get_tokenizer()
         __UpperCamelCase    							=self.get_rust_tokenizer(add_prefix_space=A_    )

         __UpperCamelCase    							='lower newer'

         # Testing tokenization
         __UpperCamelCase    							=tokenizer.tokenize(A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.tokenize(A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing conversion to ids without special tokens
         __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.encode(A_					,   add_special_tokens=A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing conversion to ids with special tokens
         __UpperCamelCase    							=self.get_rust_tokenizer(add_prefix_space=A_    )
         __UpperCamelCase    							=tokenizer.encode(A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.encode(A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing the unknown token
         __UpperCamelCase    							=tokens + [rust_tokenizer.unk_token]
         __UpperCamelCase    							=[14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_    )					,   A_    )






    def _a					(			self					,   *A_					,   **A_    )  ->       Optional[int]:
         # It's very difficult to mix/test pretokenization with byte-level
         # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
         pass






    def _a					(			self					,   A_=15    )  ->       List[str]:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
                   __UpperCamelCase    							=self.rust_tokenizer_class.from_pretrained(A_					,   **A_    )

                   # Simple input
                   __UpperCamelCase    							='This is a simple input'
                   __UpperCamelCase    							=['This is a simple input 1', 'This is a simple input 2']
                   __UpperCamelCase    							=('This is a simple input', 'This is a pair')
                   __UpperCamelCase    							=[
                       ('This is a simple input 1', 'This is a simple input 2'),
                       ('This is a simple pair 1', 'This is a simple pair 2'),
                   ]

                   # Simple input tests
                   self.assertRaises(A_					,   tokenizer_r.encode					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Simple input
                   self.assertRaises(A_					,   tokenizer_r.encode_plus					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Simple input
                   self.assertRaises(
                       A_					,   tokenizer_r.batch_encode_plus					,   A_					,   max_length=A_					,   padding='max_length'					,   )

                   # Pair input
                   self.assertRaises(A_					,   tokenizer_r.encode					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Pair input
                   self.assertRaises(A_					,   tokenizer_r.encode_plus					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Pair input
                   self.assertRaises(
                       A_					,   tokenizer_r.batch_encode_plus					,   A_					,   max_length=A_					,   padding='max_length'					,   )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=GPTaTokenizer.from_pretrained(self.tmpdirname					,   pad_token='<pad>'    )

         # Simple input
         __UpperCamelCase    							='This is a simple input'
         __UpperCamelCase    							=['This is a simple input looooooooong', 'This is a simple input']
         __UpperCamelCase    							=('This is a simple input', 'This is a pair')
         __UpperCamelCase    							=[
             ('This is a simple input loooooong', 'This is a simple input'),
             ('This is a simple pair loooooong', 'This is a simple pair'),
         ]

         __UpperCamelCase    							=tokenizer.pad_token_id

         __UpperCamelCase    							=tokenizer(A_					,   padding='max_length'					,   max_length=30					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   truncate=A_					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(*A_					,   padding='max_length'					,   max_length=60					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   truncate=A_					,   return_tensors='np'    )

         # s
         # test single string max_length padding
         self.assertEqual(out_s['input_ids'].shape[-1]					,   30    )
         self.assertTrue(pad_token_id in out_s['input_ids']    )
         self.assertTrue(0 in out_s['attention_mask']    )

         # s2
         # test automatic padding
         self.assertEqual(out_sa['input_ids'].shape[-1]					,   33    )
         # long slice doesn't have padding
         self.assertFalse(pad_token_id in out_sa['input_ids'][0]    )
         self.assertFalse(0 in out_sa['attention_mask'][0]    )
         # short slice does have padding
         self.assertTrue(pad_token_id in out_sa['input_ids'][1]    )
         self.assertTrue(0 in out_sa['attention_mask'][1]    )

         # p
         # test single pair max_length padding
         self.assertEqual(out_p['input_ids'].shape[-1]					,   60    )
         self.assertTrue(pad_token_id in out_p['input_ids']    )
         self.assertTrue(0 in out_p['attention_mask']    )

         # p2
         # test automatic padding pair
         self.assertEqual(out_pa['input_ids'].shape[-1]					,   52    )
         # long slice pair doesn't have padding
         self.assertFalse(pad_token_id in out_pa['input_ids'][0]    )
         self.assertFalse(0 in out_pa['attention_mask'][0]    )
         # short slice pair does have padding
         self.assertTrue(pad_token_id in out_pa['input_ids'][1]    )
         self.assertTrue(0 in out_pa['attention_mask'][1]    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							='$$$'
         __UpperCamelCase    							=GPTaTokenizer.from_pretrained(self.tmpdirname					,   bos_token=A_					,   add_bos_token=A_    )

         __UpperCamelCase    							='This is a simple input'
         __UpperCamelCase    							=['This is a simple input 1', 'This is a simple input 2']

         __UpperCamelCase    							=tokenizer.bos_token_id

         __UpperCamelCase    							=tokenizer(A_    )
         __UpperCamelCase    							=tokenizer(A_    )

         self.assertEqual(out_s.input_ids[0]					,   A_    )
         self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids    )    )

         __UpperCamelCase    							=tokenizer.decode(out_s.input_ids    )
         __UpperCamelCase    							=tokenizer.batch_decode(out_sa.input_ids    )

         self.assertEqual(decode_s.split()[0]					,   A_    )
         self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa    )    )






    def _a					(			self    )  ->       Optional[int]:
         pass






    def _a					(			self    )  ->       Any:
         # TODO: change to self.get_tokenizers() when the fast version is implemented
         __UpperCamelCase    							=[self.get_tokenizer(do_lower_case=A_					,   add_bos_token=A_    )]
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   __UpperCamelCase    							='Encode this.'
                   __UpperCamelCase    							='This one too please.'
                   __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
                   encoded_sequence += tokenizer.encode(A_					,   add_special_tokens=A_    )
                   __UpperCamelCase    							=tokenizer.encode_plus(
                       A_					,   A_					,   add_special_tokens=A_					,   return_special_tokens_mask=A_					,   )
                   __UpperCamelCase    							=encoded_sequence_dict['input_ids']
                   __UpperCamelCase    							=encoded_sequence_dict['special_tokens_mask']
                   self.assertEqual(len(A_    )					,   len(A_    )    )

                   __UpperCamelCase    							=[
                       (x if not special_tokens_mask[i] else None) for i, x in enumerate(A_    )
                   ]
                   __UpperCamelCase    							=[x for x in filtered_sequence if x is not None]
                   self.assertEqual(A_					,   A_    )






@require_tokenizers
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       Optional[Any]:
         # More context:
         # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
         # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
         # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   from_slow=A_    )
         __UpperCamelCase    							='A photo of a cat'

         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )
         tokenizer.save_pretrained('test_opt'    )

         __UpperCamelCase    							=AutoTokenizer.from_pretrained('./test_opt'    )
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   use_slow=A_    )
         __UpperCamelCase    							='A photo of a cat'

         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         # Same as above
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )






    @unittest.skip('This test is failing because of a bug in the fast tokenizer'    )
    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   from_slow=A_    )

         __UpperCamelCase    							='bos'
         __UpperCamelCase    							=tokenizer.get_vocab()['bos']

         __UpperCamelCase    							='A photo of a cat'
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         # We changed the bos token
         self.assertEqual(A_					,   [31957, 250, 1345, 9, 10, 4758]    )
         tokenizer.save_pretrained('./tok'    )
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('./tok'    )
         self.assertTrue(tokenizer.is_fast    )
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [31957, 250, 1345, 9, 10, 4758]    )





