'''simple docstring'''




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import torch


class       UpperCAmelCase__  (       TensorFormatter[Mapping, '''torch.Tensor''', Mapping]			):






   """simple docstring"""
   def __init__(self       ,				_a=None       ,				**_a				)    ->     Optional[Any]:
        super().__init__(features=_a				)
        lowercase_					:       int     				=					torch_tensor_kwargs
        import torch  # noqa import torch at initialization
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        import torch

        if isinstance(_a       ,				_a				) and column:
             if all(
                 isinstance(_a       ,				torch.Tensor				) and x.shape == column[0].shape and x.dtype == column[0].dtype
                 for x in column				):
                  return torch.stack(_a				)
        return column
   def 		_lowerCamelCase (self       ,				_a				)    ->     Tuple:
        import torch

        if isinstance(_a       ,				(str, bytes, type(_a				))				):
             return value
        elif isinstance(_a       ,				(np.character, np.ndarray)				) and np.issubdtype(value.dtype       ,				np.character				):
             return value.tolist()

        lowercase_					:       List[Any]     				=					{}

        if isinstance(_a       ,				(np.number, np.ndarray)				) and np.issubdtype(value.dtype       ,				np.integer				):
             lowercase_					:       int     				=					{'dtype': torch.intaa}
        elif isinstance(_a       ,				(np.number, np.ndarray)				) and np.issubdtype(value.dtype       ,				np.floating				):
             lowercase_					:       Dict     				=					{'dtype': torch.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(_a       ,				PIL.Image.Image				):
                  lowercase_					:       Optional[Any]     				=					np.asarray(_a				)
        return torch.tensor(_a       ,				**{**default_dtype, **self.torch_tensor_kwargs}				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[str]:
        import torch

        # support for torch, tf, jax etc.
        if hasattr(_a       ,				'__array__'				) and not isinstance(_a       ,				torch.Tensor				):
             lowercase_					:       Optional[Any]     				=					data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(_a       ,				np.ndarray				):
             if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                  return self._consolidate([self.recursive_tensorize(_a				) for substruct in data_struct]				)
        elif isinstance(_a       ,				(list, tuple)				):
             return self._consolidate([self.recursive_tensorize(_a				) for substruct in data_struct]				)
        return self._tensorize(_a				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[Any]:
        return map_nested(self._recursive_tensorize       ,				_a       ,				map_list=_a				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     Mapping:
        lowercase_					:       Dict     				=					self.numpy_arrow_extractor().extract_row(_a				)
        lowercase_					:       str     				=					self.python_features_decoder.decode_row(_a				)
        return self.recursive_tensorize(_a				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     "torch.Tensor":
        lowercase_					:       str     				=					self.numpy_arrow_extractor().extract_column(_a				)
        lowercase_					:       Optional[Any]     				=					self.python_features_decoder.decode_column(_a       ,				pa_table.column_names[0]				)
        lowercase_					:       Optional[int]     				=					self.recursive_tensorize(_a				)
        lowercase_					:       Tuple     				=					self._consolidate(_a				)
        return column





   def 		_lowerCamelCase (self       ,				_a				)    ->     Mapping:
        lowercase_					:       Union[str, Any]     				=					self.numpy_arrow_extractor().extract_batch(_a				)
        lowercase_					:       List[str]     				=					self.python_features_decoder.decode_batch(_a				)
        lowercase_					:       Tuple     				=					self.recursive_tensorize(_a				)
        for column_name in batch:
             lowercase_					:       Tuple     				=					self._consolidate(batch[column_name]				)
        return batch





'''simple docstring'''




import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       str     =    TransfoXLTokenizer
   A							:       Optional[int]     =    False
   A							:       List[str]     =    False
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        super().setUp()

        lowercase_					:       Any     				=					[
            '<unk>',
            '[CLS]',
            '[SEP]',
            'want',
            'unwanted',
            'wa',
            'un',
            'running',
            ',',
            'low',
            'l',
        ]
        lowercase_					:       Any     				=					os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']				)
        with open(self.vocab_file       ,				'w'       ,				encoding='utf-8'				) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Union[str, Any]:
        lowercase_					:       str     				=					True
        return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        lowercase_					:       int     				=					'<unk> UNwanted , running'
        lowercase_					:       int     				=					'<unk> unwanted, running'
        return input_text, output_text
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Any     				=					TransfoXLTokenizer(vocab_file=self.vocab_file       ,				lower_case=_a				)

        lowercase_					:       Optional[Any]     				=					tokenizer.tokenize('<unk> UNwanted , running'				)
        self.assertListEqual(_a       ,				['<unk>', 'unwanted', ',', 'running']				)

        self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)       ,				[0, 4, 8, 7]				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       List[str]     				=					TransfoXLTokenizer(lower_case=_a				)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '				)       ,				['hello', '!', 'how', 'are', 'you', '?']				)
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Dict     				=					TransfoXLTokenizer(lower_case=_a				)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '				)       ,				['HeLLo', '!', 'how', 'Are', 'yoU', '?']				)
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					TransfoXLTokenizer(lower_case=_a				)
        lowercase_					:       int     				=					'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
        lowercase_					:       List[str]     				=					[
            'Hello',
            '(',
            'bracket',
            ')',
            'and',
            'side',
            '@-@',
            'scrolled',
            '[',
            'and',
            ']',
            'Henry',
            '\'s',
            '$',
            '5',
            '@,@',
            '000',
            'with',
            '3',
            '@.@',
            '34',
            'm',
            '.',
            'What',
            '\'s',
            'up',
            '!',
            '?',
        ]

        self.assertListEqual(tokenizer.tokenize(_a				)       ,				_a				)

        self.assertEqual(tokenizer.convert_tokens_to_string(_a				)       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Optional[int]     				=					self.get_tokenizer()
        lowercase_					:       str     				=					len(_a				)

        tokenizer.add_tokens(['new1', 'new2']				)
        tokenizer.move_added_token('new1'       ,				1				)

        # Check that moved token is not copied (duplicate)
        self.assertEqual(len(_a				)       ,				original_len + 2				)
        # Check that token is moved to specified id
        self.assertEqual(tokenizer.encode('new1'				)       ,				[1]				)
        self.assertEqual(tokenizer.decode([1]				)       ,				'new1'				)




