






import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 							SCREAMING_SNAKE_CASE__		(       __a				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	LongformerTokenizer
					a_			=	True
					a_			=	LongformerTokenizerFast
					a_			=	True



					def  _lowercase  (      self  :						Any ):
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						snake_case__				:   List[Any]     =						[
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """\u0120""",
						    """\u0120l""",
						    """\u0120n""",
						    """\u0120lo""",
						    """\u0120low""",
						    """er""",
						    """\u0120lowest""",
						    """\u0120newer""",
						    """\u0120wider""",
						    """<unk>""",
						]
						snake_case__				:   List[str]     =						dict(zip(a_							,	range(len(a_ ) ) ) )
						snake_case__				:   List[Any]     =						["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
						snake_case__				:   Any     =						{"""unk_token""": """<unk>"""}

						snake_case__				:   int     =						os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES["vocab_file"] )
						snake_case__				:   Tuple     =						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  _lowercase  (      self  :						Union[str, Any]							,	**__A  :						List[Any] ):
						kwargs.update(self.special_tokens_map )
						return self.tokenizer_class.from_pretrained(self.tmpdirname							,	**a_ )



					def  _lowercase  (      self  :						str							,	**__A  :						str ):
						kwargs.update(self.special_tokens_map )
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,	**a_ )



					def  _lowercase  (      self  :						Tuple							,	__A  :						Tuple ):
						snake_case__				:   int     =						"""lower newer"""
						snake_case__				:   Optional[int]     =						"""lower newer"""
						return input_text, output_text



					def  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   Any     =						self.tokenizer_class(self.vocab_file							,	self.merges_file							,	**self.special_tokens_map )
						snake_case__				:   int     =						"""lower newer"""
						snake_case__				:   Union[str, Any]     =						["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
						snake_case__				:   int     =						tokenizer.tokenize(a_ )  # , add_prefix_space=True)
						self.assertListEqual(a_							,	a_ )

						snake_case__				:   Any     =						tokens + [tokenizer.unk_token]
						snake_case__				:   str     =						[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(a_ )							,	a_ )



					def  _lowercase  (      self  :						List[Any] ):
						snake_case__				:   int     =						self.get_tokenizer()

						self.assertListEqual(tokenizer.encode("Hello world!"							,	add_special_tokens=a_ )							,	[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2] )
						self.assertListEqual(
						    tokenizer.encode("Hello world! cécé herlolip 418"							,	add_special_tokens=a_ )							,	[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]							,	)



					@slow
					def  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   Optional[int]     =						self.tokenizer_class.from_pretrained("allenai/longformer-base-4096" )

						snake_case__				:   Union[str, Any]     =						tokenizer.encode("sequence builders"							,	add_special_tokens=a_ )
						snake_case__				:   Tuple     =						tokenizer.encode("multi-sequence build"							,	add_special_tokens=a_ )

						snake_case__				:   Tuple     =						tokenizer.encode(
						    "sequence builders"							,	add_special_tokens=a_							,	add_prefix_space=a_ )
						snake_case__				:   Optional[Any]     =						tokenizer.encode(
						    "sequence builders"							,	"multi-sequence build"							,	add_special_tokens=a_							,	add_prefix_space=a_ )

						snake_case__				:   List[str]     =						tokenizer.build_inputs_with_special_tokens(a_ )
						snake_case__				:   Union[str, Any]     =						tokenizer.build_inputs_with_special_tokens(a_							,	a_ )

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode



					def  _lowercase  (      self  :						str ):
						snake_case__				:   Optional[Any]     =						self.get_tokenizer()

						snake_case__				:   Dict     =						"""Encode this sequence."""
						snake_case__				:   Optional[Any]     =						tokenizer.byte_encoder[""" """.encode("utf-8" )[0]]

						# Testing encoder arguments
						snake_case__				:   Union[str, Any]     =						tokenizer.encode(a_							,	add_special_tokens=a_							,	add_prefix_space=a_ )
						snake_case__				:   Optional[int]     =						tokenizer.convert_ids_to_tokens(encoded[0] )[0]
						self.assertNotEqual(a_							,	a_ )

						snake_case__				:   Optional[int]     =						tokenizer.encode(a_							,	add_special_tokens=a_							,	add_prefix_space=a_ )
						snake_case__				:   Optional[int]     =						tokenizer.convert_ids_to_tokens(encoded[0] )[0]
						self.assertEqual(a_							,	a_ )

						tokenizer.add_special_tokens({"bos_token": "<s>"} )
						snake_case__				:   Union[str, Any]     =						tokenizer.encode(a_							,	add_special_tokens=a_ )
						snake_case__				:   str     =						tokenizer.convert_ids_to_tokens(encoded[1] )[0]
						self.assertNotEqual(a_							,	a_ )

						# Testing spaces after special tokens
						snake_case__				:   List[str]     =						"""<mask>"""
						tokenizer.add_special_tokens(
						    {"mask_token": AddedToken(a_							,	lstrip=a_							,	rstrip=a_ )} )  # mask token has a left space
						snake_case__				:   str     =						tokenizer.convert_tokens_to_ids(a_ )

						snake_case__				:   int     =						"""Encode <mask> sequence"""
						snake_case__				:   Tuple     =						"""Encode <mask>sequence"""

						snake_case__				:   List[str]     =						tokenizer.encode(a_ )
						snake_case__				:   Optional[int]     =						encoded.index(a_ )
						snake_case__				:   Tuple     =						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
						self.assertEqual(a_							,	a_ )

						snake_case__				:   Union[str, Any]     =						tokenizer.encode(a_ )
						snake_case__				:   Optional[int]     =						encoded.index(a_ )
						snake_case__				:   Any     =						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
						self.assertNotEqual(a_							,	a_ )



					def  _lowercase  (      self  :						Tuple ):
						pass



					def  _lowercase  (      self  :						List[str] ):



						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
								snake_case__				:   str     =						self.rust_tokenizer_class.from_pretrained(a_							,	**a_ )
								snake_case__				:   Dict     =						self.tokenizer_class.from_pretrained(a_							,	**a_ )
								snake_case__				:   List[str]     =						"""A, <mask> AllenNLP sentence."""
								snake_case__				:   Tuple     =						tokenizer_r.encode_plus(a_							,	add_special_tokens=a_							,	return_token_type_ids=a_ )
								snake_case__				:   Optional[int]     =						tokenizer_p.encode_plus(a_							,	add_special_tokens=a_							,	return_token_type_ids=a_ )

								# token_type_ids should put 0 everywhere
								self.assertEqual(sum(tokens_r["token_type_ids"] )							,	sum(tokens_p["token_type_ids"] ) )

								# attention_mask should put 1 everywhere, so sum over length should be 1
								self.assertEqual(
								    sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] )							,	sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] )							,	)

								snake_case__				:   int     =						tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
								snake_case__				:   int     =						tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )

								# Rust correctly handles the space before the mask while python doesnt
								self.assertSequenceEqual(tokens_p["input_ids"]							,	[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )
								self.assertSequenceEqual(tokens_r["input_ids"]							,	[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )

								self.assertSequenceEqual(
								    a_							,	["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
								self.assertSequenceEqual(
								    a_							,	["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )



					def  _lowercase  (      self  :						List[str] ):



						for trim_offsets, add_prefix_space in itertools.product([True, False]							,	repeat=2 ):
							snake_case__				:   Any     =						self.rust_tokenizer_class.from_pretrained(
							    self.tmpdirname							,	use_fast=a_							,	add_prefix_space=a_							,	trim_offsets=a_ )

							snake_case__				:   Optional[Any]     =						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
							snake_case__				:   Any     =						json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

							self.assertEqual(pre_tokenizer_state["add_prefix_space"]							,	a_ )

							self.assertEqual(post_processor_state["add_prefix_space"]							,	a_ )
							self.assertEqual(post_processor_state["trim_offsets"]							,	a_ )






					def  _lowercase  (      self  :						List[str] ):



						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
								snake_case__				:   Optional[int]     =						"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
								snake_case__				:   int     =						f'''{text_of_1_token} {text_of_1_token}'''

								snake_case__				:   Optional[int]     =						self.rust_tokenizer_class.from_pretrained(
								    a_							,	use_fast=a_							,	add_prefix_space=a_							,	trim_offsets=a_ )
								snake_case__				:   Dict     =						tokenizer_r(a_							,	return_offsets_mapping=a_							,	add_special_tokens=a_ )
								self.assertEqual(encoding.offset_mapping[0]							,	(0, len(a_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]							,	(len(a_ ) + 1, len(a_ ) + 1 + len(a_ ))							,	)

								snake_case__				:   Union[str, Any]     =						self.rust_tokenizer_class.from_pretrained(
								    a_							,	use_fast=a_							,	add_prefix_space=a_							,	trim_offsets=a_ )
								snake_case__				:   str     =						tokenizer_r(a_							,	return_offsets_mapping=a_							,	add_special_tokens=a_ )
								self.assertEqual(encoding.offset_mapping[0]							,	(0, len(a_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]							,	(len(a_ ) + 1, len(a_ ) + 1 + len(a_ ))							,	)

								snake_case__				:   Union[str, Any]     =						self.rust_tokenizer_class.from_pretrained(
								    a_							,	use_fast=a_							,	add_prefix_space=a_							,	trim_offsets=a_ )
								snake_case__				:   Dict     =						tokenizer_r(a_							,	return_offsets_mapping=a_							,	add_special_tokens=a_ )
								self.assertEqual(encoding.offset_mapping[0]							,	(0, len(a_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]							,	(len(a_ ), len(a_ ) + 1 + len(a_ ))							,	)

								snake_case__				:   Tuple     =						self.rust_tokenizer_class.from_pretrained(
								    a_							,	use_fast=a_							,	add_prefix_space=a_							,	trim_offsets=a_ )
								snake_case__				:   Union[str, Any]     =						tokenizer_r(a_							,	return_offsets_mapping=a_							,	add_special_tokens=a_ )
								self.assertEqual(encoding.offset_mapping[0]							,	(0, len(a_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]							,	(len(a_ ), len(a_ ) + 1 + len(a_ ))							,	)

								snake_case__				:   Tuple     =						f''' {text}'''

								# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
								#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
								# )
								# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
								# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
								# self.assertEqual(
								#     encoding.offset_mapping[1],
								#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
								# )

								snake_case__				:   Optional[Any]     =						self.rust_tokenizer_class.from_pretrained(
								    a_							,	use_fast=a_							,	add_prefix_space=a_							,	trim_offsets=a_ )
								snake_case__				:   Union[str, Any]     =						tokenizer_r(a_							,	return_offsets_mapping=a_							,	add_special_tokens=a_ )
								self.assertEqual(encoding.offset_mapping[0]							,	(1, 1 + len(a_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]							,	(1 + len(a_ ) + 1, 1 + len(a_ ) + 1 + len(a_ ))							,	)

								snake_case__				:   Tuple     =						self.rust_tokenizer_class.from_pretrained(
								    a_							,	use_fast=a_							,	add_prefix_space=a_							,	trim_offsets=a_ )
								snake_case__				:   Any     =						tokenizer_r(a_							,	return_offsets_mapping=a_							,	add_special_tokens=a_ )
								self.assertEqual(encoding.offset_mapping[0]							,	(0, 1 + len(a_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]							,	(1 + len(a_ ), 1 + len(a_ ) + 1 + len(a_ ))							,	)

								snake_case__				:   List[str]     =						self.rust_tokenizer_class.from_pretrained(
								    a_							,	use_fast=a_							,	add_prefix_space=a_							,	trim_offsets=a_ )
								snake_case__				:   Optional[Any]     =						tokenizer_r(a_							,	return_offsets_mapping=a_							,	add_special_tokens=a_ )
								self.assertEqual(encoding.offset_mapping[0]							,	(0, 1 + len(a_ )) )
								self.assertEqual(
								    encoding.offset_mapping[1]							,	(1 + len(a_ ), 1 + len(a_ ) + 1 + len(a_ ))							,	)









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

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
	import jax
	import jaxlib

__lowerCamelCase      :    Optional[int]   		=   get_logger()

__lowerCamelCase      :    Optional[dict]   		=   None



class 							SCREAMING_SNAKE_CASE__		(       TensorFormatter[Mapping, "jax.Array", Mapping]		):





					"""simple docstring"""






					def __init__(      self  :						Optional[Any]							,	__A  :						Dict=None							,	__A  :						List[str]=None							,	**__A  :						str ):
						super().__init__(features=__A )
						import jax
						from jaxlib.xla_client import Device

						if isinstance(__A							,	__A ):
							raise ValueError(
							    f'''Expected {device} to be a `str` not {type(__A )}, as `jaxlib.xla_extension.Device` '''
							    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
							    "the device with `str()` to get its string identifier that will be internally mapped "
							    "to the actual `jaxlib.xla_extension.Device`." )
						snake_case__				:   List[Any]     =						device if isinstance(__A							,	__A ) else str(jax.devices()[0] )
						# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
						# with `pickle` nor with `dill`, so we need to use a global variable instead
						global DEVICE_MAPPING
						if DEVICE_MAPPING is None:
							snake_case__				:   Any     =						self._map_devices_to_str()
						if self.device not in list(DEVICE_MAPPING.keys() ):
							logger.warning(
							    f'''Device with string identifier {self.device} not listed among the available '''
							    f'''devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '''
							    f'''device: {str(jax.devices()[0] )}.''' )
							snake_case__				:   str     =						str(jax.devices()[0] )
						snake_case__				:   str     =						jnp_array_kwargs



					@staticmethod
					def  _lowercase  (      ):
						import jax

						return {str(__A ): device for device in jax.devices()}



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						str ):
						import jax
						import jax.numpy as jnp

						if isinstance(__A							,	__A ) and column:
							if all(
							    isinstance(__A							,	jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
								return jnp.stack(__A							,	axis=0 )
						return column



					def  _lowercase  (      self  :						int							,	__A  :						Tuple ):
						import jax
						import jax.numpy as jnp

						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()

						snake_case__				:   Optional[int]     =						{}

						if isinstance(__A							,	(np.number, np.ndarray) ) and np.issubdtype(value.dtype							,	np.integer ):
							# the default int precision depends on the jax config
							# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
							if jax.config.jax_enable_xaa:
								snake_case__				:   Any     =						{"dtype": jnp.intaa}
							else:
								snake_case__				:   Tuple     =						{"dtype": jnp.intaa}
						elif isinstance(__A							,	(np.number, np.ndarray) ) and np.issubdtype(value.dtype							,	np.floating ):
							snake_case__				:   str     =						{"dtype": jnp.floataa}
						elif config.PIL_AVAILABLE and "PIL" in sys.modules:
							import PIL.Image

							if isinstance(__A							,	PIL.Image.Image ):
								snake_case__				:   Optional[Any]     =						np.asarray(__A )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
						global DEVICE_MAPPING
						if DEVICE_MAPPING is None:
							snake_case__				:   int     =						self._map_devices_to_str()

						with jax.default_device(DEVICE_MAPPING[self.device] ):
							# calling jnp.array on a np.ndarray does copy the data
							# see https://github.com/google/jax/issues/4486
							return jnp.array(__A							,	**{**default_dtype, **self.jnp_array_kwargs} )



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Optional[int] ):
						import jax

						# support for torch, tf, jax etc.
						if config.TORCH_AVAILABLE and "torch" in sys.modules:
							import torch

							if isinstance(__A							,	torch.Tensor ):
								return self._tensorize(data_struct.detach().cpu().numpy()[()] )
						if hasattr(__A							,	"__array__" ) and not isinstance(__A							,	jax.Array ):
							snake_case__				:   Union[str, Any]     =						data_struct.__array__()
						# support for nested types like struct of list of struct
						if isinstance(__A							,	np.ndarray ):
							if data_struct.dtype == object:  # jax arrays 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  _lowercase  (      self  :						Tuple							,	__A  :						dict ):
						return map_nested(self._recursive_tensorize							,	__A							,	map_list=__A )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						pa.Table ):
						snake_case__				:   int     =						self.numpy_arrow_extractor().extract_row(__A )
						snake_case__				:   Tuple     =						self.python_features_decoder.decode_row(__A )
						return self.recursive_tensorize(__A )



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						pa.Table ):
						snake_case__				:   Any     =						self.numpy_arrow_extractor().extract_column(__A )
						snake_case__				:   Optional[int]     =						self.python_features_decoder.decode_column(__A							,	pa_table.column_names[0] )
						snake_case__				:   List[Any]     =						self.recursive_tensorize(__A )
						snake_case__				:   Dict     =						self._consolidate(__A )
						return column






					def  _lowercase  (      self  :						str							,	__A  :						pa.Table ):
						snake_case__				:   Any     =						self.numpy_arrow_extractor().extract_batch(__A )
						snake_case__				:   int     =						self.python_features_decoder.decode_batch(__A )
						snake_case__				:   List[Any]     =						self.recursive_tensorize(__A )
						for column_name in batch:
							snake_case__				:   Any     =						self._consolidate(batch[column_name] )
						return batch



