






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase      :    List[str]   		=   get_tests_dir("""fixtures/test_sentencepiece.model""")
__lowerCamelCase      :    Optional[int]   		=   get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

__lowerCamelCase      :    Union[str, Any]   		=   """pt""" if is_torch_available() else """tf"""



@require_sentencepiece
@require_tokenizers
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	CamembertTokenizer
					a_			=	CamembertTokenizerFast
					a_			=	True
					a_			=	True



					def  _lowercase  (      self  :						Optional[Any] ):
						super().setUp()

						# We have a SentencePiece fixture for testing
						snake_case__				:   Optional[Any]     =						CamembertTokenizer(__A )
						tokenizer.save_pretrained(self.tmpdirname )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   Union[str, Any]     =						"<pad>"
						snake_case__				:   Dict     =						1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A )							,	__A )
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A )							,	__A )



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   List[Any]     =						list(self.get_tokenizer().get_vocab().keys() )

						self.assertEqual(vocab_keys[0]							,	"<s>NOTUSED" )
						self.assertEqual(vocab_keys[1]							,	"<pad>" )
						self.assertEqual(vocab_keys[-1]							,	"<mask>" )
						self.assertEqual(len(__A )							,	1_0_0_4 )



					def  _lowercase  (      self  :						Optional[Any] ):
						self.assertEqual(self.get_tokenizer().vocab_size							,	1_0_0_5 )



					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Optional[int]     =						CamembertTokenizer(__A )
						tokenizer.save_pretrained(self.tmpdirname )
						snake_case__				:   Dict     =						CamembertTokenizerFast.from_pretrained(self.tmpdirname )

						snake_case__				:   int     =						"I was born in 92000, and this is falsé."

						snake_case__				:   Union[str, Any]     =						tokenizer.encode(__A )
						snake_case__				:   Tuple     =						rust_tokenizer.encode(__A )
						self.assertListEqual(__A							,	__A )

						snake_case__				:   int     =						tokenizer.encode(__A							,	add_special_tokens=__A )
						snake_case__				:   List[str]     =						rust_tokenizer.encode(__A							,	add_special_tokens=__A )
						self.assertListEqual(__A							,	__A )

						# <unk> tokens are not the same for `rust` than for `slow`.
						# Because spm gives back raw token instead of `unk` in EncodeAsPieces
						# tokens = tokenizer.tokenize(sequence)
						snake_case__				:   Optional[Any]     =						tokenizer.convert_ids_to_tokens(__A )
						snake_case__				:   Optional[int]     =						rust_tokenizer.tokenize(__A )
						self.assertListEqual(__A							,	__A )



					def  _lowercase  (      self  :						Union[str, Any] ):




						if not self.test_rust_tokenizer:
							return

						snake_case__				:   Optional[Any]     =						self.get_tokenizer()
						snake_case__				:   Dict     =						self.get_rust_tokenizer()

						snake_case__				:   Union[str, Any]     =						"I was born in 92000, and this is falsé."

						snake_case__				:   List[Any]     =						tokenizer.tokenize(__A )
						snake_case__				:   List[Any]     =						rust_tokenizer.tokenize(__A )
						self.assertListEqual(__A							,	__A )

						snake_case__				:   Union[str, Any]     =						tokenizer.encode(__A							,	add_special_tokens=__A )
						snake_case__				:   Union[str, Any]     =						rust_tokenizer.encode(__A							,	add_special_tokens=__A )
						self.assertListEqual(__A							,	__A )

						snake_case__				:   int     =						self.get_rust_tokenizer()
						snake_case__				:   List[str]     =						tokenizer.encode(__A )
						snake_case__				:   Any     =						rust_tokenizer.encode(__A )
						self.assertListEqual(__A							,	__A )






					@slow
					def  _lowercase  (      self  :						int ):
						snake_case__				:   Tuple     =						{"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						# camembert is a french model. So we also use french texts.
						snake_case__				:   List[Any]     =						[
						    "Le transformeur est un modèle d\'apprentissage profond introduit en 2017, "
						    "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
						    "À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
						    "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
						    "telles que la traduction et la synthèse de texte.",
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=__A							,	model_name="camembert-base"							,	revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"							,	sequences=__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



