

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			List[str]      		=       RobertaTokenizer
      _lowerCamelCase       :			Tuple      		=       RobertaTokenizerFast
      _lowerCamelCase       :			str      		=       True
      _lowerCamelCase       :			Tuple      		=       {'cls_token': '<s>'}





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

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

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





      def 		__A	(   self							:				Any						,			**UpperCAmelCase							:				Union[str, Any]	):
            kwargs.update(self.special_tokens_map	)
            return self.tokenizer_class.from_pretrained(self.tmpdirname						,			**UpperCAmelCase	)





      def 		__A	(   self							:				List[Any]						,			**UpperCAmelCase							:				List[str]	):
            kwargs.update(self.special_tokens_map	)
            return RobertaTokenizerFast.from_pretrained(self.tmpdirname						,			**UpperCAmelCase	)





      def 		__A	(   self							:				str						,			UpperCAmelCase							:				Optional[int]	):
            A_						  =				"lower newer"
            A_						  =				"lower newer"
            return input_text, output_text





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

            A_						  =				tokens + [tokenizer.unk_token]
            A_						  =				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase	)						,			UpperCAmelCase	)





      def 		__A	(   self							:				Union[str, Any]	):
            A_						  =				self.get_tokenizer()

            self.assertListEqual(tokenizer.encode("Hello world!"						,			add_special_tokens=UpperCAmelCase	)						,			[0, 31414, 232, 328, 2]	)
            self.assertListEqual(
                tokenizer.encode("Hello world! cécé herlolip 418"						,			add_special_tokens=UpperCAmelCase	)						,			[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]						,			)





      @slow
      def 		__A	(   self							:				int	):
            A_						  =				self.tokenizer_class.from_pretrained("roberta-base"	)

            A_						  =				tokenizer.encode("sequence builders"						,			add_special_tokens=UpperCAmelCase	)
            A_						  =				tokenizer.encode("multi-sequence build"						,			add_special_tokens=UpperCAmelCase	)

            A_						  =				tokenizer.encode(
                "sequence builders"						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)
            A_						  =				tokenizer.encode(
                "sequence builders"						,			"multi-sequence build"						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)

            A_						  =				tokenizer.build_inputs_with_special_tokens(UpperCAmelCase	)
            A_						  =				tokenizer.build_inputs_with_special_tokens(UpperCAmelCase						,			UpperCAmelCase	)

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode





      def 		__A	(   self							:				Tuple	):
            A_						  =				self.get_tokenizer()

            A_						  =				"Encode this sequence."
            A_						  =				tokenizer.byte_encoder[" ".encode("utf-8"	)[0]]

            # Testing encoder arguments
            A_						  =				tokenizer.encode(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
            self.assertNotEqual(UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				tokenizer.encode(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)

            tokenizer.add_special_tokens({"bos_token": "<s>"}	)
            A_						  =				tokenizer.encode(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[1]	)[0]
            self.assertNotEqual(UpperCAmelCase						,			UpperCAmelCase	)

            # Testing spaces after special tokens
            A_						  =				"<mask>"
            tokenizer.add_special_tokens(
                {"mask_token": AddedToken(UpperCAmelCase						,			lstrip=UpperCAmelCase						,			rstrip=UpperCAmelCase	)}	)  # mask token has a left space
            A_						  =				tokenizer.convert_tokens_to_ids(UpperCAmelCase	)

            A_						  =				"Encode <mask> sequence"
            A_						  =				"Encode <mask>sequence"

            A_						  =				tokenizer.encode(UpperCAmelCase	)
            A_						  =				encoded.index(UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				tokenizer.encode(UpperCAmelCase	)
            A_						  =				encoded.index(UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
            self.assertNotEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Any	):
            pass





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

                        # 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"]	)						,			)

                        A_						  =				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
                        A_						  =				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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)
                        self.assertSequenceEqual(tokens_r["input_ids"]						,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)

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





      def 		__A	(   self							:				Union[str, Any]	):
            for trim_offsets, add_prefix_space in itertools.product([True, False]						,			repeat=2	):
                  A_						  =				self.rust_tokenizer_class.from_pretrained(
                      self.tmpdirname						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)

                  A_						  =				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()	)
                  A_						  =				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()	)

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

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







      def 		__A	(   self							:				List[Any]	):
            # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
            # `trim_offsets`
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                        A_						  =				"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                        A_						  =				f'''{text_of_1_token} {text_of_1_token}'''

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	) + 1, len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	) + 1, len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	), len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	), len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				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)),
                        # )

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(1, 1 + len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(1 + len(UpperCAmelCase	) + 1, 1 + len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, 1 + len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(1 + len(UpperCAmelCase	), 1 + len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, 1 + len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(1 + len(UpperCAmelCase	), 1 + len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)



from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
	from .deepspeed import (
	    DeepSpeedEngineWrapper,
	    DeepSpeedOptimizerWrapper,
	    DeepSpeedSchedulerWrapper,
	    DummyOptim,
	    DummyScheduler,
	    HfDeepSpeedConfig,
	)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers

