
"""simple docstring"""




import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						List[str]					       =   CodeGenTokenizer
				__UpperCamelCase      :						int					       =   CodeGenTokenizerFast
				__UpperCamelCase      :						List[str]					       =   True
				__UpperCamelCase      :						Optional[Any]					       =   {'''add_prefix_space''': True}
				__UpperCamelCase      :						str					       =   False



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											super().setUp()

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

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



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											kwargs.update(self.special_tokens_map    )
											return CodeGenTokenizer.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											kwargs.update(self.special_tokens_map    )
											return CodeGenTokenizerFast.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   """lower newer"""
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   """lower newer"""
											return input_text, output_text



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   CodeGenTokenizer(self.vocab_file   ,       self.merges_file   ,       **self.special_tokens_map    )
											SCREAMING_SNAKE_CASE__						:       int           =   """lower newer"""
											SCREAMING_SNAKE_CASE__						:       Dict           =   ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
											SCREAMING_SNAKE_CASE__						:       int           =   tokenizer.tokenize(SCREAMING_SNAKE_CASE__   ,       add_prefix_space=SCREAMING_SNAKE_CASE__    )
											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   tokens + [tokenizer.unk_token]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   [14, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											if not self.test_rust_tokenizer:
																		return

											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       str           =   self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   """lower newer"""

											# Testing tokenization
											SCREAMING_SNAKE_CASE__						:       List[str]           =   tokenizer.tokenize(SCREAMING_SNAKE_CASE__   ,       add_prefix_space=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__    )
											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# Testing conversion to ids without special tokens
											SCREAMING_SNAKE_CASE__						:       int           =   tokenizer.encode(SCREAMING_SNAKE_CASE__   ,       add_special_tokens=SCREAMING_SNAKE_CASE__   ,       add_prefix_space=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE__   ,       add_special_tokens=SCREAMING_SNAKE_CASE__    )
											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# Testing conversion to ids with special tokens
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer.encode(SCREAMING_SNAKE_CASE__   ,       add_prefix_space=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE__    )
											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# Testing the unknown token
											SCREAMING_SNAKE_CASE__						:       str           =   tokens + [rust_tokenizer.unk_token]
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [14, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__=15    )					->		List[str]:



											"""simple docstring"""




											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )

																									# Simple input
																									SCREAMING_SNAKE_CASE__						:       Any           =   """This is a simple input"""
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   ["""This is a simple input 1""", """This is a simple input 2"""]
																									SCREAMING_SNAKE_CASE__						:       Dict           =   ("""This is a simple input""", """This is a pair""")
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [
																									    ("""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(SCREAMING_SNAKE_CASE__   ,       tokenizer_r.encode   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""    )

																									# Simple input
																									self.assertRaises(SCREAMING_SNAKE_CASE__   ,       tokenizer_r.encode_plus   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""    )

																									# Simple input
																									self.assertRaises(
																									    SCREAMING_SNAKE_CASE__   ,       tokenizer_r.batch_encode_plus   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""   ,       )

																									# Pair input
																									self.assertRaises(SCREAMING_SNAKE_CASE__   ,       tokenizer_r.encode   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""    )

																									# Pair input
																									self.assertRaises(SCREAMING_SNAKE_CASE__   ,       tokenizer_r.encode_plus   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""    )

																									# Pair input
																									self.assertRaises(
																									    SCREAMING_SNAKE_CASE__   ,       tokenizer_r.batch_encode_plus   ,       SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""   ,       )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   CodeGenTokenizer.from_pretrained(self.tmpdirname   ,       pad_token="""<pad>"""    )

											# Simple input
											SCREAMING_SNAKE_CASE__						:       str           =   """This is a simple input"""
											SCREAMING_SNAKE_CASE__						:       int           =   ["""This is a simple input looooooooong""", """This is a simple input"""]
											SCREAMING_SNAKE_CASE__						:       str           =   ("""This is a simple input""", """This is a pair""")
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [
											    ("""This is a simple input loooooong""", """This is a simple input"""),
											    ("""This is a simple pair loooooong""", """This is a simple pair"""),
											]

											SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer.pad_token_id

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""   ,       max_length=30   ,       return_tensors="""np"""    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       padding=SCREAMING_SNAKE_CASE__   ,       truncate=SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer(*SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""   ,       max_length=60   ,       return_tensors="""np"""    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       padding=SCREAMING_SNAKE_CASE__   ,       truncate=SCREAMING_SNAKE_CASE__   ,       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 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   """$$$"""
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   CodeGenTokenizer.from_pretrained(self.tmpdirname   ,       bos_token=SCREAMING_SNAKE_CASE__   ,       add_bos_token=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """This is a simple input"""
											SCREAMING_SNAKE_CASE__						:       List[str]           =   ["""This is a simple input 1""", """This is a simple input 2"""]

											SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer.bos_token_id

											SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer(SCREAMING_SNAKE_CASE__    )

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

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tokenizer.decode(out_s.input_ids    )
											SCREAMING_SNAKE_CASE__						:       int           =   tokenizer.batch_decode(out_sa.input_ids    )

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



				@slow
				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""    )

											SCREAMING_SNAKE_CASE__						:       str           =   """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
											SCREAMING_SNAKE_CASE__						:       int           =   """\nif len_a > len_b:      result = a\nelse:      result = b"""

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   tokenizer.encode(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   ["""^#""", re.escape("""<|endoftext|>"""    ), """^'''""", """^\"\"\"""", """\n\n\n"""]
											SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer.decode(SCREAMING_SNAKE_CASE__   ,       truncate_before_pattern=SCREAMING_SNAKE_CASE__    )
											self.assertEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											pass





"""simple docstring"""


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
