



import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     @require_torch
     def 	__snake_case    (       self :							Dict				)->int:
      # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
      # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
      # while running an external program

      # python one-liner segments

      # this must be loaded before socket.socket is monkey-patched
      __SCREAMING_SNAKE_CASE       :    Dict       =	"\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

      __SCREAMING_SNAKE_CASE       :    Dict       =	"\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

      __SCREAMING_SNAKE_CASE       :    str       =	"\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")\nsocket.socket = offline_socket\n        "

      # Force fetching the files so that we can use the cache
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	"hf-internal-testing/tiny-random-bert"
      BertConfig.from_pretrained(UpperCamelCase				)
      BertModel.from_pretrained(UpperCamelCase				)
      BertTokenizer.from_pretrained(UpperCamelCase				)
      pipeline(task="fill-mask"     ,  model=UpperCamelCase				)

      # baseline - just load from_pretrained with normal network
      __SCREAMING_SNAKE_CASE       :    List[str]       =	[sys.executable, "-c", "\n".join([load, run, mock]				)]

      # should succeed
      __SCREAMING_SNAKE_CASE       :    str       =	self.get_env()
      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      __SCREAMING_SNAKE_CASE       :    List[str]       =	"1"
      __SCREAMING_SNAKE_CASE       :    Dict       =	subprocess.run(UpperCamelCase     ,  env=UpperCamelCase     ,  check=UpperCamelCase     ,  capture_output=UpperCamelCase				)
      self.assertEqual(result.returncode     ,  0     ,  result.stderr				)
      self.assertIn("success"     ,  result.stdout.decode()				)





     @require_torch
     def 	__snake_case    (       self :							Any				)->Optional[Any]:
      # python one-liner segments
      # this must be loaded before socket.socket is monkey-patched
      __SCREAMING_SNAKE_CASE       :    str       =	"\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

      __SCREAMING_SNAKE_CASE       :    Tuple       =	"\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

      __SCREAMING_SNAKE_CASE       :    str       =	"\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")\nsocket.socket = offline_socket\n        "

      # Force fetching the files so that we can use the cache
      __SCREAMING_SNAKE_CASE       :    str       =	"hf-internal-testing/tiny-random-bert"
      BertConfig.from_pretrained(UpperCamelCase				)
      BertModel.from_pretrained(UpperCamelCase				)
      BertTokenizer.from_pretrained(UpperCamelCase				)
      pipeline(task="fill-mask"     ,  model=UpperCamelCase				)

      # baseline - just load from_pretrained with normal network
      __SCREAMING_SNAKE_CASE       :    str       =	[sys.executable, "-c", "\n".join([load, run, mock]				)]

      # should succeed
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.get_env()
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	subprocess.run(UpperCamelCase     ,  env=UpperCamelCase     ,  check=UpperCamelCase     ,  capture_output=UpperCamelCase				)
      self.assertEqual(result.returncode     ,  0     ,  result.stderr				)
      self.assertIn("success"     ,  result.stdout.decode()				)





     @require_torch
     def 	__snake_case    (       self :							Dict				)->Any:
      # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
      # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
      # while running an external program

      # python one-liner segments

      # this must be loaded before socket.socket is monkey-patched
      __SCREAMING_SNAKE_CASE       :    Any       =	"\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        "

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"\nmname = \"hf-internal-testing/tiny-random-bert-sharded\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint(\"success\")\n        "

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	"\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "

      # baseline - just load from_pretrained with normal network
      __SCREAMING_SNAKE_CASE       :    Dict       =	[sys.executable, "-c", "\n".join([load, run]				)]

      # should succeed
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.get_env()
      __SCREAMING_SNAKE_CASE       :    List[str]       =	subprocess.run(UpperCamelCase     ,  env=UpperCamelCase     ,  check=UpperCamelCase     ,  capture_output=UpperCamelCase				)
      self.assertEqual(result.returncode     ,  0     ,  result.stderr				)
      self.assertIn("success"     ,  result.stdout.decode()				)

      # next emulate no network
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	[sys.executable, "-c", "\n".join([load, mock, run]				)]

      # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
      # env["TRANSFORMERS_OFFLINE"] = "0"
      # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
      # self.assertEqual(result.returncode, 1, result.stderr)

      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      __SCREAMING_SNAKE_CASE       :    int       =	"1"
      __SCREAMING_SNAKE_CASE       :    str       =	subprocess.run(UpperCamelCase     ,  env=UpperCamelCase     ,  check=UpperCamelCase     ,  capture_output=UpperCamelCase				)
      self.assertEqual(result.returncode     ,  0     ,  result.stderr				)
      self.assertIn("success"     ,  result.stdout.decode()				)





     @require_torch
     def 	__snake_case    (       self :							Optional[Any]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Any       =	"\nfrom transformers import pipeline\n        "
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	"\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n        "

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "
      __SCREAMING_SNAKE_CASE       :    int       =	self.get_env()
      __SCREAMING_SNAKE_CASE       :    Tuple       =	"1"
      __SCREAMING_SNAKE_CASE       :    str       =	[sys.executable, "-c", "\n".join([load, mock, run]				)]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	subprocess.run(UpperCamelCase     ,  env=UpperCamelCase     ,  check=UpperCamelCase     ,  capture_output=UpperCamelCase				)
      self.assertEqual(result.returncode     ,  1     ,  result.stderr				)
      self.assertIn(
          "You cannot infer task automatically within `pipeline` when using offline mode"     ,  result.stderr.decode().replace("\n"     ,  ""				)     ,  )






     @require_torch
     def 	__snake_case    (       self :							List[str]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Dict       =	"\nfrom transformers import AutoModel\n        "
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	"\nmname = \"hf-internal-testing/test_dynamic_model\"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint(\"success\")\n        "

      # baseline - just load from_pretrained with normal network
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	[sys.executable, "-c", "\n".join([load, run]				)]

      # should succeed
      __SCREAMING_SNAKE_CASE       :    str       =	self.get_env()
      __SCREAMING_SNAKE_CASE       :    str       =	subprocess.run(UpperCamelCase     ,  env=UpperCamelCase     ,  check=UpperCamelCase     ,  capture_output=UpperCamelCase				)
      self.assertEqual(result.returncode     ,  0     ,  result.stderr				)
      self.assertIn("success"     ,  result.stdout.decode()				)

      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      __SCREAMING_SNAKE_CASE       :    str       =	"1"
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	subprocess.run(UpperCamelCase     ,  env=UpperCamelCase     ,  check=UpperCamelCase     ,  capture_output=UpperCamelCase				)
      self.assertEqual(result.returncode     ,  0     ,  result.stderr				)
      self.assertIn("success"     ,  result.stdout.decode()				)











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






