
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


snake_case 				=       """pytorch_model.bin"""
snake_case 				=       """pytorch_model.bin.index.json"""
snake_case 				=       """adapter_config.json"""
snake_case 				=       """adapter_model.bin"""
snake_case 				=       """adapter_model.safetensors"""
snake_case 				=       """tf_model.h5"""
snake_case 				=       """tf_model.h5.index.json"""
snake_case 				=       """model.ckpt"""
snake_case 				=       """flax_model.msgpack"""
snake_case 				=       """flax_model.msgpack.index.json"""
snake_case 				=       """model.safetensors"""
snake_case 				=       """model.safetensors.index.json"""
snake_case 				=       """config.json"""
snake_case 				=       """preprocessor_config.json"""
snake_case 				=       FEATURE_EXTRACTOR_NAME
snake_case 				=       """generation_config.json"""
snake_case 				=       """modelcard.json"""

snake_case 				=       """▁"""
snake_case 				=       SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

snake_case 				=       [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
snake_case 				=       [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
snake_case 				=       [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str     )      ->						Dict:
  if version.parse(snake_case__     ) < version.parse(snake_case__     ):
    if "dev" in min_version:
      _lowercase							= (
          'This example requires a source install from HuggingFace Transformers (see '
          '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
      )
    else:
      _lowercase							= F"""This example requires a minimum version of {min_version},"""
    error_message += F""" but the version found is {__version__}.\n"""
    raise ImportError(
        error_message
        + 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
        'versions of HuggingFace Transformers.'     )






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


snake_case 				=       logging.get_logger(__name__)


if is_torch_available():
      import torch
      from torch import nn
      from torch.utils.data import Dataset

      from transformers import Trainer





      class 				A_				(      UpperCAmelCase	):







             """simple docstring"""





             def __init__(    self	:	str						,__A	:	int = 101  )						->					Optional[int]:
               _lowercase							= length







             def __len__(    self	:	List[Any]  )						->					Any:
               return self.length







             def __getitem__(    self	:	List[Any]						,__A	:	Optional[int]  )						->					int:
               return i





      class 				A_				:







             """simple docstring"""





             def __call__(    self	:	str						,__A	:	Union[str, Any]  )						->					Any:
               return {"input_ids": torch.tensor(__A  ), "labels": torch.tensor(__A  )}





      class 				A_				(      nn.Module	):







             """simple docstring"""





             def __init__(    self	:	Dict  )						->					Any:
               super().__init__()
               # Add some (unused) params otherwise DDP will complain.
               _lowercase							= nn.Linear(120						,80  )







             def    __UpperCAmelCase (    self	:	Dict						,__A	:	Dict						,__A	:	Any=None  )						->					Tuple:
               if labels is not None:
                 return torch.tensor(0.0						,device=input_ids.device  ), input_ids
               else:
                 return input_ids





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_neuroncore
       def    __UpperCAmelCase (    self	:	int  )						->					Any:
         _lowercase							= F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['torchrun'] + distributed_args + args
         execute_subprocess_async(__A						,env=self.get_env()  )
         # successful return here == success - any errors would have caused an error in the sub-call





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_multi_gpu
       def    __UpperCAmelCase (    self	:	Dict  )						->					List[Any]:
         _lowercase							= F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['torchrun'] + distributed_args + args
         execute_subprocess_async(__A						,env=self.get_env()  )
         # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
      # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
      #
      # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

      snake_case 				=       HfArgumentParser((TrainingArguments,))
      snake_case 				=       parser.parse_args_into_dataclasses()[0]

      logger.warning(
          F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
          F"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
      )

      # Essentially, what we want to verify in the distributed case is that we get all samples back,
      # in the right order. (this is crucial for prediction for instance)
      for dataset_length in [1_0_1, 4_0, 7]:
            snake_case 				=       DummyDataset(dataset_length)

            def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :EvalPrediction     )      ->						Dict:
              _lowercase							= list(range(len(snake_case__     )     )     )
              _lowercase							= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
              if not success and training_args.local_rank == 0:
                logger.warning(
                    'Predictions and/or labels do not match expected results:\n  - predictions: '
                    F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""     )
              return {"success": success}

            snake_case 				=       Trainer(
                model=DummyModel(),
                args=training_args,
                data_collator=DummyDataCollator(),
                eval_dataset=dataset,
                compute_metrics=compute_metrics,
            )
            snake_case 				=       trainer.evaluate()
            logger.info(metrics)
            if metrics["eval_success"] is not True:
                  logger.error(metrics)
                  exit(1)

            snake_case 				=       trainer.predict(dataset)
            logger.info(p.metrics)
            if p.metrics["test_success"] is not True:
                  logger.error(p.metrics)
                  exit(1)

            snake_case 				=       2

            snake_case 				=       trainer.evaluate()
            logger.info(metrics)
            if metrics["eval_success"] is not True:
                  logger.error(metrics)
                  exit(1)

            snake_case 				=       trainer.predict(dataset)
            logger.info(p.metrics)
            if p.metrics["test_success"] is not True:
                  logger.error(p.metrics)
                  exit(1)







            snake_case 				=       None