# Copyright 2022 The HuggingFace 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.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available




def       _snake_case	(   lowerCAmelCase							:   Dict=None	):
		"""simple docstring"""







		if subparsers is not None:
				SCREAMING_SNAKE_CASE_  :		int				      =    subparsers.add_parser("env"	)
		else:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    argparse.ArgumentParser("Accelerate env command"	)

		parser.add_argument(
		    "--config_file"   , default=lowerCAmelCase   , help="The config file to use for the default values in the launching script."	)

		if subparsers is not None:
				parser.set_defaults(func=lowerCAmelCase	)
		return parser




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.__version__
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    torch.cuda.is_available()
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    is_xpu_available()
		SCREAMING_SNAKE_CASE_  :		str				      =    is_npu_available()

		SCREAMING_SNAKE_CASE_  :		int				      =    "Not found"
		# Get the default from the config file.
		if args.config_file is not None or os.path.isfile(lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    load_config_from_file(args.config_file	).to_dict()

		SCREAMING_SNAKE_CASE_  :		str				      =    {
		    "`Accelerate` version": version,
		    "Platform": platform.platform(),
		    "Python version": platform.python_version(),
		    "Numpy version": np.__version__,
		    "PyTorch version (GPU?)": f'{pt_version} ({pt_cuda_available})',
		    "PyTorch XPU available": str(lowerCAmelCase	),
		    "PyTorch NPU available": str(lowerCAmelCase	),
		    "System RAM": f'{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB',
		}
		if pt_cuda_available:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    torch.cuda.get_device_name()

		print("\nCopy-and-paste the text below in your GitHub issue\n"	)
		print("\n".join([f'- {prop}: {val}' for prop, val in info.items()]	)	)

		print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:"	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    (
		    "\n".join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]	)
		    if isinstance(lowerCAmelCase   , lowerCAmelCase	)
		    else f'\t{accelerate_config}'
		)
		print(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Dict				      =    accelerate_config

		return info




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    env_command_parser()
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    parser.parse_args()
		env_command(lowerCAmelCase	)
		return 0


if __name__ == "__main__":
		raise SystemExit(main())






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:		Optional[Any]     =   {
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   ["WhisperTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "WhisperForConditionalGeneration",
     "WhisperModel",
     "WhisperPreTrainedModel",
     "WhisperForAudioClassification",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFWhisperForConditionalGeneration",
     "TFWhisperModel",
     "TFWhisperPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "FlaxWhisperForConditionalGeneration",
     "FlaxWhisperModel",
     "FlaxWhisperPreTrainedModel",
     "FlaxWhisperForAudioClassification",
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:		Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



