



# 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     SCREAMING_SNAKE_CASE(      __UpperCamelCase=None )	->						Union[str, Any]:
				if subparsers is not None:
								a__	:							List[str]      						= subparsers.add_parser("env" )
				else:
								a__	:							Tuple      						= argparse.ArgumentParser("Accelerate env command" )

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

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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Tuple:
				a__	:							Optional[Any]      						= torch.__version__
				a__	:							Dict      						= torch.cuda.is_available()
				a__	:							Any      						= is_xpu_available()
				a__	:							Any      						= is_npu_available()

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

				a__	:							List[Any]      						= {
				    "`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(__UpperCamelCase ),
				    "PyTorch NPU available": str(__UpperCamelCase ),
				    "System RAM": F'{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB',
				}
				if pt_cuda_available:
								a__	:							Dict      						= 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:" )
				a__	:							str      						= (
				    "\n".join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()] )
				    if isinstance(__UpperCamelCase	,				__UpperCamelCase )
				    else F'\t{accelerate_config}'
				)
				print(__UpperCamelCase )

				a__	:							Optional[int]      						= accelerate_config

				return info






def     SCREAMING_SNAKE_CASE(      )	->						int:
				a__	:							List[str]      						= env_command_parser()
				a__	:							Any      						= parser.parse_args()
				env_command(__UpperCamelCase )
				return 0


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







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

lowerCamelCase		       =      """facebook/wmt19-en-de"""
lowerCamelCase		       =      FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
lowerCamelCase		       =      FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

lowerCamelCase		       =      FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
lowerCamelCase		       =      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
lowerCamelCase		       =      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
lowerCamelCase		       =      """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de


