




# 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 							_UpperCAmelCase  (UpperCamelCase_ : Any=None       ):
				'''simple docstring'''
				if subparsers is not None:
								_lowerCAmelCase    :    Tuple										=      subparsers.add_parser("""env"""       )
				else:
								_lowerCAmelCase    :    int										=      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 							_UpperCAmelCase  (UpperCamelCase_ : Optional[int]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Tuple										=      torch.__version__
				_lowerCAmelCase    :    List[str]										=      torch.cuda.is_available()
				_lowerCAmelCase    :    str										=      is_xpu_available()
				_lowerCAmelCase    :    Optional[Any]										=      is_npu_available()

				_lowerCAmelCase    :    Optional[int]										=      """Not found"""
				# Get the default from the config file.
				if args.config_file is not None or os.path.isfile(UpperCamelCase_       ):
								_lowerCAmelCase    :    Dict										=      load_config_from_file(args.config_file       ).to_dict()

				_lowerCAmelCase    :    Optional[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 / 1024 ** 3:.2f} GB",
				}
				if pt_cuda_available:
								_lowerCAmelCase    :    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:"""       )
				_lowerCAmelCase    :    List[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_       )

				_lowerCAmelCase    :    Tuple										=      accelerate_config

				return info




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    List[Any]										=      env_command_parser()
				_lowerCAmelCase    :    List[Any]										=      parser.parse_args()
				env_command(UpperCamelCase_       )
				return 0


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







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFResNetForImageClassification, TFResNetModel
	from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor
class        __snake_case  :



				def __init__(      self      :		str		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		List[str]=3		,	_UpperCAmelCase      :		List[str]=32		,	_UpperCAmelCase      :		str=3		,	_UpperCAmelCase      :		Tuple=10		,	_UpperCAmelCase      :		Optional[Any]=[10, 20, 30, 40]		,	_UpperCAmelCase      :		int=[1, 1, 2, 1]		,	_UpperCAmelCase      :		str=True		,	_UpperCAmelCase      :		List[str]=True		,	_UpperCAmelCase      :		str="relu"		,	_UpperCAmelCase      :		int=3		,	_UpperCAmelCase      :		int=None		,	)  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      parent
								_lowerCAmelCase    :    int										=      batch_size
								_lowerCAmelCase    :    List[str]										=      image_size
								_lowerCAmelCase    :    Optional[int]										=      num_channels
								_lowerCAmelCase    :    List[str]										=      embeddings_size
								_lowerCAmelCase    :    int										=      hidden_sizes
								_lowerCAmelCase    :    str										=      depths
								_lowerCAmelCase    :    int										=      is_training
								_lowerCAmelCase    :    Dict										=      use_labels
								_lowerCAmelCase    :    Dict										=      hidden_act
								_lowerCAmelCase    :    Dict										=      num_labels
								_lowerCAmelCase    :    List[Any]										=      scope
								_lowerCAmelCase    :    Optional[int]										=      len(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								_lowerCAmelCase    :    Tuple										=      None
								if self.use_labels:
												_lowerCAmelCase    :    Optional[int]										=      ids_tensor([self.batch_size]		,	self.num_labels      )

								_lowerCAmelCase    :    Dict										=      self.get_config()

								return config, pixel_values, labels



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    int:





								'''simple docstring'''




								return ResNetConfig(
								    num_channels=self.num_channels		,	embeddings_size=self.embeddings_size		,	hidden_sizes=self.hidden_sizes		,	depths=self.depths		,	hidden_act=self.hidden_act		,	num_labels=self.num_labels		,	image_size=self.image_size		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      TFResNetModel(config=_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      model(_UpperCAmelCase      )
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape		,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Optional[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.num_labels
								_lowerCAmelCase    :    Any										=      TFResNetForImageClassification(_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      model(_UpperCAmelCase		,	labels=_UpperCAmelCase      )
								self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.prepare_config_and_inputs()
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      config_and_inputs
								_lowerCAmelCase    :    Tuple										=      {"""pixel_values""": pixel_values}
								return config, inputs_dict






@require_tf
class        __snake_case  (_a						,	_a						,	unittest.TestCase		):
				lowerCAmelCase__				=     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
				lowerCAmelCase__				=     (
				    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
				    if is_tf_available()
				    else {}
				)

				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      TFResNetModelTester(self      )
								_lowerCAmelCase    :    Union[str, Any]										=      ConfigTester(self		,	config_class=_UpperCAmelCase		,	has_text_modality=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()



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





								'''simple docstring'''




								return



				@unittest.skip(reason="""ResNet does not use inputs_embeds"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								pass



				@unittest.skip(reason="""ResNet does not support input and output embeddings"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Union[str, Any]:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Dict										=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_lowerCAmelCase    :    Any										=      model_class(_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[Any]										=      inspect.signature(model.call      )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												_lowerCAmelCase    :    Tuple										=      [*signature.parameters.keys()]

												_lowerCAmelCase    :    Optional[Any]										=      ["""pixel_values"""]
												self.assertListEqual(arg_names[:1]		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_UpperCAmelCase      )



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





								'''simple docstring'''




								def check_hidden_states_output(_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Tuple      ):
												_lowerCAmelCase    :    Dict										=      model_class(_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[int]										=      model(**self._prepare_for_class(_UpperCAmelCase		,	_UpperCAmelCase      )      )

												_lowerCAmelCase    :    Optional[Any]										=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												_lowerCAmelCase    :    Dict										=      self.model_tester.num_stages
												self.assertEqual(len(_UpperCAmelCase      )		,	expected_num_stages + 1      )

												# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]      )		,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,	)

								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      self.model_tester.prepare_config_and_inputs_for_common()
								_lowerCAmelCase    :    str										=      ["""basic""", """bottleneck"""]
								for model_class in self.all_model_classes:
												for layer_type in layers_type:
																_lowerCAmelCase    :    Tuple										=      layer_type
																_lowerCAmelCase    :    Any										=      True
																check_hidden_states_output(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																_lowerCAmelCase    :    Any										=      True

																check_hidden_states_output(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase      )



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    str:





								'''simple docstring'''




								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowerCAmelCase    :    Dict										=      TFResNetModel.from_pretrained(_UpperCAmelCase      )
												self.assertIsNotNone(_UpperCAmelCase      )






def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
				return image





@require_tf
@require_vision
class        __snake_case  (unittest.TestCase		):



				@cached_property
				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
								    if is_vision_available()
								    else None
								)



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )

								_lowerCAmelCase    :    Optional[int]										=      self.default_image_processor
								_lowerCAmelCase    :    int										=      prepare_img()
								_lowerCAmelCase    :    int										=      image_processor(images=_UpperCAmelCase		,	return_tensors="""tf"""      )

								# forward pass
								_lowerCAmelCase    :    int										=      model(**_UpperCAmelCase      )

								# verify the logits
								_lowerCAmelCase    :    Union[str, Any]										=      tf.TensorShape((1, 1000)      )
								self.assertEqual(outputs.logits.shape		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Any										=      tf.constant([-11.1069, -9.7877, -8.3777]      )

								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()		,	_UpperCAmelCase		,	atol=1E-4      )      )

