





'''simple docstring'''





# Copyright 2021 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_lowerCamelCase			       =      'Create a default config file for Accelerate with only a few flags set.'




def    __UpperCAmelCase(      lowercase_="no" ,    lowercase_ = default_json_config_file ,    lowercase_ = False      ):
       _lowerCamelCase							:     List[str]	=		Path(lowercase_      )
       path.parent.mkdir(parents=lowercase_ ,    exist_ok=lowercase_      )
       if path.exists():
              print(
                  F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""      )
              return False
       _lowerCamelCase							:     List[Any]	=		mixed_precision.lower()
       if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
              raise ValueError(
                  F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""      )
       _lowerCamelCase							:     Optional[int]	=		{
           '''compute_environment''': '''LOCAL_MACHINE''',
           '''mixed_precision''': mixed_precision,
       }
       if torch.cuda.is_available():
              _lowerCamelCase							:     Any	=		torch.cuda.device_count()
              _lowerCamelCase							:     str	=		num_gpus
              _lowerCamelCase							:     Union[str, Any]	=		False
              if num_gpus > 1:
                     _lowerCamelCase							:     str	=		'''MULTI_GPU'''
              else:
                     _lowerCamelCase							:     List[str]	=		'''NO'''
       elif is_xpu_available() and use_xpu:
              _lowerCamelCase							:     Union[str, Any]	=		torch.xpu.device_count()
              _lowerCamelCase							:     int	=		num_xpus
              _lowerCamelCase							:     Dict	=		False
              if num_xpus > 1:
                     _lowerCamelCase							:     List[str]	=		'''MULTI_XPU'''
              else:
                     _lowerCamelCase							:     Any	=		'''NO'''
       elif is_npu_available():
              _lowerCamelCase							:     List[str]	=		torch.npu.device_count()
              _lowerCamelCase							:     Optional[int]	=		num_npus
              _lowerCamelCase							:     str	=		False
              if num_npus > 1:
                     _lowerCamelCase							:     List[Any]	=		'''MULTI_NPU'''
              else:
                     _lowerCamelCase							:     str	=		'''NO'''
       else:
              _lowerCamelCase							:     Tuple	=		0
              _lowerCamelCase							:     Optional[Any]	=		True
              _lowerCamelCase							:     List[str]	=		1
              _lowerCamelCase							:     Union[str, Any]	=		'''NO'''
       _lowerCamelCase							:     Any	=		ClusterConfig(**lowercase_      )
       config.to_json_file(lowercase_      )
       return path




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     List[str]	=		parser.add_parser('''default''' ,    parents=lowercase_ ,    help=lowercase_ ,    formatter_class=lowercase_      )
       parser.add_argument(
           '''--config_file''' ,    default=lowercase_ ,    help=(
               '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
               '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
               '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
               '''with \'huggingface\'.'''
           ) ,    dest='''save_location''' ,    )

       parser.add_argument(
           '''--mixed_precision''' ,    choices=['''no''', '''fp16''', '''bf16'''] ,    type=lowercase_ ,    help='''Whether or not to use mixed precision training. '''
           '''Choose between FP16 and BF16 (bfloat16) training. '''
           '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.''' ,    default='''no''' ,    )
       parser.set_defaults(func=lowercase_      )
       return parser




def    __UpperCAmelCase(      lowercase_      ):
       _lowerCamelCase							:     str	=		write_basic_config(args.mixed_precision ,    args.save_location      )
       if config_file:
              print(F"""accelerate configuration saved at {config_file}"""      )








import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           LevitForImageClassification,
           LevitForImageClassificationWithTeacher,
           LevitModel,
       )
       from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import LevitImageProcessor







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.config_class(**self.inputs_dict)
             self.parent.assertTrue(hasattr(a__      ,							'''hidden_sizes'''))
             self.parent.assertTrue(hasattr(a__      ,							'''num_attention_heads'''))







class 			__A       :




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__=13      ,							a__=64      ,							a__=3      ,							a__=3      ,							a__=2      ,							a__=1      ,							a__=16      ,							a__=[128, 256, 384]      ,							a__=[4, 6, 8]      ,							a__=[2, 3, 4]      ,							a__=[16, 16, 16]      ,							a__=0      ,							a__=[2, 2, 2]      ,							a__=[2, 2, 2]      ,							a__=0.02      ,							a__=True      ,							a__=True      ,							a__=2      ,							):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		parent
             _lowerCamelCase							:     Union[str, Any]	=		batch_size
             _lowerCamelCase							:     Dict	=		image_size
             _lowerCamelCase							:     Dict	=		num_channels
             _lowerCamelCase							:     Any	=		kernel_size
             _lowerCamelCase							:     int	=		stride
             _lowerCamelCase							:     Dict	=		padding
             _lowerCamelCase							:     Union[str, Any]	=		hidden_sizes
             _lowerCamelCase							:     Optional[Any]	=		num_attention_heads
             _lowerCamelCase							:     Any	=		depths
             _lowerCamelCase							:     List[Any]	=		key_dim
             _lowerCamelCase							:     Optional[Any]	=		drop_path_rate
             _lowerCamelCase							:     Union[str, Any]	=		patch_size
             _lowerCamelCase							:     int	=		attention_ratio
             _lowerCamelCase							:     Any	=		mlp_ratio
             _lowerCamelCase							:     List[str]	=		initializer_range
             _lowerCamelCase							:     Dict	=		[
                 ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
                 ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
             ]
             _lowerCamelCase							:     str	=		is_training
             _lowerCamelCase							:     Tuple	=		use_labels
             _lowerCamelCase							:     List[Any]	=		num_labels
             _lowerCamelCase							:     Union[str, Any]	=		initializer_range


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

             _lowerCamelCase							:     str	=		None
             if self.use_labels:
                    _lowerCamelCase							:     List[str]	=		ids_tensor([self.batch_size]      ,							self.num_labels)

             _lowerCamelCase							:     List[Any]	=		self.get_config()
             return config, pixel_values, labels


      def 				__snake_case			(		self):



             """simple docstring"""





             return LevitConfig(
                 image_size=self.image_size      ,							num_channels=self.num_channels      ,							kernel_size=self.kernel_size      ,							stride=self.stride      ,							padding=self.padding      ,							patch_size=self.patch_size      ,							hidden_sizes=self.hidden_sizes      ,							num_attention_heads=self.num_attention_heads      ,							depths=self.depths      ,							key_dim=self.key_dim      ,							drop_path_rate=self.drop_path_rate      ,							mlp_ratio=self.mlp_ratio      ,							attention_ratio=self.attention_ratio      ,							initializer_range=self.initializer_range      ,							down_ops=self.down_ops      ,							)


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		LevitModel(config=a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     List[str]	=		model(a__)
             _lowerCamelCase							:     List[str]	=		(self.image_size, self.image_size)
             _lowerCamelCase,  _lowerCamelCase							:     Optional[int]	=		image_size[0], image_size[1]
             for _ in range(4):
                    _lowerCamelCase							:     int	=		floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
                    _lowerCamelCase							:     Dict	=		floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
             self.parent.assertEqual(
                 result.last_hidden_state.shape      ,							(self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1])      ,							)


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		self.num_labels
             _lowerCamelCase							:     Dict	=		LevitForImageClassification(a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     List[Any]	=		model(a__      ,							labels=a__)
             self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels))



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		self.prepare_config_and_inputs()
             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		config_and_inputs
             _lowerCamelCase							:     List[str]	=		{'''pixel_values''': pixel_values}
             return config, inputs_dict







@require_torch
class 			__A       (      lowerCamelCase__ ,lowerCamelCase__ ,unittest.TestCase  ):




      """simple docstring"""







      UpperCAmelCase__						=      (
          (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
          if is_torch_available()
          else ()
      )
      UpperCAmelCase__						=      (
          {
              """feature-extraction""": LevitModel,
              """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
          }
          if is_torch_available()
          else {}
      )

      UpperCAmelCase__						=      False
      UpperCAmelCase__						=      False
      UpperCAmelCase__						=      False
      UpperCAmelCase__						=      False
      UpperCAmelCase__						=      False


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		LevitModelTester(self)
             _lowerCamelCase							:     Tuple	=		ConfigTester(self      ,							config_class=a__      ,							has_text_modality=a__      ,							hidden_size=37)


      def 				__snake_case			(		self):



             """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 				__snake_case			(		self):



             """simple docstring"""





             return


      @unittest.skip(reason='''Levit does not use inputs_embeds''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      @unittest.skip(reason='''Levit does not support input and output embeddings''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      @unittest.skip(reason='''Levit does not output attentions''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Any	=		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _lowerCamelCase							:     Any	=		model_class(a__)
                    _lowerCamelCase							:     str	=		inspect.signature(model.forward)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    _lowerCamelCase							:     Dict	=		[*signature.parameters.keys()]

                    _lowerCamelCase							:     List[Any]	=		['''pixel_values''']
                    self.assertListEqual(arg_names[:1]      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             def check_hidden_states_output(a__      ,							a__      ,							a__):
                    _lowerCamelCase							:     List[Any]	=		model_class(a__)
                    model.to(a__)
                    model.eval()

                    with torch.no_grad():
                           _lowerCamelCase							:     Any	=		model(**self._prepare_for_class(a__      ,							a__))

                    _lowerCamelCase							:     int	=		outputs.hidden_states

                    _lowerCamelCase							:     List[str]	=		len(self.model_tester.depths) + 1
                    self.assertEqual(len(a__)      ,							a__)

                    _lowerCamelCase							:     Tuple	=		(self.model_tester.image_size, self.model_tester.image_size)
                    _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		image_size[0], image_size[1]
                    for _ in range(4):
                           _lowerCamelCase							:     Optional[Any]	=		floor(
                               (
                                   (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                                   / self.model_tester.stride
                               )
                               + 1)
                           _lowerCamelCase							:     List[Any]	=		floor(
                               (
                                   (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                                   / self.model_tester.stride
                               )
                               + 1)
                    # verify the first hidden states (first block)
                    self.assertListEqual(
                        list(hidden_states[0].shape[-2:])      ,							[
                            height * width,
                            self.model_tester.hidden_sizes[0],
                        ]      ,							)

             _lowerCamelCase,  _lowerCamelCase							:     Optional[int]	=		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _lowerCamelCase							:     List[Any]	=		True
                    check_hidden_states_output(a__      ,							a__      ,							a__)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    _lowerCamelCase							:     List[str]	=		True

                    check_hidden_states_output(a__      ,							a__      ,							a__)


      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__=False):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		super()._prepare_for_class(a__      ,							a__      ,							return_labels=a__)

             if return_labels:
                    if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                           del inputs_dict["labels"]

             return inputs_dict


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             if not self.model_tester.is_training:
                    return

             _lowerCamelCase,  _lowerCamelCase							:     List[Any]	=		self.model_tester.prepare_config_and_inputs_for_common()
             _lowerCamelCase							:     Optional[Any]	=		True

             for model_class in self.all_model_classes:
                    # LevitForImageClassificationWithTeacher supports inference-only
                    if (
                        model_class in get_values(a__)
                        or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                    ):
                           continue
                    _lowerCamelCase							:     Optional[Any]	=		model_class(a__)
                    model.to(a__)
                    model.train()
                    _lowerCamelCase							:     Any	=		self._prepare_for_class(a__      ,							a__      ,							return_labels=a__)
                    _lowerCamelCase							:     Tuple	=		model(**a__).loss
                    loss.backward()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		self.model_tester.prepare_config_and_inputs_for_common()
             if not self.model_tester.is_training:
                    return

             _lowerCamelCase							:     Union[str, Any]	=		False
             _lowerCamelCase							:     List[str]	=		True

             for model_class in self.all_model_classes:
                    if model_class in get_values(a__) or not model_class.supports_gradient_checkpointing:
                           continue
                    # LevitForImageClassificationWithTeacher supports inference-only
                    if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                           continue
                    _lowerCamelCase							:     List[Any]	=		model_class(a__)
                    model.gradient_checkpointing_enable()
                    model.to(a__)
                    model.train()
                    _lowerCamelCase							:     int	=		self._prepare_for_class(a__      ,							a__      ,							return_labels=a__)
                    _lowerCamelCase							:     Any	=		model(**a__).loss
                    loss.backward()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		self.model_tester.prepare_config_and_inputs_for_common()

             _lowerCamelCase							:     Dict	=		[
                 {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
                 {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
                 {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
             ]

             for model_class in self.all_model_classes:
                    if (
                        model_class
                        not in [
                            *get_values(a__),
                        ]
                        or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                    ):
                           continue

                    for problem_type in problem_types:
                           with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}"""):
                                  _lowerCamelCase							:     Tuple	=		problem_type['''title''']
                                  _lowerCamelCase							:     Tuple	=		problem_type['''num_labels''']

                                  _lowerCamelCase							:     int	=		model_class(a__)
                                  model.to(a__)
                                  model.train()

                                  _lowerCamelCase							:     int	=		self._prepare_for_class(a__      ,							a__      ,							return_labels=a__)

                                  if problem_type["num_labels"] > 1:
                                         _lowerCamelCase							:     int	=		inputs['''labels'''].unsqueeze(1).repeat(1      ,							problem_type['''num_labels'''])

                                  _lowerCamelCase							:     Tuple	=		inputs['''labels'''].to(problem_type['''dtype'''])

                                  # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                                  # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                                  # they have the same size." which is a symptom something in wrong for the regression problem.
                                  # See https://github.com/huggingface/transformers/issues/11780
                                  with warnings.catch_warnings(record=a__) as warning_list:
                                         _lowerCamelCase							:     Any	=		model(**a__).loss
                                  for w in warning_list:
                                         if "Using a target size that is different to the input size" in str(w.message):
                                                raise ValueError(
                                                    F"""Something is going wrong in the regression problem: intercepted {w.message}""")

                                  loss.backward()



      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase							:     int	=		LevitModel.from_pretrained(a__)
                    self.assertIsNotNone(a__)




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     int	=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       return image







@require_torch
@require_vision
class 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      @cached_property
      def 				__snake_case			(		self):



             """simple docstring"""





             return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])



      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
                 a__)

             _lowerCamelCase							:     int	=		self.default_image_processor
             _lowerCamelCase							:     int	=		prepare_img()
             _lowerCamelCase							:     str	=		image_processor(images=a__      ,							return_tensors='''pt''').to(a__)

             # forward pass
             with torch.no_grad():
                    _lowerCamelCase							:     int	=		model(**a__)

             # verify the logits
             _lowerCamelCase							:     List[str]	=		torch.Size((1, 1000))
             self.assertEqual(outputs.logits.shape      ,							a__)

             _lowerCamelCase							:     Optional[Any]	=		torch.tensor([1.0448, -0.3745, -1.8317]).to(a__)

             self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,							a__      ,							atol=1e-4))

