


'''simple docstring'''
import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_MAPPING,
     BeitForImageClassification,
     BeitForMaskedImageModeling,
     BeitForSemanticSegmentation,
     BeitModel,
 )
 from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 import PIL
 from PIL import Image

 from transformers import BeitImageProcessor

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase=100   ,   __lowercase=13   ,   __lowercase=30   ,   __lowercase=2   ,   __lowercase=3   ,   __lowercase=True   ,   __lowercase=True   ,   __lowercase=32   ,   __lowercase=4   ,   __lowercase=4   ,   __lowercase=37   ,   __lowercase="gelu"   ,   __lowercase=0.1   ,   __lowercase=0.1   ,   __lowercase=10   ,   __lowercase=0.0_2   ,   __lowercase=3   ,   __lowercase=None   ,   __lowercase=[0, 1, 2, 3]   ,   ):

  """simple docstring"""







  __A	:		Dict	  =      parent
  __A	:		int	  =      100
  __A	:		int	  =      batch_size
  __A	:		str	  =      image_size
  __A	:		Dict	  =      patch_size
  __A	:		Dict	  =      num_channels
  __A	:		Optional[int]	  =      is_training
  __A	:		List[str]	  =      use_labels
  __A	:		Dict	  =      hidden_size
  __A	:		List[str]	  =      num_hidden_layers
  __A	:		Optional[int]	  =      num_attention_heads
  __A	:		str	  =      intermediate_size
  __A	:		Union[str, Any]	  =      hidden_act
  __A	:		int	  =      hidden_dropout_prob
  __A	:		Optional[Any]	  =      attention_probs_dropout_prob
  __A	:		Dict	  =      type_sequence_label_size
  __A	:		Dict	  =      initializer_range
  __A	:		str	  =      scope
  __A	:		Tuple	  =      out_indices
  __A	:		List[str]	  =      num_labels

  # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
  __A	:		List[str]	  =      (image_size // patch_size) ** 2
  __A	:		List[Any]	  =      num_patches + 1


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

  __A	:		Dict	  =      None
  __A	:		Tuple	  =      None
  if self.use_labels:
   __A	:		List[Any]	  =      ids_tensor([self.batch_size]   ,   self.type_sequence_label_size    )
   __A	:		List[Any]	  =      ids_tensor([self.batch_size, self.image_size, self.image_size]   ,   self.num_labels    )

  __A	:		Any	  =      self.get_config()

  return config, pixel_values, labels, pixel_labels


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return BeitConfig(
      vocab_size=self.vocab_size   ,   image_size=self.image_size   ,   patch_size=self.patch_size   ,   num_channels=self.num_channels   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   is_decoder=__lowercase   ,   initializer_range=self.initializer_range   ,   out_indices=self.out_indices   ,   )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      BeitModel(config=__lowercase    )
  model.to(__lowercase    )
  model.eval()
  __A	:		int	  =      model(__lowercase    )
  self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		int	  =      BeitForMaskedImageModeling(config=__lowercase    )
  model.to(__lowercase    )
  model.eval()
  __A	:		str	  =      model(__lowercase    )
  self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length - 1, self.vocab_size)    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      self.type_sequence_label_size
  __A	:		Any	  =      BeitForImageClassification(__lowercase    )
  model.to(__lowercase    )
  model.eval()
  __A	:		Dict	  =      model(__lowercase   ,   labels=__lowercase    )
  self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)    )

  # test greyscale images
  __A	:		List[str]	  =      1
  __A	:		int	  =      BeitForImageClassification(__lowercase    )
  model.to(__lowercase    )
  model.eval()

  __A	:		Dict	  =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
  __A	:		str	  =      model(__lowercase   ,   labels=__lowercase    )
  self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.num_labels
  __A	:		List[str]	  =      BeitForSemanticSegmentation(__lowercase    )
  model.to(__lowercase    )
  model.eval()
  __A	:		Optional[Any]	  =      model(__lowercase    )
  self.parent.assertEqual(
      result.logits.shape   ,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)    )
  __A	:		int	  =      model(__lowercase   ,   labels=__lowercase    )
  self.parent.assertEqual(
      result.logits.shape   ,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)    )







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      self.prepare_config_and_inputs()
  __A  ,__A  ,__A  ,__A	:		Dict	  =      config_and_inputs
  __A	:		List[Any]	  =      {'pixel_values': pixel_values}
  return config, inputs_dict


@require_torch
class     _lowerCamelCase       (     snake_case_     ,		snake_case_     ,		unittest.TestCase					):




 '''simple docstring'''
 __lowercase					:		Optional[Any]     				=    (
     (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 __lowercase					:		Union[str, Any]     				=    (
     {
         '''feature-extraction''': BeitModel,
         '''image-classification''': BeitForImageClassification,
         '''image-segmentation''': BeitForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __lowercase					:		Optional[int]     				=    False
 __lowercase					:		str     				=    False
 __lowercase					:		Dict     				=    False


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      BeitModelTester(self    )
  __A	:		Union[str, Any]	  =      ConfigTester(self   ,   config_class=__lowercase   ,   has_text_modality=__lowercase   ,   hidden_size=37    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self.config_tester.run_common_tests()


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

  """simple docstring"""







  pass


 @require_torch_multi_gpu
 @unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'    )
 def 			snake_case__ (   self    ):

  """simple docstring"""







  pass


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A  ,__A	:		List[Any]	  =      self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __A	:		int	  =      model_class(__lowercase    )
   self.assertIsInstance(model.get_input_embeddings()   ,   (nn.Module)    )
   __A	:		Tuple	  =      model.get_output_embeddings()
   self.assertTrue(x is None or isinstance(__lowercase   ,   nn.Linear    )    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







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

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

   __A	:		Optional[int]	  =      ['pixel_values']
   self.assertListEqual(arg_names[:1]   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		int	  =      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		str	  =      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  if not self.model_tester.is_training:
   return

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

  for model_class in self.all_model_classes:
   # we don't test BeitForMaskedImageModeling
   if model_class in [*get_values(__lowercase    ), BeitForMaskedImageModeling]:
    continue

   __A	:		Dict	  =      model_class(__lowercase    )
   model.to(__lowercase    )
   model.train()
   __A	:		Optional[int]	  =      self._prepare_for_class(__lowercase   ,   __lowercase   ,   return_labels=__lowercase    )
   __A	:		List[Any]	  =      model(**__lowercase    ).loss
   loss.backward()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A  ,__A	:		str	  =      self.model_tester.prepare_config_and_inputs_for_common()
  if not self.model_tester.is_training:
   return

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

  for model_class in self.all_model_classes:
   # we don't test BeitForMaskedImageModeling
   if (
       model_class in [*get_values(__lowercase    ), BeitForMaskedImageModeling]
       or not model_class.supports_gradient_checkpointing
   ):
    continue

   __A	:		Any	  =      model_class(__lowercase    )
   model.gradient_checkpointing_enable()
   model.to(__lowercase    )
   model.train()
   __A	:		Any	  =      self._prepare_for_class(__lowercase   ,   __lowercase   ,   return_labels=__lowercase    )
   __A	:		Tuple	  =      model(**__lowercase    ).loss
   loss.backward()


 def 			snake_case__ (   self    ):

  """simple docstring"""







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

  __A	:		int	  =      _config_zero_init(__lowercase    )
  for model_class in self.all_model_classes:
   __A	:		Optional[Any]	  =      model_class(config=__lowercase    )
   for name, param in model.named_parameters():
    # we skip lambda parameters as these require special initial values
    # determined by config.layer_scale_init_value
    if "lambda" in name:
     continue
    if param.requires_grad:
     self.assertIn(
         ((param.data.mean() * 1E9).round() / 1E9).item()   ,   [0.0, 1.0]   ,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""   ,   )







 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __A	:		int	  =      BeitModel.from_pretrained(__lowercase    )
   self.assertIsNotNone(__lowercase    )


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


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




 '''simple docstring'''


 @cached_property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'    ) if is_vision_available() else None


 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'    ).to(__lowercase    )

  __A	:		Optional[Any]	  =      self.default_image_processor
  __A	:		Union[str, Any]	  =      prepare_img()
  __A	:		List[Any]	  =      image_processor(images=__lowercase   ,   return_tensors='pt'    ).pixel_values.to(__lowercase    )

  # prepare bool_masked_pos
  __A	:		Tuple	  =      torch.ones((1, 196)   ,   dtype=torch.bool    ).to(__lowercase    )

  # forward pass
  with torch.no_grad():
   __A	:		Tuple	  =      model(pixel_values=__lowercase   ,   bool_masked_pos=__lowercase    )
  __A	:		str	  =      outputs.logits

  # verify the logits
  __A	:		List[Any]	  =      torch.Size((1, 196, 8_192)    )
  self.assertEqual(logits.shape   ,   __lowercase    )

  __A	:		Optional[int]	  =      torch.tensor(
      [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]    ).to(__lowercase    )

  self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]   ,   __lowercase   ,   atol=1E-2    )    )


 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'    ).to(__lowercase    )

  __A	:		Union[str, Any]	  =      self.default_image_processor
  __A	:		List[Any]	  =      prepare_img()
  __A	:		Optional[Any]	  =      image_processor(images=__lowercase   ,   return_tensors='pt'    ).to(__lowercase    )

  # forward pass
  with torch.no_grad():
   __A	:		Any	  =      model(**__lowercase    )
  __A	:		Optional[Any]	  =      outputs.logits

  # verify the logits
  __A	:		List[str]	  =      torch.Size((1, 1_000)    )
  self.assertEqual(logits.shape   ,   __lowercase    )

  __A	:		Optional[Any]	  =      torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]    ).to(__lowercase    )

  self.assertTrue(torch.allclose(logits[0, :3]   ,   __lowercase   ,   atol=1E-4    )    )

  __A	:		Dict	  =      281
  self.assertEqual(logits.argmax(-1    ).item()   ,   __lowercase    )


 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'    ).to(
      __lowercase    )

  __A	:		Union[str, Any]	  =      self.default_image_processor
  __A	:		Union[str, Any]	  =      prepare_img()
  __A	:		List[str]	  =      image_processor(images=__lowercase   ,   return_tensors='pt'    ).to(__lowercase    )

  # forward pass
  with torch.no_grad():
   __A	:		List[str]	  =      model(**__lowercase    )
  __A	:		str	  =      outputs.logits

  # verify the logits
  __A	:		List[str]	  =      torch.Size((1, 21_841)    )
  self.assertEqual(logits.shape   ,   __lowercase    )

  __A	:		Dict	  =      torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]    ).to(__lowercase    )

  self.assertTrue(torch.allclose(logits[0, :3]   ,   __lowercase   ,   atol=1E-4    )    )

  __A	:		Union[str, Any]	  =      2_396
  self.assertEqual(logits.argmax(-1    ).item()   ,   __lowercase    )


 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'    )
  __A	:		Any	  =      model.to(__lowercase    )

  __A	:		List[str]	  =      BeitImageProcessor(do_resize=__lowercase   ,   size=640   ,   do_center_crop=__lowercase    )

  __A	:		Optional[Any]	  =      load_dataset('hf-internal-testing/fixtures_ade20k'   ,   split='test'    )
  __A	:		int	  =      Image.open(ds[0]['file']    )
  __A	:		Union[str, Any]	  =      image_processor(images=__lowercase   ,   return_tensors='pt'    ).to(__lowercase    )

  # forward pass
  with torch.no_grad():
   __A	:		Optional[Any]	  =      model(**__lowercase    )
  __A	:		List[str]	  =      outputs.logits

  # verify the logits
  __A	:		int	  =      torch.Size((1, 150, 160, 160)    )
  self.assertEqual(logits.shape   ,   __lowercase    )

  __A	:		Dict	  =      version.parse(PIL.__version__    ) < version.parse('9.0.0'    )

  if is_pillow_less_than_a:
   __A	:		Union[str, Any]	  =      torch.tensor(
       [
           [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
           [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
           [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
       ]   ,   device=__lowercase   ,   )
  else:
   __A	:		Optional[int]	  =      torch.tensor(
       [
           [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
           [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
           [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
       ]   ,   device=__lowercase   ,   )

  self.assertTrue(torch.allclose(logits[0, :3, :3, :3]   ,   __lowercase   ,   atol=1E-4    )    )







 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		str	  =      BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'    )
  __A	:		Optional[Any]	  =      model.to(__lowercase    )

  __A	:		str	  =      BeitImageProcessor(do_resize=__lowercase   ,   size=640   ,   do_center_crop=__lowercase    )

  __A	:		Optional[int]	  =      load_dataset('hf-internal-testing/fixtures_ade20k'   ,   split='test'    )
  __A	:		int	  =      Image.open(ds[0]['file']    )
  __A	:		Any	  =      image_processor(images=__lowercase   ,   return_tensors='pt'    ).to(__lowercase    )

  # forward pass
  with torch.no_grad():
   __A	:		Dict	  =      model(**__lowercase    )

  __A	:		Tuple	  =      outputs.logits.detach().cpu()

  __A	:		Optional[Any]	  =      image_processor.post_process_semantic_segmentation(outputs=__lowercase   ,   target_sizes=[(500, 300)]    )
  __A	:		Tuple	  =      torch.Size((500, 300)    )
  self.assertEqual(segmentation[0].shape   ,   __lowercase    )

  __A	:		str	  =      image_processor.post_process_semantic_segmentation(outputs=__lowercase    )
  __A	:		int	  =      torch.Size((160, 160)    )
  self.assertEqual(segmentation[0].shape   ,   __lowercase    )










'''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 argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def       _lowercase							(					):
 __A	:		Dict	  =      ArgumentParser('Accelerate CLI tool',					usage='accelerate <command> [<args>]',					allow_abbrev=UpperCamelCase__      )
 __A	:		Any	  =      parser.add_subparsers(help='accelerate command helpers'      )

 # Register commands
 get_config_parser(subparsers=UpperCamelCase__      )
 env_command_parser(subparsers=UpperCamelCase__      )
 launch_command_parser(subparsers=UpperCamelCase__      )
 tpu_command_parser(subparsers=UpperCamelCase__      )
 test_command_parser(subparsers=UpperCamelCase__      )

 # Let's go
 __A	:		Optional[Any]	  =      parser.parse_args()

 if not hasattr(UpperCamelCase__,					'func'      ):
  parser.print_help()
  exit(1      )

 # Run
 args.func(UpperCamelCase__      )


if __name__ == "__main__":
 main()






