

# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


_UpperCAmelCase						:  Union[str, Any]									=			"""3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
					import torch

					print("""Torch version:""", torch.__version__)
					print("""Cuda available:""", torch.cuda.is_available())
					print("""Cuda version:""", torch.version.cuda)
					print("""CuDNN version:""", torch.backends.cudnn.version())
					print("""Number of GPUs available:""", torch.cuda.device_count())
					print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
					print("""Torch version:""", None)

try:
					import deepspeed

					print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
					print("""DeepSpeed version:""", None)

try:
					import tensorflow as tf

					print("""TensorFlow version:""", tf.__version__)
					print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
					print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
					print("""TensorFlow version:""", None)







import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import ViTMAEForPreTraining, ViTMAEModel
					from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor







class 					lowercase							:




				def __init__(   self ,  snake_case ,  snake_case=13 ,  snake_case=30 ,  snake_case=2 ,  snake_case=3 ,  snake_case=True ,  snake_case=True ,  snake_case=32 ,  snake_case=5 ,  snake_case=4 ,  snake_case=37 ,  snake_case="gelu" ,  snake_case=0.1 ,  snake_case=0.1 ,  snake_case=10 ,  snake_case=0.02 ,  snake_case=3 ,  snake_case=0.6 ,  snake_case=None ,  ):
											snake_case_     							=       parent
											snake_case_     							=       batch_size
											snake_case_     							=       image_size
											snake_case_     							=       patch_size
											snake_case_     							=       num_channels
											snake_case_     							=       is_training
											snake_case_     							=       use_labels
											snake_case_     							=       hidden_size
											snake_case_     							=       num_hidden_layers
											snake_case_     							=       num_attention_heads
											snake_case_     							=       intermediate_size
											snake_case_     							=       hidden_act
											snake_case_     							=       hidden_dropout_prob
											snake_case_     							=       attention_probs_dropout_prob
											snake_case_     							=       type_sequence_label_size
											snake_case_     							=       initializer_range
											snake_case_     							=       mask_ratio
											snake_case_     							=       scope

											# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
											# (we add 1 for the [CLS] token)
											snake_case_     							=       (image_size // patch_size) ** 2
											snake_case_     							=       int(math.ceil((1 - mask_ratio) * (num_patches + 1)			)			)




				def        a				(   self			):
											snake_case_     							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

											snake_case_     							=       None
											if self.use_labels:
																		snake_case_     							=       ids_tensor([self.batch_size] ,  self.type_sequence_label_size			)

											snake_case_     							=       self.get_config()

											return config, pixel_values, labels




				def        a				(   self			):
											return ViTMAEConfig(
											    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=snake_case ,  initializer_range=self.initializer_range ,  mask_ratio=self.mask_ratio ,  )




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       ViTMAEModel(config=snake_case			)
											model.to(snake_case			)
											model.eval()
											snake_case_     							=       model(snake_case			)
											self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)			)




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       ViTMAEForPreTraining(snake_case			)
											model.to(snake_case			)
											model.eval()
											snake_case_     							=       model(snake_case			)
											snake_case_     							=       (self.image_size // self.patch_size) ** 2
											snake_case_     							=       self.patch_size**2 * self.num_channels
											self.parent.assertEqual(result.logits.shape ,  (self.batch_size, num_patches, expected_num_channels)			)

											# test greyscale images
											snake_case_     							=       1
											snake_case_     							=       ViTMAEForPreTraining(snake_case			)
											model.to(snake_case			)
											model.eval()
											snake_case_     							=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
											snake_case_     							=       model(snake_case			)
											snake_case_     							=       self.patch_size**2
											self.parent.assertEqual(result.logits.shape ,  (self.batch_size, num_patches, expected_num_channels)			)




				def        a				(   self			):
											snake_case_     							=       self.prepare_config_and_inputs()
											snake_case_							,       snake_case_							,       snake_case_     							=       config_and_inputs
											snake_case_     							=       {'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
class 					lowercase							(			lowercase_      ,    lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					Dict			     =     (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     False
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     False
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     False
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     False




				def        a				(   self			):
											snake_case_     							=       ViTMAEModelTester(self			)
											snake_case_     							=       ConfigTester(self ,  config_class=snake_case ,  has_text_modality=snake_case ,  hidden_size=37			)




				def        a				(   self			):
											self.config_tester.run_common_tests()




				@unittest.skip(reason='ViTMAE does not use inputs_embeds'			)
				def        a				(   self			):
											pass




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()

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




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()

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

																		snake_case_     							=       ['pixel_values']
																		self.assertListEqual(arg_names[:1] ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*snake_case			)




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											# make masks reproducible
											np.random.seed(2			)

											snake_case_     							=       int((pt_model.config.image_size // pt_model.config.patch_size) ** 2			)
											snake_case_     							=       np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
											snake_case_     							=       torch.from_numpy(snake_case			)

											# Add `noise` argument.
											# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
											snake_case_     							=       pt_noise

											super().check_pt_tf_models(snake_case ,  snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		snake_case_     							=       model_class(snake_case			)
																		model.to(snake_case			)
																		model.eval()
																		# make random mask reproducible
																		torch.manual_seed(2			)
																		with torch.no_grad():
																									snake_case_     							=       model(**self._prepare_for_class(snake_case ,  snake_case			)			)

																		snake_case_     							=       outputs[0].cpu().numpy()
																		snake_case_     							=       0

																		with tempfile.TemporaryDirectory() as tmpdirname:
																									model.save_pretrained(snake_case			)
																									snake_case_     							=       model_class.from_pretrained(snake_case			)
																									model.to(snake_case			)
																									# make random mask reproducible
																									torch.manual_seed(2			)
																									with torch.no_grad():
																																snake_case_     							=       model(**self._prepare_for_class(snake_case ,  snake_case			)			)

																									# Make sure we don't have nans
																									snake_case_     							=       after_outputs[0].cpu().numpy()
																									snake_case_     							=       0
																									snake_case_     							=       np.amax(np.abs(out_a - out_a			)			)
																									self.assertLessEqual(snake_case ,  1e-5			)




				@unittest.skip(
				    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'			)
				def        a				(   self			):
											pass




				@unittest.skip(
				    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'			)
				def        a				(   self			):
											pass




				@unittest.skip(
				    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'			)
				def        a				(   self			):
											pass




				@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'			)
				def        a				(   self			):
											pass




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




				@slow
				def        a				(   self			):
											for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_     							=       ViTMAEModel.from_pretrained(snake_case			)
																		self.assertIsNotNone(snake_case			)





def    __lowerCamelCase      (					):







							'''simple docstring'''






							snake_case_     							=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
							return image







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




				@cached_property
				def        a				(   self			):
											return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'			) if is_vision_available() else None




				@slow
				def        a				(   self			):
											# make random mask reproducible across the PT and TF model
											np.random.seed(2			)

											snake_case_     							=       ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'			).to(snake_case			)

											snake_case_     							=       self.default_image_processor
											snake_case_     							=       prepare_img()
											snake_case_     							=       image_processor(images=snake_case ,  return_tensors='pt'			).to(snake_case			)

											# prepare a noise vector that will be also used for testing the TF model
											# (this way we can ensure that the PT and TF models operate on the same inputs)
											snake_case_     							=       ViTMAEConfig()
											snake_case_     							=       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
											snake_case_     							=       np.random.uniform(size=(1, num_patches)			)

											# forward pass
											with torch.no_grad():
																		snake_case_     							=       model(**snake_case ,  noise=torch.from_numpy(snake_case			).to(device=snake_case			)			)

											# verify the logits
											snake_case_     							=       torch.Size((1, 196, 768)			)
											self.assertEqual(outputs.logits.shape ,  snake_case			)

											snake_case_     							=       torch.tensor(
											    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]			)

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




