



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  __snake_case      :







							'''simple docstring'''






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

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


							def _a      (							self		):
											a__				      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

											a__				      =		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=a_  ,  initializer_range=self.initializer_range  ,  mask_ratio=self.mask_ratio  ,  )


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		ViTMAEModel(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(a_		)
											self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)		)


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

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







							def _a      (							self		):
											a__				      =		self.prepare_config_and_inputs()
											a__      ,			a__      ,			a__				      =		config_and_inputs
											a__				      =		{"""pixel_values""": pixel_values}
											return config, inputs_dict




@require_torch
class  __snake_case      (   SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							UpperCamelCase__				:      int         =					{"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

							UpperCamelCase__				:      Tuple         =					False
							UpperCamelCase__				:      int         =					False
							UpperCamelCase__				:      List[str]         =					False
							UpperCamelCase__				:      List[str]         =					False


							def _a      (							self		):
											a__				      =		ViTMAEModelTester(self		)
											a__				      =		ConfigTester(self  ,  config_class=a_  ,  has_text_modality=a_  ,  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		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()

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


							def _a      (							self		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()

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

															a__				      =		["""pixel_values"""]
															self.assertListEqual(arg_names[:1]  ,  a_		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*a_		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*a_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											# make masks reproducible
											np.random.seed(2		)

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

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

											super().check_pt_tf_models(a_  ,  a_  ,  a_		)


							def _a      (							self		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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


							@unittest.skip(
							    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    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
    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
    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]:
															a__				      =		ViTMAEModel.from_pretrained(a_		)
															self.assertIsNotNone(a_		)




def 			A_      (	):

				"""simple docstring"""
				a__				      =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
				return image




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







							'''simple docstring'''






							@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		)

											a__				      =		ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""		).to(a_		)

											a__				      =		self.default_image_processor
											a__				      =		prepare_img()
											a__				      =		image_processor(images=a_  ,  return_tensors="""pt"""		).to(a_		)

											# 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)
											a__				      =		ViTMAEConfig()
											a__				      =		int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
											a__				      =		np.random.uniform(size=(1, num_patches)		)

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

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

											a__				      =		torch.tensor(
											    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]		)

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






# 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							 =					"""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)

