



import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
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 (
	    FocalNetBackbone,
	    FocalNetForImageClassification,
	    FocalNetForMaskedImageModeling,
	    FocalNetModel,
	)
	from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor

class  __snake_case      :







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=13  ,  a_=32  ,  a_=2  ,  a_=3  ,  a_=16  ,  a_=[32, 64, 128]  ,  a_=[1, 2, 1]  ,  a_=[2, 2, 4]  ,  a_=2  ,  a_=2.0  ,  a_=True  ,  a_=0.0  ,  a_=0.0  ,  a_=0.1  ,  a_="gelu"  ,  a_=False  ,  a_=True  ,  a_=0.02  ,  a_=1E-5  ,  a_=True  ,  a_=None  ,  a_=True  ,  a_=10  ,  a_=8  ,  a_=["stage1", "stage2"]  ,  a_=[1, 2]  ,  ):
											a__				      =		parent
											a__				      =		batch_size
											a__				      =		image_size
											a__				      =		patch_size
											a__				      =		num_channels
											a__				      =		embed_dim
											a__				      =		hidden_sizes
											a__				      =		depths
											a__				      =		num_heads
											a__				      =		window_size
											a__				      =		mlp_ratio
											a__				      =		qkv_bias
											a__				      =		hidden_dropout_prob
											a__				      =		attention_probs_dropout_prob
											a__				      =		drop_path_rate
											a__				      =		hidden_act
											a__				      =		use_absolute_embeddings
											a__				      =		patch_norm
											a__				      =		layer_norm_eps
											a__				      =		initializer_range
											a__				      =		is_training
											a__				      =		scope
											a__				      =		use_labels
											a__				      =		type_sequence_label_size
											a__				      =		encoder_stride
											a__				      =		out_features
											a__				      =		out_indices


							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 FocalNetConfig(
											    image_size=self.image_size  ,  patch_size=self.patch_size  ,  num_channels=self.num_channels  ,  embed_dim=self.embed_dim  ,  hidden_sizes=self.hidden_sizes  ,  depths=self.depths  ,  num_heads=self.num_heads  ,  window_size=self.window_size  ,  mlp_ratio=self.mlp_ratio  ,  qkv_bias=self.qkv_bias  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,  drop_path_rate=self.drop_path_rate  ,  hidden_act=self.hidden_act  ,  use_absolute_embeddings=self.use_absolute_embeddings  ,  path_norm=self.patch_norm  ,  layer_norm_eps=self.layer_norm_eps  ,  initializer_range=self.initializer_range  ,  encoder_stride=self.encoder_stride  ,  out_features=self.out_features  ,  out_indices=self.out_indices  ,  )


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		FocalNetModel(config=__UpperCamelCase		)
											model.to(__UpperCamelCase		)
											model.eval()
											a__				      =		model(__UpperCamelCase		)

											a__				      =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
											a__				      =		int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

											self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, expected_seq_len, expected_dim)		)


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		FocalNetBackbone(config=__UpperCamelCase		)
											model.to(__UpperCamelCase		)
											model.eval()
											a__				      =		model(__UpperCamelCase		)

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps		)  ,  len(config.out_features		)		)
											self.parent.assertListEqual(list(result.feature_maps[0].shape		)  ,  [self.batch_size, self.image_size, 8, 8]		)

											# verify channels
											self.parent.assertEqual(len(model.channels		)  ,  len(config.out_features		)		)
											self.parent.assertListEqual(model.channels  ,  config.hidden_sizes[:-1]		)

											# verify backbone works with out_features=None
											a__				      =		None
											a__				      =		FocalNetBackbone(config=__UpperCamelCase		)
											model.to(__UpperCamelCase		)
											model.eval()
											a__				      =		model(__UpperCamelCase		)

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps		)  ,  1		)
											self.parent.assertListEqual(list(result.feature_maps[0].shape		)  ,  [self.batch_size, self.image_size * 2, 4, 4]		)

											# verify channels
											self.parent.assertEqual(len(model.channels		)  ,  1		)
											self.parent.assertListEqual(model.channels  ,  [config.hidden_sizes[-1]]		)


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		FocalNetForMaskedImageModeling(config=__UpperCamelCase		)
											model.to(__UpperCamelCase		)
											model.eval()
											a__				      =		model(__UpperCamelCase		)
											self.parent.assertEqual(
											    result.reconstruction.shape  ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

											# test greyscale images
											a__				      =		1
											a__				      =		FocalNetForMaskedImageModeling(__UpperCamelCase		)
											model.to(__UpperCamelCase		)
											model.eval()

											a__				      =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
											a__				      =		model(__UpperCamelCase		)
											self.parent.assertEqual(result.reconstruction.shape  ,  (self.batch_size, 1, self.image_size, self.image_size)		)


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		self.type_sequence_label_size
											a__				      =		FocalNetForImageClassification(__UpperCamelCase		)
											model.to(__UpperCamelCase		)
											model.eval()
											a__				      =		model(__UpperCamelCase  ,  labels=__UpperCamelCase		)
											self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.type_sequence_label_size)		)

											# test greyscale images
											a__				      =		1
											a__				      =		FocalNetForImageClassification(__UpperCamelCase		)
											model.to(__UpperCamelCase		)
											model.eval()

											a__				      =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
											a__				      =		model(__UpperCamelCase		)
											self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.type_sequence_label_size)		)







							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      (   _UpperCAmelCase			,_UpperCAmelCase			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      str         =					(
							    (
							        FocalNetModel,
							        FocalNetForImageClassification,
							        FocalNetForMaskedImageModeling,
							        FocalNetBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase__				:      int         =					(
							    {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
							    if is_torch_available()
							    else {}
							)
							UpperCamelCase__				:      Optional[Any]         =					False

							UpperCamelCase__				:      Dict         =					False
							UpperCamelCase__				:      List[Any]         =					False
							UpperCamelCase__				:      int         =					False
							UpperCamelCase__				:      Optional[Any]         =					False


							def _a      (							self		):
											a__				      =		FocalNetModelTester(self		)
											a__				      =		ConfigTester(self  ,  config_class=__UpperCamelCase  ,  embed_dim=37  ,  has_text_modality=__UpperCamelCase		)


							def _a      (							self		):
											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 _a      (							self		):
											return


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


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*__UpperCamelCase		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase		)


							@unittest.skip(reason="""FocalNet does not use inputs_embeds"""		)
							def _a      (							self		):
											pass


							@unittest.skip(reason="""FocalNet does not use feedforward chunking"""		)
							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[:-1]:
															a__				      =		model_class(__UpperCamelCase		)
															self.assertIsInstance(model.get_input_embeddings()  ,  (nn.Module)		)
															a__				      =		model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(__UpperCamelCase  ,  nn.Linear		)		)


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

											for model_class in self.all_model_classes[:-1]:
															a__				      =		model_class(__UpperCamelCase		)
															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]  ,  __UpperCamelCase		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		model_class(__UpperCamelCase		)
											model.to(__UpperCamelCase		)
											model.eval()

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

											a__				      =		outputs.hidden_states

											a__				      =		getattr(
											    self.model_tester  ,  """expected_num_hidden_layers"""  ,  len(self.model_tester.depths		) + 1		)
											self.assertEqual(len(__UpperCamelCase		)  ,  __UpperCamelCase		)

											# FocalNet has a different seq_length
											a__				      =		(
											    config.patch_size
											    if isinstance(config.patch_size  ,  collections.abc.Iterable		)
											    else (config.patch_size, config.patch_size)
											)

											a__				      =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]		)  ,  [num_patches, self.model_tester.embed_dim]  ,  )

											a__				      =		outputs.reshaped_hidden_states
											self.assertEqual(len(__UpperCamelCase		)  ,  __UpperCamelCase		)

											a__      ,			a__      ,			a__      ,			a__				      =		reshaped_hidden_states[0].shape
											a__				      =		(
											    reshaped_hidden_states[0].view(__UpperCamelCase  ,  __UpperCamelCase  ,  height * width		).permute(0  ,  2  ,  1		)
											)
											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]		)  ,  [num_patches, self.model_tester.embed_dim]  ,  )


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

											a__				      =		(
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size  ,  collections.abc.Iterable		)
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)

											for model_class in self.all_model_classes[:-1]:
															a__				      =		True
															self.check_hidden_states_output(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase		)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															a__				      =		True

															self.check_hidden_states_output(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase		)


							def _a      (							self		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()
											a__				      =		3

											a__				      =		(
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size  ,  collections.abc.Iterable		)
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)
											a__				      =		(
											    config.patch_size
											    if isinstance(config.patch_size  ,  collections.abc.Iterable		)
											    else (config.patch_size, config.patch_size)
											)

											a__				      =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											a__				      =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes[:-1]:
															a__				      =		True
															self.check_hidden_states_output(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  (padded_height, padded_width)		)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															a__				      =		True
															self.check_hidden_states_output(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  (padded_height, padded_width)		)


							@slow
							def _a      (							self		):






											for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															a__				      =		FocalNetModel.from_pretrained(__UpperCamelCase		)
															self.assertIsNotNone(__UpperCamelCase		)







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

											a__				      =		_config_zero_init(__UpperCamelCase		)
											for model_class in self.all_model_classes:
															a__				      =		model_class(config=__UpperCamelCase		)
															for name, param in model.named_parameters():
																			if "embeddings" not in name and 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'''  ,  )




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







							'''simple docstring'''






							@cached_property
							def _a      (							self		):
											return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""		) if is_vision_available() else None







							@slow
							def _a      (							self		):
											a__				      =		FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""		).to(__UpperCamelCase		)
											a__				      =		self.default_image_processor

											a__				      =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
											a__				      =		image_processor(images=__UpperCamelCase  ,  return_tensors="""pt"""		).to(__UpperCamelCase		)

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

											# verify the logits
											a__				      =		torch.Size((1, 1_000)		)
											self.assertEqual(outputs.logits.shape  ,  __UpperCamelCase		)
											a__				      =		torch.tensor([0.2_166, -0.4_368, 0.2_191]		).to(__UpperCamelCase		)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,  __UpperCamelCase  ,  atol=1E-4		)		)
											self.assertTrue(outputs.logits.argmax(dim=-1		).item()  ,  281		)




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







							'''simple docstring'''






							UpperCamelCase__				:      List[str]         =					(FocalNetBackbone,) if is_torch_available() else ()
							UpperCamelCase__				:      Tuple         =					FocalNetConfig

							UpperCamelCase__				:      Optional[Any]         =					False


							def _a      (							self		):
											a__				      =		FocalNetModelTester(self		)






# 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)

