



"""simple docstring"""
import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
			from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor


class 							lowerCamelCase_:

					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=1_3				,       lowerCamelCase__=3_2				,       lowerCamelCase__=2				,       lowerCamelCase__=3				,       lowerCamelCase__=1_6				,       lowerCamelCase__=[1, 2, 1]				,       lowerCamelCase__=[2, 2, 4]				,       lowerCamelCase__=2				,       lowerCamelCase__=2.0				,       lowerCamelCase__=True				,       lowerCamelCase__=0.0				,       lowerCamelCase__=0.0				,       lowerCamelCase__=0.1				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=False				,       lowerCamelCase__=True				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=1e-5				,       lowerCamelCase__=True				,       lowerCamelCase__=None				,       lowerCamelCase__=True				,       lowerCamelCase__=1_0				,       lowerCamelCase__=8				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				image_size
									_lowerCamelCase        =				patch_size
									_lowerCamelCase        =				num_channels
									_lowerCamelCase        =				embed_dim
									_lowerCamelCase        =				depths
									_lowerCamelCase        =				num_heads
									_lowerCamelCase        =				window_size
									_lowerCamelCase        =				mlp_ratio
									_lowerCamelCase        =				qkv_bias
									_lowerCamelCase        =				hidden_dropout_prob
									_lowerCamelCase        =				attention_probs_dropout_prob
									_lowerCamelCase        =				drop_path_rate
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				use_absolute_embeddings
									_lowerCamelCase        =				patch_norm
									_lowerCamelCase        =				layer_norm_eps
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				scope
									_lowerCamelCase        =				use_labels
									_lowerCamelCase        =				type_sequence_label_size
									_lowerCamelCase        =				encoder_stride

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

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

									_lowerCamelCase        =				self.get_config()

									return config, pixel_values, labels

					def 			snake_case__      (    self   ):
									return SwinvaConfig(
									    image_size=self.image_size				,       patch_size=self.patch_size				,       num_channels=self.num_channels				,       embed_dim=self.embed_dim				,       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				,       )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				SwinvaModel(config=lowerCamelCase__   )
									model.to(lowerCamelCase__   )
									model.eval()
									_lowerCamelCase        =				model(lowerCamelCase__   )

									_lowerCamelCase        =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
									_lowerCamelCase        =				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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				SwinvaForMaskedImageModeling(config=lowerCamelCase__   )
									model.to(lowerCamelCase__   )
									model.eval()
									_lowerCamelCase        =				model(lowerCamelCase__   )
									self.parent.assertEqual(
									    result.logits.shape				,       (self.batch_size, self.num_channels, self.image_size, self.image_size)   )

									# test greyscale images
									_lowerCamelCase        =				1
									_lowerCamelCase        =				SwinvaForMaskedImageModeling(lowerCamelCase__   )
									model.to(lowerCamelCase__   )
									model.eval()

									_lowerCamelCase        =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
									_lowerCamelCase        =				model(lowerCamelCase__   )
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, 1, self.image_size, self.image_size)   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				self.type_sequence_label_size
									_lowerCamelCase        =				SwinvaForImageClassification(lowerCamelCase__   )
									model.to(lowerCamelCase__   )
									model.eval()
									_lowerCamelCase        =				model(lowerCamelCase__				,       labels=lowerCamelCase__   )
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.type_sequence_label_size)   )





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


@require_torch
class 							lowerCamelCase_(   A__,				A__,				unittest.TestCase    ):
					lowercase__   :       Tuple										= (
					    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
					)
					lowercase__   :       Optional[int]										= (
					    {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					lowercase__   :       Any										= False
					lowercase__   :       Tuple										= False
					lowercase__   :       Tuple										= False
					lowercase__   :       Dict										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				SwinvaModelTester(self   )
									_lowerCamelCase        =				ConfigTester(self				,       config_class=lowerCamelCase__				,       embed_dim=3_7   )

					def 			snake_case__      (    self   ):
									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   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__   )

					@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''   )
					def 			snake_case__      (    self   ):
									pass

					@unittest.skip(reason='''Swinv2 does not use inputs_embeds'''   )
					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

													_lowerCamelCase        =				['''pixel_values''']
													self.assertListEqual(arg_names[:1]				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()
									_lowerCamelCase        =				True

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				True
													_lowerCamelCase        =				False
													_lowerCamelCase        =				True
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													model.to(lowerCamelCase__   )
													model.eval()
													with torch.no_grad():
																	_lowerCamelCase        =				model(**self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )   )
													_lowerCamelCase        =				outputs.attentions
													_lowerCamelCase        =				len(self.model_tester.depths   )
													self.assertEqual(len(lowerCamelCase__   )				,       lowerCamelCase__   )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													_lowerCamelCase        =				True
													_lowerCamelCase        =				config.window_size**2
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													model.to(lowerCamelCase__   )
													model.eval()
													with torch.no_grad():
																	_lowerCamelCase        =				model(**self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )   )
													_lowerCamelCase        =				outputs.attentions
													self.assertEqual(len(lowerCamelCase__   )				,       lowerCamelCase__   )

													self.assertListEqual(
													    list(attentions[0].shape[-3:]   )				,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,       )
													_lowerCamelCase        =				len(lowerCamelCase__   )

													# Check attention is always last and order is fine
													_lowerCamelCase        =				True
													_lowerCamelCase        =				True
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													model.to(lowerCamelCase__   )
													model.eval()
													with torch.no_grad():
																	_lowerCamelCase        =				model(**self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )   )

													if hasattr(self.model_tester				,       '''num_hidden_states_types'''   ):
																	_lowerCamelCase        =				self.model_tester.num_hidden_states_types
													else:
																	# also another +1 for reshaped_hidden_states
																	_lowerCamelCase        =				2
													self.assertEqual(out_len + added_hidden_states				,       len(lowerCamelCase__   )   )

													_lowerCamelCase        =				outputs.attentions

													self.assertEqual(len(lowerCamelCase__   )				,       lowerCamelCase__   )

													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]   )				,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,       )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				model_class(lowerCamelCase__   )
									model.to(lowerCamelCase__   )
									model.eval()

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

									_lowerCamelCase        =				outputs.hidden_states

									_lowerCamelCase        =				getattr(
									    self.model_tester				,       '''expected_num_hidden_layers'''				,       len(self.model_tester.depths   ) + 1   )
									self.assertEqual(len(lowerCamelCase__   )				,       lowerCamelCase__   )

									# Swinv2 has a different seq_length
									_lowerCamelCase        =				(
									    config.patch_size
									    if isinstance(config.patch_size				,       collections.abc.Iterable   )
									    else (config.patch_size, config.patch_size)
									)

									_lowerCamelCase        =				(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]				,       )

									_lowerCamelCase        =				outputs.reshaped_hidden_states
									self.assertEqual(len(lowerCamelCase__   )				,       lowerCamelCase__   )

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

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCamelCase        =				(
									    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:
													_lowerCamelCase        =				True
													self.check_hidden_states_output(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

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

													self.check_hidden_states_output(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()
									_lowerCamelCase        =				3

									_lowerCamelCase        =				(
									    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)
									)
									_lowerCamelCase        =				(
									    config.patch_size
									    if isinstance(config.patch_size				,       collections.abc.Iterable   )
									    else (config.patch_size, config.patch_size)
									)

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

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				True
													self.check_hidden_states_output(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       (padded_height, padded_width)   )

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

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__   )

					@slow
					def 			snake_case__      (    self   ):
									for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowerCamelCase        =				SwinvaModel.from_pretrained(lowerCamelCase__   )
													self.assertIsNotNone(lowerCamelCase__   )





					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCamelCase        =				_config_zero_init(lowerCamelCase__   )
									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(config=lowerCamelCase__   )
													for name, param in model.named_parameters():
																	if "embeddings" not in name and "logit_scale" 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 							lowerCamelCase_(   unittest.TestCase    ):

					@cached_property
					def 			snake_case__      (    self   ):
									return (
									    AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''   )
									    if is_vision_available()
									    else None
									)





					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''   ).to(
									    lowerCamelCase__   )
									_lowerCamelCase        =				self.default_image_processor

									_lowerCamelCase        =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
									_lowerCamelCase        =				image_processor(images=lowerCamelCase__				,       return_tensors='''pt'''   ).to(lowerCamelCase__   )

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

									# verify the logits
									_lowerCamelCase        =				torch.Size((1, 1_0_0_0)   )
									self.assertEqual(outputs.logits.shape				,       lowerCamelCase__   )
									_lowerCamelCase        =				torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]   ).to(lowerCamelCase__   )
									self.assertTrue(torch.allclose(outputs.logits[0, :3]				,       lowerCamelCase__				,       atol=1e-4   )   )





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE  :						List[Any]    							=      {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						int    							=      [
			    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMForMultipleChoice''',
			    '''XLMForQuestionAnswering''',
			    '''XLMForQuestionAnsweringSimple''',
			    '''XLMForSequenceClassification''',
			    '''XLMForTokenClassification''',
			    '''XLMModel''',
			    '''XLMPreTrainedModel''',
			    '''XLMWithLMHeadModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      [
			    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMForMultipleChoice''',
			    '''TFXLMForQuestionAnsweringSimple''',
			    '''TFXLMForSequenceClassification''',
			    '''TFXLMForTokenClassification''',
			    '''TFXLMMainLayer''',
			    '''TFXLMModel''',
			    '''TFXLMPreTrainedModel''',
			    '''TFXLMWithLMHeadModel''',
			]


if TYPE_CHECKING:
			from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
			from .tokenization_xlm import XLMTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm import (
						    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMForMultipleChoice,
						    XLMForQuestionAnswering,
						    XLMForQuestionAnsweringSimple,
						    XLMForSequenceClassification,
						    XLMForTokenClassification,
						    XLMModel,
						    XLMPreTrainedModel,
						    XLMWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm import (
						    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMForMultipleChoice,
						    TFXLMForQuestionAnsweringSimple,
						    TFXLMForSequenceClassification,
						    TFXLMForTokenClassification,
						    TFXLMMainLayer,
						    TFXLMModel,
						    TFXLMPreTrainedModel,
						    TFXLMWithLMHeadModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						Tuple    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
