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

from transformers import SwinConfig
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
					from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor



class 	lowerCAmelCase_     :



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase=13		,     lowerCAmelCase=32		,     lowerCAmelCase=2		,     lowerCAmelCase=3		,     lowerCAmelCase=16		,     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=0.02		,     lowerCAmelCase=1E-5		,     lowerCAmelCase=True		,     lowerCAmelCase=None		,     lowerCAmelCase=True		,     lowerCAmelCase=10		,     lowerCAmelCase=8		,     lowerCAmelCase=["stage1", "stage2"]		,     lowerCAmelCase=[1, 2]		,     ):






										"""simple docstring"""

										snake_case             =      parent
										snake_case             =      batch_size
										snake_case             =      image_size
										snake_case             =      patch_size
										snake_case             =      num_channels
										snake_case             =      embed_dim
										snake_case             =      depths
										snake_case             =      num_heads
										snake_case             =      window_size
										snake_case             =      mlp_ratio
										snake_case             =      qkv_bias
										snake_case             =      hidden_dropout_prob
										snake_case             =      attention_probs_dropout_prob
										snake_case             =      drop_path_rate
										snake_case             =      hidden_act
										snake_case             =      use_absolute_embeddings
										snake_case             =      patch_norm
										snake_case             =      layer_norm_eps
										snake_case             =      initializer_range
										snake_case             =      is_training
										snake_case             =      scope
										snake_case             =      use_labels
										snake_case             =      type_sequence_label_size
										snake_case             =      encoder_stride
										snake_case             =      out_features
										snake_case             =      out_indices

				def 		snake_case			(					self     ):






										"""simple docstring"""

										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 		snake_case			(					self     ):






										"""simple docstring"""

										return SwinConfig(
										    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		,     out_features=self.out_features		,     out_indices=self.out_indices		,     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      SwinModel(config=lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase     )

										snake_case             =      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
										snake_case             =      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     ):






										"""simple docstring"""

										snake_case             =      SwinBackbone(config=lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase     )

										# verify hidden states
										self.parent.assertEqual(len(result.feature_maps     )		,     len(config.out_features     )     )
										self.parent.assertListEqual(list(result.feature_maps[0].shape     )		,     [self.batch_size, model.channels[0], 16, 16]     )

										# verify channels
										self.parent.assertEqual(len(model.channels     )		,     len(config.out_features     )     )

										# verify backbone works with out_features=None
										snake_case             =      None
										snake_case             =      SwinBackbone(config=lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase     )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps     )		,     1     )
										self.parent.assertListEqual(list(result.feature_maps[0].shape     )		,     [self.batch_size, model.channels[-1], 4, 4]     )

										# verify channels
										self.parent.assertEqual(len(model.channels     )		,     1     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      SwinForMaskedImageModeling(config=lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase     )
										self.parent.assertEqual(
										    result.logits.shape		,     (self.batch_size, self.num_channels, self.image_size, self.image_size)     )

										# test greyscale images
										snake_case             =      1
										snake_case             =      SwinForMaskedImageModeling(lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()

										snake_case             =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
										snake_case             =      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     ):






										"""simple docstring"""

										snake_case             =      self.type_sequence_label_size
										snake_case             =      SwinForImageClassification(lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase		,     labels=lowerCAmelCase     )
										self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)     )

										# test greyscale images
										snake_case             =      1
										snake_case             =      SwinForImageClassification(lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

										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 	lowerCAmelCase_     (      lowerCAmelCase  ,     lowerCAmelCase  ,     unittest.TestCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     Tuple      	=    (
				    (
				        SwinModel,
				        SwinBackbone,
				        SwinForImageClassification,
				        SwinForMaskedImageModeling,
				    )
				    if is_torch_available()
				    else ()
				)
				_lowerCAmelCase  :     Any      	=    (
				    {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
				    if is_torch_available()
				    else {}
				)
				_lowerCAmelCase  :     List[str]      	=    True

				_lowerCAmelCase  :     List[Any]      	=    False
				_lowerCAmelCase  :     Optional[Any]      	=    False
				_lowerCAmelCase  :     Dict      	=    False

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      SwinModelTester(self     )
										snake_case             =      ConfigTester(self		,     config_class=lowerCAmelCase		,     embed_dim=37     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										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 		snake_case			(					self     ):






										"""simple docstring"""

										return

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      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     ):






										"""simple docstring"""

										pass

				def 		snake_case			(					self     ):






										"""simple docstring"""

										super().test_training_gradient_checkpointing()

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase     )

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






										"""simple docstring"""

										pass

				@unittest.skip(reason='Swin Transformer does not use feedforward chunking'     )
				def 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				def 		snake_case			(					self     ):






										"""simple docstring"""

										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(lowerCAmelCase     )
																self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module)     )
																snake_case             =      model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowerCAmelCase		,     nn.Linear     )     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										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(lowerCAmelCase     )
																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]		,     lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()
										snake_case             =      True

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

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

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

																# also another +1 for reshaped_hidden_states
																snake_case             =      1 if model_class.__name__ == 'SwinBackbone' else 2
																self.assertEqual(out_len + added_hidden_states		,     len(lowerCAmelCase     )     )

																snake_case             =      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     ):






										"""simple docstring"""

										snake_case             =      model_class(lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()

										with torch.no_grad():
																snake_case             =      model(**self._prepare_for_class(lowerCAmelCase		,     lowerCAmelCase     )     )

										snake_case             =      outputs.hidden_states

										snake_case             =      getattr(
										    self.model_tester		,     'expected_num_hidden_layers'		,     len(self.model_tester.depths     ) + 1     )
										self.assertEqual(len(lowerCAmelCase     )		,     lowerCAmelCase     )

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

										snake_case             =      (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]		,     )

										if not model_class.__name__ == "SwinBackbone":
																snake_case             =      outputs.reshaped_hidden_states
																self.assertEqual(len(lowerCAmelCase     )		,     lowerCAmelCase     )

																snake_case       ,snake_case       ,snake_case       ,snake_case             =      reshaped_hidden_states[0].shape
																snake_case             =      (
																    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     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

										snake_case             =      (
										    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:
																snake_case             =      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"]
																snake_case             =      True

																self.check_hidden_states_output(lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()
										snake_case             =      3

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

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

										for model_class in self.all_model_classes:
																snake_case             =      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"]
																snake_case             =      True
																self.check_hidden_states_output(lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     (padded_height, padded_width)     )

				@slow
				def 		snake_case			(					self     ):






										"""simple docstring"""

										for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case             =      SwinModel.from_pretrained(lowerCAmelCase     )
																self.assertIsNotNone(lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

										snake_case             =      _config_zero_init(lowerCAmelCase     )
										for model_class in self.all_model_classes:
																snake_case             =      model_class(config=lowerCAmelCase     )
																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 	lowerCAmelCase_     (      unittest.TestCase       ):



				"""simple docstring"""

				@cached_property
				def 		snake_case			(					self     ):






										"""simple docstring"""

										return (
										    AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'     )
										    if is_vision_available()
										    else None
										)

				@slow
				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'     ).to(lowerCAmelCase     )
										snake_case             =      self.default_image_processor

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

										# forward pass
										with torch.no_grad():
																snake_case             =      model(**lowerCAmelCase     )

										# verify the logits
										snake_case             =      torch.Size((1, 10_00)     )
										self.assertEqual(outputs.logits.shape		,     lowerCAmelCase     )
										snake_case             =      torch.tensor([-0.09_48, -0.64_54, -0.09_21]     ).to(lowerCAmelCase     )
										self.assertTrue(torch.allclose(outputs.logits[0, :3]		,     lowerCAmelCase		,     atol=1E-4     )     )





@require_torch
class 	lowerCAmelCase_     (      unittest.TestCase  ,     lowerCAmelCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     Any      	=    (SwinBackbone,) if is_torch_available() else ()
				_lowerCAmelCase  :     List[str]      	=    SwinConfig

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      SwinModelTester(self     )



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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       =	{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "OpenLlamaForCausalLM",
					    "OpenLlamaModel",
					    "OpenLlamaPreTrainedModel",
					    "OpenLlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_open_llama import (
										    OpenLlamaForCausalLM,
										    OpenLlamaForSequenceClassification,
										    OpenLlamaModel,
										    OpenLlamaPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


