

import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
						from transformers.models.maskformer import MaskFormerSwinModel



class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     List[Any]=13   ,						lowerCAmelCase__  :     List[str]=32   ,						lowerCAmelCase__  :     List[str]=2   ,						lowerCAmelCase__  :     Optional[int]=3   ,						lowerCAmelCase__  :     List[Any]=16   ,						lowerCAmelCase__  :     int=[1, 2, 1]   ,						lowerCAmelCase__  :     Optional[Any]=[2, 2, 4]   ,						lowerCAmelCase__  :     List[Any]=2   ,						lowerCAmelCase__  :     str=2.0   ,						lowerCAmelCase__  :     List[Any]=True   ,						lowerCAmelCase__  :     Tuple=0.0   ,						lowerCAmelCase__  :     Tuple=0.0   ,						lowerCAmelCase__  :     Dict=0.1   ,						lowerCAmelCase__  :     Union[str, Any]="gelu"   ,						lowerCAmelCase__  :     Union[str, Any]=False   ,						lowerCAmelCase__  :     Tuple=True   ,						lowerCAmelCase__  :     List[str]=0.02   ,						lowerCAmelCase__  :     List[str]=1E-5   ,						lowerCAmelCase__  :     int=True   ,						lowerCAmelCase__  :     List[Any]=None   ,						lowerCAmelCase__  :     str=True   ,						lowerCAmelCase__  :     Tuple=10   ,						lowerCAmelCase__  :     Any=8   ,						lowerCAmelCase__  :     List[str]=["stage1", "stage2", "stage3"]   ,						lowerCAmelCase__  :     Optional[int]=[1, 2, 3]   ,						):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       parent
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       batch_size
									SCREAMING_SNAKE_CASE_: List[str]  		=       image_size
									SCREAMING_SNAKE_CASE_: int  		=       patch_size
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       num_channels
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       embed_dim
									SCREAMING_SNAKE_CASE_: List[str]  		=       depths
									SCREAMING_SNAKE_CASE_: Any  		=       num_heads
									SCREAMING_SNAKE_CASE_: Any  		=       window_size
									SCREAMING_SNAKE_CASE_: str  		=       mlp_ratio
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       qkv_bias
									SCREAMING_SNAKE_CASE_: Any  		=       hidden_dropout_prob
									SCREAMING_SNAKE_CASE_: Any  		=       attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       drop_path_rate
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       hidden_act
									SCREAMING_SNAKE_CASE_: int  		=       use_absolute_embeddings
									SCREAMING_SNAKE_CASE_: Tuple  		=       patch_norm
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       layer_norm_eps
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       initializer_range
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       is_training
									SCREAMING_SNAKE_CASE_: List[str]  		=       scope
									SCREAMING_SNAKE_CASE_: List[Any]  		=       use_labels
									SCREAMING_SNAKE_CASE_: Any  		=       type_sequence_label_size
									SCREAMING_SNAKE_CASE_: int  		=       encoder_stride
									SCREAMING_SNAKE_CASE_: List[str]  		=       out_features
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       out_indices



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

									SCREAMING_SNAKE_CASE_: List[str]  		=       None
									if self.use_labels:
															SCREAMING_SNAKE_CASE_: Tuple  		=       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_config()

									return config, pixel_values, labels



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return MaskFormerSwinConfig(
									    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        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       MaskFormerSwinModel(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Tuple  		=       model(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Any  		=       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
									SCREAMING_SNAKE_CASE_: Dict  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       MaskFormerSwinBackbone(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: List[Any]  		=       model(lowerCAmelCase__)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps)   ,						len(config.out_features))
									self.parent.assertListEqual(list(result.feature_maps[0].shape)   ,						[13, 16, 16, 16])

									# verify channels
									self.parent.assertEqual(len(model.channels)   ,						len(config.out_features))
									self.parent.assertListEqual(model.channels   ,						[16, 32, 64])

									# verify ValueError
									with self.parent.assertRaises(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       ["stem"]
															SCREAMING_SNAKE_CASE_: Dict  		=       MaskFormerSwinBackbone(config=lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       config_and_inputs
									SCREAMING_SNAKE_CASE_: int  		=       {"pixel_values": pixel_values}
									return config, inputs_dict







@require_torch
class   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[int]										=     (
			    (
			        MaskFormerSwinModel,
			        MaskFormerSwinBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			_UpperCAmelCase   :							List[Any]										=     {'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
			_UpperCAmelCase   :							str										=     False
			_UpperCAmelCase   :							Optional[Any]										=     False
			_UpperCAmelCase   :							Union[str, Any]										=     False
			_UpperCAmelCase   :							Optional[Any]										=     False
			_UpperCAmelCase   :							Optional[int]										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Dict  		=       MaskFormerSwinModelTester(self)
									SCREAMING_SNAKE_CASE_: Tuple  		=       ConfigTester(self   ,						config_class=lowerCAmelCase__   ,						embed_dim=37)



			@require_torch_multi_gpu
			@unittest.skip(
			    reason=(
			        "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
			        " `nn.DataParallel`"
			    ))
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									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        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									return



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*lowerCAmelCase__)



			@unittest.skip("Swin does not use inputs_embeds")
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									pass



			@unittest.skip("Swin does not support feedforward chunking")
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       model_class(lowerCAmelCase__)
															self.assertIsInstance(model.get_input_embeddings()   ,						(nn.Module))
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(lowerCAmelCase__   ,						nn.Linear))



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Dict  		=       inspect.signature(model.forward)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															SCREAMING_SNAKE_CASE_: List[Any]  		=       [*signature.parameters.keys()]

															SCREAMING_SNAKE_CASE_: Dict  		=       ["pixel_values"]
															self.assertListEqual(arg_names[:1]   ,						lowerCAmelCase__)



			@unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions")
			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									pass



			@unittest.skip(reason="MaskFormerSwin is only used as an internal backbone")
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       model_class(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()

									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: List[Any]  		=       model(**self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__))

									SCREAMING_SNAKE_CASE_: List[Any]  		=       outputs.hidden_states

									SCREAMING_SNAKE_CASE_: int  		=       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
									SCREAMING_SNAKE_CASE_: Dict  		=       (
									    config.patch_size
									    if isinstance(config.patch_size   ,						collections.abc.Iterable)
									    else (config.patch_size, config.patch_size)
									)

									SCREAMING_SNAKE_CASE_: str  		=       (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]   ,						)



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE_: List[Any]  		=       (
									    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:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       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"]
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       True

															self.check_hidden_states_output(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE_: int  		=       3

									SCREAMING_SNAKE_CASE_: Tuple  		=       (
									    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)
									)
									SCREAMING_SNAKE_CASE_: Tuple  		=       (
									    config.patch_size
									    if isinstance(config.patch_size   ,						collections.abc.Iterable)
									    else (config.patch_size, config.patch_size)
									)

									SCREAMING_SNAKE_CASE_: Dict  		=       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       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"]
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       True
															self.check_hidden_states_output(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						(padded_height, padded_width))



			@unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints")
			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									pass



			@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									pass



			@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									pass




			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.model_tester.prepare_config_and_inputs_for_common()

									def set_nan_tensor_to_zero(lowerCAmelCase__  :     List[str]):
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       0
															return t

									def check_equivalence(lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Union[str, Any]={}):
															with torch.no_grad():
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model(**lowerCAmelCase__   ,						return_dict=lowerCAmelCase__   ,						**lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(**lowerCAmelCase__   ,						return_dict=lowerCAmelCase__   ,						**lowerCAmelCase__).to_tuple()

																					def recursive_check(lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Optional[int]):
																											if isinstance(lowerCAmelCase__   ,						(List, Tuple)):
																																	for tuple_iterable_value, dict_iterable_value in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
																																							recursive_check(lowerCAmelCase__   ,						lowerCAmelCase__)
																											elif isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
																																	for tuple_iterable_value, dict_iterable_value in zip(
																																	    tuple_object.values()   ,						dict_object.values()):
																																							recursive_check(lowerCAmelCase__   ,						lowerCAmelCase__)
																											elif tuple_object is None:
																																	return
																											else:
																																	self.assertTrue(
																																	    torch.allclose(
																																	        set_nan_tensor_to_zero(lowerCAmelCase__)   ,						set_nan_tensor_to_zero(lowerCAmelCase__)   ,						atol=1E-5)   ,						msg=(
																																	        "Tuple and dict output are not equal. Difference:"
																																	        F" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
																																	        F" {torch.isnan(lowerCAmelCase__).any()} and `inf`: {torch.isinf(lowerCAmelCase__)}. Dict has"
																																	        F" `nan`: {torch.isnan(lowerCAmelCase__).any()} and `inf`: {torch.isinf(lowerCAmelCase__)}."
																																	    )   ,						)

																					recursive_check(lowerCAmelCase__   ,						lowerCAmelCase__)

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_class(lowerCAmelCase__)
															model.to(lowerCAmelCase__)
															model.eval()

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: str  		=       self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__)
															check_equivalence(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Any  		=       self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__   ,						return_labels=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: str  		=       self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__   ,						return_labels=lowerCAmelCase__)
															check_equivalence(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Dict  		=       self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__)
															check_equivalence(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						{"output_hidden_states": True})

															SCREAMING_SNAKE_CASE_: List[Any]  		=       self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__   ,						return_labels=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Any  		=       self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__   ,						return_labels=lowerCAmelCase__)
															check_equivalence(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						{"output_hidden_states": True})







@require_torch
class   __lowercase				( unittest.TestCase		,       UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Union[str, Any]										=     (MaskFormerSwinBackbone,) if is_torch_available() else ()
			_UpperCAmelCase   :							str										=     MaskFormerSwinConfig



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       MaskFormerSwinModelTester(self)




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE_: Any  		=       inputs_dict["pixel_values"].shape[0]

									for backbone_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: List[str]  		=       backbone_class(lowerCAmelCase__)
															backbone.to(lowerCAmelCase__)
															backbone.eval()

															SCREAMING_SNAKE_CASE_: Any  		=       backbone(**lowerCAmelCase__)

															# Test default outputs and verify feature maps
															self.assertIsInstance(outputs.feature_maps   ,						lowerCAmelCase__)
															self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
															for feature_map, n_channels in zip(outputs.feature_maps   ,						backbone.channels):
																					self.assertTrue(feature_map.shape[:2]   ,						(batch_size, n_channels))
															self.assertIsNone(outputs.hidden_states)
															self.assertIsNone(outputs.attentions)

															# Test output_hidden_states=True
															SCREAMING_SNAKE_CASE_: str  		=       backbone(**lowerCAmelCase__   ,						output_hidden_states=lowerCAmelCase__)
															self.assertIsNotNone(outputs.hidden_states)
															self.assertTrue(len(outputs.hidden_states)   ,						len(backbone.stage_names))
															# We skip the stem layer
															for hidden_states, n_channels in zip(outputs.hidden_states[1:]   ,						backbone.channels):
																					for hidden_state in hidden_states:
																											# Hidden states are in the format (batch_size, (height * width), n_channels)
																											SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       hidden_state.shape
																											self.assertTrue((h_batch_size, h_n_channels)   ,						(batch_size, n_channels))

            # Test output_attentions=True
															if self.has_attentions:
																					SCREAMING_SNAKE_CASE_: Dict  		=       backbone(**lowerCAmelCase__   ,						output_attentions=lowerCAmelCase__)
																					self.assertIsNotNone(outputs.attentions)










from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       :      str												= {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

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

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= [
     "LlamaForCausalLM",
     "LlamaModel",
     "LlamaPreTrainedModel",
     "LlamaForSequenceClassification",
 ]


if TYPE_CHECKING:
 from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

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

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

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
 import sys

 SCREAMING_SNAKE_CASE       :      Optional[int]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


