


'''simple docstring'''



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
							from ...onnx.config import PatchingSpec
							from ...tokenization_utils_base import PreTrainedTokenizerBase


_lowercase      :      Optional[Any]            =					logging.get_logger(__name__)

_lowercase      :      str            =					{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Any  		=	"longformer"




				def __init__(		self      :			Dict				,   lowerCAmelCase      :			Union[List[int], int] = 512				,   lowerCAmelCase      :			int = 2				,   lowerCAmelCase      :			int = 1				,   lowerCAmelCase      :			int = 0				,   lowerCAmelCase      :			int = 2				,   lowerCAmelCase      :			int = 30522				,   lowerCAmelCase      :			int = 768				,   lowerCAmelCase      :			int = 12				,   lowerCAmelCase      :			int = 12				,   lowerCAmelCase      :			int = 3072				,   lowerCAmelCase      :			str = "gelu"				,   lowerCAmelCase      :			float = 0.1				,   lowerCAmelCase      :			float = 0.1				,   lowerCAmelCase      :			int = 512				,   lowerCAmelCase      :			int = 2				,   lowerCAmelCase      :			float = 0.02				,   lowerCAmelCase      :			float = 1E-12				,   lowerCAmelCase      :			bool = False				,   **lowerCAmelCase      :			Union[str, Any]				,   )->					List[str]:

										"""simple docstring"""




										super().__init__(pad_token_id=lowerCAmelCase				,   **lowerCAmelCase				)

										UpperCAmelCase					    =     attention_window
										UpperCAmelCase					    =     sep_token_id
										UpperCAmelCase					    =     bos_token_id
										UpperCAmelCase					    =     eos_token_id
										UpperCAmelCase					    =     vocab_size
										UpperCAmelCase					    =     hidden_size
										UpperCAmelCase					    =     num_hidden_layers
										UpperCAmelCase					    =     num_attention_heads
										UpperCAmelCase					    =     hidden_act
										UpperCAmelCase					    =     intermediate_size
										UpperCAmelCase					    =     hidden_dropout_prob
										UpperCAmelCase					    =     attention_probs_dropout_prob
										UpperCAmelCase					    =     max_position_embeddings
										UpperCAmelCase					    =     type_vocab_size
										UpperCAmelCase					    =     initializer_range
										UpperCAmelCase					    =     layer_norm_eps
										UpperCAmelCase					    =     onnx_export







class 	UpperCamelCase__(       lowerCAmelCase	):




				def __init__(		self      :			List[Any]				,   lowerCAmelCase      :			"PretrainedConfig"				,   lowerCAmelCase      :			str = "default"				,   lowerCAmelCase      :			"List[PatchingSpec]" = None				)->					Tuple:

										"""simple docstring"""




										super().__init__(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)
										UpperCAmelCase					    =     True




				@property
				def 					a__(		self      :			int				)->					Mapping[str, Mapping[int, str]]:

										"""simple docstring"""




										if self.task == "multiple-choice":
																UpperCAmelCase					    =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																UpperCAmelCase					    =     {0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										        ('''global_attention_mask''', dynamic_axis),
										    ]				)




				@property
				def 					a__(		self      :			Tuple				)->					Mapping[str, Mapping[int, str]]:

										"""simple docstring"""




										UpperCAmelCase					    =     super().outputs
										if self.task == "default":
																UpperCAmelCase					    =     {0: '''batch'''}
										return outputs




				@property
				def 					a__(		self      :			Dict				)->					float:

										"""simple docstring"""




										return 1E-4




				@property
				def 					a__(		self      :			Tuple				)->					int:

										"""simple docstring"""




										return max(super().default_onnx_opset				,   14				)





				def 					a__(		self      :			int				,   lowerCAmelCase      :			"PreTrainedTokenizerBase"				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			Optional[TensorType] = None				,   )->					Mapping[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     super().generate_dummy_inputs(
										    preprocessor=lowerCAmelCase				,   batch_size=lowerCAmelCase				,   seq_length=lowerCAmelCase				,   is_pair=lowerCAmelCase				,   framework=lowerCAmelCase				)
										import torch

										# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
										# makes the export fail randomly
										UpperCAmelCase					    =     torch.zeros_like(inputs['''input_ids''']				)
										# make every second token global
										UpperCAmelCase					    =     1

										return inputs









'''simple docstring'''



import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_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


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class 	UpperCamelCase__:




				def __init__(		self      :			List[str]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			List[str]=None				,   lowerCAmelCase      :			int="resnet50"				,   lowerCAmelCase      :			Optional[int]=3				,   lowerCAmelCase      :			Union[str, Any]=32				,   lowerCAmelCase      :			List[Any]=3				,   lowerCAmelCase      :			Tuple=True				,   lowerCAmelCase      :			str=True				,   )->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     parent
										UpperCAmelCase					    =     out_indices if out_indices is not None else [4]
										UpperCAmelCase					    =     stage_names
										UpperCAmelCase					    =     out_features
										UpperCAmelCase					    =     backbone
										UpperCAmelCase					    =     batch_size
										UpperCAmelCase					    =     image_size
										UpperCAmelCase					    =     num_channels
										UpperCAmelCase					    =     use_pretrained_backbone
										UpperCAmelCase					    =     is_training




				def 					a__(		self      :			Optional[Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
										UpperCAmelCase					    =     self.get_config()

										return config, pixel_values




				def 					a__(		self      :			int				)->					Optional[Any]:

										"""simple docstring"""




										return TimmBackboneConfig(
										    image_size=self.image_size				,   num_channels=self.num_channels				,   out_features=self.out_features				,   out_indices=self.out_indices				,   stage_names=self.stage_names				,   use_pretrained_backbone=self.use_pretrained_backbone				,   backbone=self.backbone				,   )




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     TimmBackbone(config=lowerCAmelCase				)
										model.to(lowerCAmelCase				)
										model.eval()
										with torch.no_grad():
																UpperCAmelCase					    =     model(lowerCAmelCase				)
										self.parent.assertEqual(
										    result.feature_map[-1].shape				,   (self.batch_size, model.channels[-1], 14, 14)				,   )





				def 					a__(		self      :			str				)->					Optional[Any]:

										"""simple docstring"""




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







@require_torch
@require_timm
class 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase						,    lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				List[str]  		=	(TimmBackbone,) if is_torch_available() else ()
				__magic_name__    :				Any  		=	{"feature-extraction": TimmBackbone} if is_torch_available() else {}
				__magic_name__    :				Union[str, Any]  		=	False
				__magic_name__    :				int  		=	False
				__magic_name__    :				Tuple  		=	False
				__magic_name__    :				List[str]  		=	False




				def 					a__(		self      :			int				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     TimmBackboneModelTester(self				)
										UpperCAmelCase					    =     ConfigTester(self				,   config_class=lowerCAmelCase				,   has_text_modality=lowerCAmelCase				)




				def 					a__(		self      :			List[str]				)->					Dict:

										"""simple docstring"""




										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      :			List[Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     '''resnet18'''
										UpperCAmelCase					    =     '''microsoft/resnet-18'''

										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   use_timm_backbone=lowerCAmelCase				)
										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				)

										self.assertEqual(len(timm_model.out_features				)				,   len(transformers_model.out_features				)				)
										self.assertEqual(len(timm_model.stage_names				)				,   len(transformers_model.stage_names				)				)
										self.assertEqual(timm_model.channels				,   transformers_model.channels				)
										# Out indices are set to the last layer by default. For timm models, we don't know
										# the number of layers in advance, so we set it to (-1,), whereas for transformers
										# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
										self.assertEqual(timm_model.out_indices				,   (-1,)				)
										self.assertEqual(transformers_model.out_indices				,   [len(timm_model.stage_names				) - 1]				)

										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   use_timm_backbone=lowerCAmelCase				,   out_indices=[1, 2, 3]				)
										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   out_indices=[1, 2, 3]				)

										self.assertEqual(timm_model.out_indices				,   transformers_model.out_indices				)
										self.assertEqual(len(timm_model.out_features				)				,   len(transformers_model.out_features				)				)
										self.assertEqual(timm_model.channels				,   transformers_model.channels				)




				@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''				)
				def 					a__(		self      :			Union[str, Any]				)->					Tuple:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''				)
				def 					a__(		self      :			Dict				)->					Dict:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone initialization is managed on the timm side'''				)
				def 					a__(		self      :			Tuple				)->					List[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
				def 					a__(		self      :			List[str]				)->					Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
				def 					a__(		self      :			Optional[Any]				)->					Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''				)
				def 					a__(		self      :			Any				)->					Optional[int]:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			Optional[int]				)->					Optional[int]:

										"""simple docstring"""




										pass




				@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
				def 					a__(		self      :			str				)->					str:

										"""simple docstring"""




										pass




				@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
				def 					a__(		self      :			Any				)->					int:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			List[Any]				)->					int:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			List[Any]				)->					List[str]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''				)
				def 					a__(		self      :			List[str]				)->					Dict:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''				)
				def 					a__(		self      :			str				)->					Tuple:

										"""simple docstring"""




										pass




				@unittest.skip('''Safetensors is not supported by timm.'''				)
				def 					a__(		self      :			List[Any]				)->					List[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
				def 					a__(		self      :			int				)->					Tuple:

										"""simple docstring"""




										pass




				def 					a__(		self      :			Dict				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

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

																UpperCAmelCase					    =     ['''pixel_values''']
																self.assertListEqual(arg_names[:1]				,   lowerCAmelCase				)




				def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     self.has_attentions

										# no need to test all models as different heads yield the same functionality
										UpperCAmelCase					    =     self.all_model_classes[0]
										UpperCAmelCase					    =     model_class(lowerCAmelCase				)
										model.to(lowerCAmelCase				)

										UpperCAmelCase					    =     self._prepare_for_class(lowerCAmelCase				,   lowerCAmelCase				)
										UpperCAmelCase					    =     model(**lowerCAmelCase				)
										UpperCAmelCase					    =     outputs[0][-1]

										# Encoder-/Decoder-only models
										UpperCAmelCase					    =     outputs.hidden_states[0]
										hidden_states.retain_grad()

										if self.has_attentions:
																UpperCAmelCase					    =     outputs.attentions[0]
																attentions.retain_grad()

										output.flatten()[0].backward(retain_graph=lowerCAmelCase				)

										self.assertIsNotNone(hidden_states.grad				)

										if self.has_attentions:
																self.assertIsNotNone(attentions.grad				)





				def 					a__(		self      :			Optional[int]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)

																self.assertEqual(len(result.feature_maps				)				,   len(config.out_indices				)				)
																self.assertEqual(len(model.channels				)				,   len(config.out_indices				)				)

																# Check output of last stage is taken if out_features=None, out_indices=None
																UpperCAmelCase					    =     copy.deepcopy(lowerCAmelCase				)
																UpperCAmelCase					    =     None
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)

																self.assertEqual(len(result.feature_maps				)				,   1				)
																self.assertEqual(len(model.channels				)				,   1				)

																# Check backbone can be initialized with fresh weights
																UpperCAmelCase					    =     copy.deepcopy(lowerCAmelCase				)
																UpperCAmelCase					    =     False
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)





