


import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
				from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_=None      ,       lowercase_=None      ,       lowercase_=None      ,       lowercase_=None      ,       lowercase_=None      ,       )		->     Any:




						'''simple docstring'''







						if attention_mask is None:
												__UpperCAmelCase			:							str	    =	input_ids.ne(config.pad_token_id      )
						if decoder_attention_mask is None:
												__UpperCAmelCase			:							Tuple	    =	decoder_input_ids.ne(config.pad_token_id      )
						if head_mask is None:
												__UpperCAmelCase			:							Any	    =	torch.ones(config.encoder_layers      ,       config.encoder_attention_heads      ,       device=lowercase_      )
						if decoder_head_mask is None:
												__UpperCAmelCase			:							List[Any]	    =	torch.ones(config.decoder_layers      ,       config.decoder_attention_heads      ,       device=lowercase_      )
						if cross_attn_head_mask is None:
												__UpperCAmelCase			:							Tuple	    =	torch.ones(config.decoder_layers      ,       config.decoder_attention_heads      ,       device=lowercase_      )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}




class 							lowerCamelCase   :


	def __init__(    self				,     lowercase__				,     lowercase__=1_3				,     lowercase__=7				,     lowercase__=True				,     lowercase__=False				,     lowercase__=9_9				,     lowercase__=1_6				,     lowercase__=2				,     lowercase__=4				,     lowercase__=4				,     lowercase__="relu"				,     lowercase__=0.1				,     lowercase__=0.1				,     lowercase__=0.0				,     lowercase__=0.0				,     lowercase__=2_0				,     lowercase__=2				,     lowercase__=1				,     lowercase__=0				,     ):
							__UpperCAmelCase			:							str	    =	parent
							__UpperCAmelCase			:							int	    =	batch_size
							__UpperCAmelCase			:							List[Any]	    =	seq_length
							__UpperCAmelCase			:							Optional[int]	    =	is_training
							__UpperCAmelCase			:							Union[str, Any]	    =	use_labels
							__UpperCAmelCase			:							Union[str, Any]	    =	vocab_size
							__UpperCAmelCase			:							Union[str, Any]	    =	hidden_size
							__UpperCAmelCase			:							Optional[int]	    =	num_hidden_layers
							__UpperCAmelCase			:							Optional[Any]	    =	num_attention_heads
							__UpperCAmelCase			:							List[Any]	    =	intermediate_size
							__UpperCAmelCase			:							Optional[int]	    =	hidden_act
							__UpperCAmelCase			:							Optional[int]	    =	hidden_dropout_prob
							__UpperCAmelCase			:							List[Any]	    =	attention_probs_dropout_prob
							__UpperCAmelCase			:							List[Any]	    =	encoder_layerdrop
							__UpperCAmelCase			:							Optional[int]	    =	decoder_layerdrop
							__UpperCAmelCase			:							int	    =	max_position_embeddings
							__UpperCAmelCase			:							Optional[Any]	    =	eos_token_id
							__UpperCAmelCase			:							List[str]	    =	pad_token_id
							__UpperCAmelCase			:							str	    =	bos_token_id


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size)
							__UpperCAmelCase			:							Any	    =	self.eos_token_id  # Eos Token
							__UpperCAmelCase			:							Tuple	    =	ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size)

							# we need to clamp the input ids here to avoid having pad token in between
							# this is because for M2M100 the position_ids are prepared such that
							# all pad tokens have pos id = 2 and rest are between 2..seq_length
							# and the seq_length here is seq_length - num_pad_tokens
							# but when using past, there is no way of knowing if the past input ids had
							# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
							# position_ids being off by num_pad_tokens in past input
							__UpperCAmelCase			:							int	    =	input_ids.clamp(self.pad_token_id + 1)
							__UpperCAmelCase			:							Optional[int]	    =	decoder_input_ids.clamp(self.pad_token_id + 1)

							__UpperCAmelCase			:							Optional[Any]	    =	self.get_config()
							__UpperCAmelCase			:							Optional[Any]	    =	prepare_mam_aaa_inputs_dict(lowercase__				,     lowercase__				,     lowercase__)
							return config, inputs_dict


	def  A(    self):
							return MaMaaaConfig(
							    vocab_size=self.vocab_size				,     d_model=self.hidden_size				,     encoder_layers=self.num_hidden_layers				,     decoder_layers=self.num_hidden_layers				,     encoder_attention_heads=self.num_attention_heads				,     decoder_attention_heads=self.num_attention_heads				,     encoder_ffn_dim=self.intermediate_size				,     decoder_ffn_dim=self.intermediate_size				,     dropout=self.hidden_dropout_prob				,     attention_dropout=self.attention_probs_dropout_prob				,     encoder_layerdrop=self.encoder_layerdrop				,     decoder_layerdrop=self.decoder_layerdrop				,     max_position_embeddings=self.max_position_embeddings				,     eos_token_id=self.eos_token_id				,     bos_token_id=self.bos_token_id				,     pad_token_id=self.pad_token_id				,     )


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	self.prepare_config_and_inputs()
							return config, inputs_dict


	def  A(    self				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	MaMaaaModel(config=lowercase__).get_decoder().to(lowercase__).eval()
							__UpperCAmelCase			:							List[str]	    =	inputs_dict['''input_ids''']
							__UpperCAmelCase			:							int	    =	inputs_dict['''attention_mask''']
							__UpperCAmelCase			:							Dict	    =	inputs_dict['''head_mask''']

							# first forward pass
							__UpperCAmelCase			:							List[str]	    =	model(lowercase__				,     attention_mask=lowercase__				,     head_mask=lowercase__				,     use_cache=lowercase__)

							__UpperCAmelCase    ,      __UpperCAmelCase			:							int	    =	outputs.to_tuple()

							# create hypothetical multiple next token and extent to next_input_ids
							__UpperCAmelCase			:							Dict	    =	ids_tensor((self.batch_size, 3)				,     config.vocab_size)
							__UpperCAmelCase			:							Tuple	    =	ids_tensor((self.batch_size, 3)				,     2)

							# append to next input_ids and
							__UpperCAmelCase			:							List[Any]	    =	torch.cat([input_ids, next_tokens]				,     dim=-1)
							__UpperCAmelCase			:							List[Any]	    =	torch.cat([attention_mask, next_attn_mask]				,     dim=-1)

							__UpperCAmelCase			:							Optional[Any]	    =	model(lowercase__				,     attention_mask=lowercase__)['''last_hidden_state''']
							__UpperCAmelCase			:							Optional[Any]	    =	model(lowercase__				,     attention_mask=lowercase__				,     past_key_values=lowercase__)[
							    '''last_hidden_state'''
							]

							# select random slice
							__UpperCAmelCase			:							int	    =	ids_tensor((1,)				,     output_from_past.shape[-1]).item()
							__UpperCAmelCase			:							int	    =	output_from_no_past[:, -3:, random_slice_idx].detach()
							__UpperCAmelCase			:							List[str]	    =	output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(lowercase__				,     lowercase__				,     atol=1e-2))


	def  A(    self				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							int	    =	MaMaaaModel(config=lowercase__).to(lowercase__).eval()
							__UpperCAmelCase			:							Any	    =	model(**lowercase__)

							__UpperCAmelCase			:							List[Any]	    =	outputs.encoder_last_hidden_state
							__UpperCAmelCase			:							Tuple	    =	outputs.last_hidden_state

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase			:							Optional[int]	    =	model.get_encoder()
													encoder.save_pretrained(lowercase__)
													__UpperCAmelCase			:							Optional[int]	    =	MaMaaaEncoder.from_pretrained(lowercase__).to(lowercase__)

							__UpperCAmelCase			:							Tuple	    =	encoder(inputs_dict['''input_ids''']				,     attention_mask=inputs_dict['''attention_mask'''])[
							    0
							]

							self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase			:							List[Any]	    =	model.get_decoder()
													decoder.save_pretrained(lowercase__)
													__UpperCAmelCase			:							Tuple	    =	MaMaaaDecoder.from_pretrained(lowercase__).to(lowercase__)

							__UpperCAmelCase			:							str	    =	decoder(
							    input_ids=inputs_dict['''decoder_input_ids''']				,     attention_mask=inputs_dict['''decoder_attention_mask''']				,     encoder_hidden_states=lowercase__				,     encoder_attention_mask=inputs_dict['''attention_mask''']				,     )[0]

							self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3)






@require_torch
class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Union[str, Any]										=						(
	    (
	        MaMaaaModel,
	        MaMaaaForConditionalGeneration,
	    )
	    if is_torch_available()
	    else ()
	)
	_lowerCAmelCase     :		int										=						(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
	_lowerCAmelCase     :		Optional[int]										=						(
	    {
	        '''conversational''': MaMaaaForConditionalGeneration,
	        '''feature-extraction''': MaMaaaModel,
	        '''summarization''': MaMaaaForConditionalGeneration,
	        '''text2text-generation''': MaMaaaForConditionalGeneration,
	        '''translation''': MaMaaaForConditionalGeneration,
	    }
	    if is_torch_available()
	    else {}
	)
	_lowerCAmelCase     :		int										=						True
	_lowerCAmelCase     :		int										=						True
	_lowerCAmelCase     :		Any										=						False
	_lowerCAmelCase     :		List[Any]										=						False


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
							if pipeline_test_casse_name == "TranslationPipelineTests":
													# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
													# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
													return True

							return False


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	MaMaaaModelTester(self)
							__UpperCAmelCase			:							Dict	    =	ConfigTester(self				,     config_class=lowercase__)


	def  A(    self):
							self.config_tester.run_common_tests()


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Dict	    =	self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
													__UpperCAmelCase			:							str	    =	model_class(lowercase__)

													with tempfile.TemporaryDirectory() as tmpdirname:
																			model.save_pretrained(lowercase__)
																			__UpperCAmelCase    ,      __UpperCAmelCase			:							List[str]	    =	model_class.from_pretrained(lowercase__				,     output_loading_info=lowercase__)
													self.assertEqual(info['''missing_keys''']				,     [])


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_encoder_decoder_model_standalone(*lowercase__)


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							int	    =	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
													__UpperCAmelCase			:							Optional[Any]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.eval()

													__UpperCAmelCase			:							int	    =	copy.deepcopy(self._prepare_for_class(lowercase__				,     lowercase__))

													if not self.is_encoder_decoder:
																			__UpperCAmelCase			:							Any	    =	inputs['''input_ids''']
																			del inputs["input_ids"]
													else:
																			__UpperCAmelCase			:							List[str]	    =	inputs['''input_ids''']
																			__UpperCAmelCase			:							int	    =	inputs.get('''decoder_input_ids'''				,     lowercase__)
																			del inputs["input_ids"]
																			inputs.pop('''decoder_input_ids'''				,     lowercase__)

													__UpperCAmelCase			:							Dict	    =	model.get_input_embeddings()
													if not self.is_encoder_decoder:
																			__UpperCAmelCase			:							Tuple	    =	wte(lowercase__)
													else:
																			__UpperCAmelCase			:							int	    =	wte(lowercase__)
																			__UpperCAmelCase			:							str	    =	wte(lowercase__)

													with torch.no_grad():
																			model(**lowercase__)[0]


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							List[Any]	    =	self.model_tester.prepare_config_and_inputs()
							__UpperCAmelCase			:							Optional[Any]	    =	input_dict['''input_ids''']
							__UpperCAmelCase			:							List[str]	    =	input_ids.ne(1).to(lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	MaMaaaForConditionalGeneration(lowercase__).eval().to(lowercase__)
							if torch_device == "cuda":
													model.half()
							model.generate(lowercase__				,     attention_mask=lowercase__)
							model.generate(num_beams=4				,     do_sample=lowercase__				,     early_stopping=lowercase__				,     num_return_sequences=3)






def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     str:




						'''simple docstring'''







						return torch.tensor(lowercase_      ,       dtype=torch.long      ,       device=lowercase_      )


lowerCAmelCase       		=    1E-4





@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 							lowerCamelCase   (  unittest.TestCase ):


	@cached_property
	def  A(    self):
							return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M''')


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	MaMaaaModel.from_pretrained('''facebook/m2m100_418M''').to(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	_long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]])
							__UpperCAmelCase			:							Dict	    =	_long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]])
							__UpperCAmelCase			:							Dict	    =	prepare_mam_aaa_inputs_dict(model.config				,     lowercase__				,     lowercase__)
							with torch.no_grad():
													__UpperCAmelCase			:							Any	    =	model(**lowercase__)[0]
							__UpperCAmelCase			:							str	    =	torch.Size((1, 1_1, 1_0_2_4))
							self.assertEqual(output.shape				,     lowercase__)
							# change to expected output here
							__UpperCAmelCase			:							List[str]	    =	torch.tensor(
							    [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]				,     device=lowercase__)
							self.assertTrue(torch.allclose(output[:, :3, :3]				,     lowercase__				,     atol=lowercase__))


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''').to(lowercase__)

							# change to intended input
							__UpperCAmelCase			:							int	    =	_long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]])
							__UpperCAmelCase			:							List[Any]	    =	_long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]])
							__UpperCAmelCase			:							str	    =	prepare_mam_aaa_inputs_dict(model.config				,     lowercase__				,     lowercase__)
							with torch.no_grad():
													__UpperCAmelCase			:							List[str]	    =	model(**lowercase__)[0]
							__UpperCAmelCase			:							str	    =	torch.Size((1, 1_1, model.config.vocab_size))
							self.assertEqual(output.shape				,     lowercase__)
							# change to expected output here
							__UpperCAmelCase			:							Dict	    =	torch.tensor(
							    [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]				,     device=lowercase__)
							self.assertTrue(torch.allclose(output[:, :3, :3]				,     lowercase__				,     atol=lowercase__))


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''').to(lowercase__)
							__UpperCAmelCase			:							str	    =	MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''				,     src_lang='''fr'''				,     tgt_lang='''en''')

							__UpperCAmelCase			:							Tuple	    =	[
							    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
							    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
							    '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
							    ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
							    ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
							]

							# The below article tests that we don't add any hypotheses outside of the top n_beams
							__UpperCAmelCase			:							Optional[int]	    =	tokenizer(lowercase__				,     padding=lowercase__				,     return_tensors='''pt''')

							__UpperCAmelCase			:							int	    =	model.generate(
							    input_ids=dct['''input_ids'''].to(lowercase__)				,     attention_mask=dct['''attention_mask'''].to(lowercase__)				,     num_beams=5				,     forced_bos_token_id=tokenizer.get_lang_id('''en''')				,     )

							__UpperCAmelCase			:							Any	    =	[
							    '''The NSA case highlights the total absence of intelligence debate''',
							    '''I think there are two levels of response from the French government.''',
							    '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
							    ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
							    ''' communications in France.''',
							]

							__UpperCAmelCase			:							List[Any]	    =	tokenizer.batch_decode(
							    hypotheses_batch.tolist()				,     clean_up_tokenization_spaces=lowercase__				,     skip_special_tokens=lowercase__)
							assert generated == expected_en





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 							lowerCamelCase   :


	def __init__(    self				,     lowercase__				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     lowercase__="resnet50"				,     lowercase__=3				,     lowercase__=3_2				,     lowercase__=3				,     lowercase__=True				,     lowercase__=True				,     ):
							__UpperCAmelCase			:							List[str]	    =	parent
							__UpperCAmelCase			:							Dict	    =	out_indices if out_indices is not None else [4]
							__UpperCAmelCase			:							List[Any]	    =	stage_names
							__UpperCAmelCase			:							int	    =	out_features
							__UpperCAmelCase			:							Union[str, Any]	    =	backbone
							__UpperCAmelCase			:							Optional[Any]	    =	batch_size
							__UpperCAmelCase			:							Optional[Any]	    =	image_size
							__UpperCAmelCase			:							Optional[int]	    =	num_channels
							__UpperCAmelCase			:							List[Any]	    =	use_pretrained_backbone
							__UpperCAmelCase			:							Dict	    =	is_training


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
							__UpperCAmelCase			:							Tuple	    =	self.get_config()

							return config, pixel_values


	def  A(    self):
							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				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Tuple	    =	TimmBackbone(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							with torch.no_grad():
													__UpperCAmelCase			:							Union[str, Any]	    =	model(lowercase__)
							self.parent.assertEqual(
							    result.feature_map[-1].shape				,     (self.batch_size, model.channels[-1], 1_4, 1_4)				,     )


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	self.prepare_config_and_inputs()
							__UpperCAmelCase    ,      __UpperCAmelCase			:							List[Any]	    =	config_and_inputs
							__UpperCAmelCase			:							List[Any]	    =	{'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
@require_timm
class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Dict										=						(TimmBackbone,) if is_torch_available() else ()
	_lowerCAmelCase     :		str										=						{'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
	_lowerCAmelCase     :		List[str]										=						False
	_lowerCAmelCase     :		str										=						False
	_lowerCAmelCase     :		List[Any]										=						False
	_lowerCAmelCase     :		List[str]										=						False


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	TimmBackboneModelTester(self)
							__UpperCAmelCase			:							int	    =	ConfigTester(self				,     config_class=lowercase__				,     has_text_modality=lowercase__)


	def  A(    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  A(    self):
							__UpperCAmelCase			:							int	    =	'''resnet18'''
							__UpperCAmelCase			:							List[str]	    =	'''microsoft/resnet-18'''

							__UpperCAmelCase			:							Any	    =	AutoBackbone.from_pretrained(lowercase__				,     use_timm_backbone=lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	AutoBackbone.from_pretrained(lowercase__)

							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			:							Union[str, Any]	    =	AutoBackbone.from_pretrained(lowercase__				,     use_timm_backbone=lowercase__				,     out_indices=[1, 2, 3])
							__UpperCAmelCase			:							Any	    =	AutoBackbone.from_pretrained(lowercase__				,     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):
							pass


	@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone initialization is managed on the timm side''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint''')
	def  A(    self):
							pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
	def  A(    self):
							pass


	@unittest.skip('''model weights aren\'t tied in TimmBackbone.''')
	def  A(    self):
							pass


	@unittest.skip('''model weights aren\'t tied in TimmBackbone.''')
	def  A(    self):
							pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
	def  A(    self):
							pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone doesn\'t support output_attentions.''')
	def  A(    self):
							pass


	@unittest.skip('''Safetensors is not supported by timm.''')
	def  A(    self):
							pass


	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
	def  A(    self):
							pass


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	self.model_tester.prepare_config_and_inputs_for_common()

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

													__UpperCAmelCase			:							Dict	    =	['''pixel_values''']
													self.assertListEqual(arg_names[:1]				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							str	    =	self.model_tester.prepare_config_and_inputs_for_common()
							__UpperCAmelCase			:							Tuple	    =	True
							__UpperCAmelCase			:							Optional[int]	    =	self.has_attentions

							# no need to test all models as different heads yield the same functionality
							__UpperCAmelCase			:							Optional[Any]	    =	self.all_model_classes[0]
							__UpperCAmelCase			:							Optional[int]	    =	model_class(lowercase__)
							model.to(lowercase__)

							__UpperCAmelCase			:							Tuple	    =	self._prepare_for_class(lowercase__				,     lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	model(**lowercase__)
							__UpperCAmelCase			:							List[str]	    =	outputs[0][-1]

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

							if self.has_attentions:
													__UpperCAmelCase			:							Optional[int]	    =	outputs.attentions[0]
													attentions.retain_grad()

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

							self.assertIsNotNone(hidden_states.grad)

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


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Dict	    =	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__UpperCAmelCase			:							Optional[Any]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.eval()
													__UpperCAmelCase			:							Union[str, Any]	    =	model(**lowercase__)

													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			:							List[str]	    =	copy.deepcopy(lowercase__)
													__UpperCAmelCase			:							str	    =	None
													__UpperCAmelCase			:							Optional[int]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.eval()
													__UpperCAmelCase			:							Optional[Any]	    =	model(**lowercase__)

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

													# Check backbone can be initialized with fresh weights
													__UpperCAmelCase			:							Tuple	    =	copy.deepcopy(lowercase__)
													__UpperCAmelCase			:							Optional[int]	    =	False
													__UpperCAmelCase			:							Optional[int]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.eval()
													__UpperCAmelCase			:							List[Any]	    =	model(**lowercase__)

