


'''simple docstring'''
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  UpperCamelCase__          = '''platform'''

  import jax
  import jax.numpy as jnp

  from transformers import BlenderbotTokenizer
  from transformers.models.blenderbot.modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      shift_tokens_right,
  )





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__=None    ,   lowerCAmelCase__=None    ,   lowerCAmelCase__=None    ,   lowerCAmelCase__=None    ,   lowerCAmelCase__=None    ,   lowerCAmelCase__=None    ,   )       ->      Tuple:
    if attention_mask is None:
        UpperCAmelCase__     :						List[Any]						=							np.where(input_ids != config.pad_token_id    ,   1    ,   0		)
    if decoder_attention_mask is None:
        UpperCAmelCase__     :						Union[str, Any]						=							np.where(decoder_input_ids != config.pad_token_id    ,   1    ,   0		)
    if head_mask is None:
        UpperCAmelCase__     :						Optional[Any]						=							np.ones((config.encoder_layers, config.encoder_attention_heads)		)
    if decoder_head_mask is None:
        UpperCAmelCase__     :						Optional[int]						=							np.ones((config.decoder_layers, config.decoder_attention_heads)		)
    if cross_attn_head_mask is None:
        UpperCAmelCase__     :						Any						=							np.ones((config.decoder_layers, config.decoder_attention_heads)		)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }




class      lowerCamelCase_  :






      def __init__(   self							:       Optional[Any]		,		_A							:       Optional[Any]		,		_A							:       str=13		,		_A							:       int=7		,		_A							:       Any=True		,		_A							:       List[Any]=False		,		_A							:       Optional[int]=99		,		_A							:       Optional[int]=16		,		_A							:       int=2		,		_A							:       Optional[int]=4		,		_A							:       Optional[int]=4		,		_A							:       int="gelu"		,		_A							:       List[str]=0.1		,		_A							:       str=0.1		,		_A							:       int=32		,		_A							:       Optional[int]=2		,		_A							:       int=1		,		_A							:       Dict=0		,		_A							:       Dict=0.0_2		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							parent
          UpperCAmelCase__     :						str						=							batch_size
          UpperCAmelCase__     :						Dict						=							seq_length
          UpperCAmelCase__     :						str						=							is_training
          UpperCAmelCase__     :						int						=							use_labels
          UpperCAmelCase__     :						Union[str, Any]						=							vocab_size
          UpperCAmelCase__     :						Union[str, Any]						=							hidden_size
          UpperCAmelCase__     :						int						=							num_hidden_layers
          UpperCAmelCase__     :						Any						=							num_attention_heads
          UpperCAmelCase__     :						List[str]						=							intermediate_size
          UpperCAmelCase__     :						str						=							hidden_act
          UpperCAmelCase__     :						str						=							hidden_dropout_prob
          UpperCAmelCase__     :						Union[str, Any]						=							attention_probs_dropout_prob
          UpperCAmelCase__     :						Union[str, Any]						=							max_position_embeddings
          UpperCAmelCase__     :						int						=							eos_token_id
          UpperCAmelCase__     :						Optional[int]						=							pad_token_id
          UpperCAmelCase__     :						List[str]						=							bos_token_id
          UpperCAmelCase__     :						Union[str, Any]						=							initializer_range






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,		self.vocab_size					)		,		3		,		self.vocab_size					)
          UpperCAmelCase__     :						str						=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,		dtype=np.intaa					))		,		-1					)

          UpperCAmelCase__     :						List[Any]						=							shift_tokens_right(_A		,		1		,		2					)

          UpperCAmelCase__     :						List[Any]						=							BlenderbotConfig(
              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		,		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		,		initializer_range=self.initializer_range		,		use_cache=_A		,		)
          UpperCAmelCase__     :						Tuple						=							prepare_blenderbot_inputs_dict(_A		,		_A		,		_A					)
          return config, inputs_dict






      def lowercase_				(   self							:       Union[str, Any]					):


          '''simple docstring'''


          UpperCAmelCase__		,		UpperCAmelCase__     :						List[Any]						=							self.prepare_config_and_inputs()
          return config, inputs_dict






      def lowercase_				(   self							:       int		,		_A							:       List[Any]		,		_A							:       Optional[Any]		,		_A							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							20
          UpperCAmelCase__     :						int						=							model_class_name(_A					)

          UpperCAmelCase__     :						str						=							model.encode(inputs_dict['''input_ids''']					)

          UpperCAmelCase__		,		UpperCAmelCase__     :						Dict						=							(
              inputs_dict['''decoder_input_ids'''],
              inputs_dict['''decoder_attention_mask'''],
          )

          UpperCAmelCase__     :						Tuple						=							model.init_cache(decoder_input_ids.shape[0]		,		_A		,		_A					)
          UpperCAmelCase__     :						Optional[Any]						=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,		dtype='''i4'''					)

          UpperCAmelCase__     :						str						=							jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]		,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,		)
          UpperCAmelCase__     :						str						=							model.decode(
              decoder_input_ids[:, :-1]		,		_A		,		decoder_attention_mask=_A		,		past_key_values=_A		,		decoder_position_ids=_A		,		)

          UpperCAmelCase__     :						Dict						=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,		dtype='''i4'''					)
          UpperCAmelCase__     :						Tuple						=							model.decode(
              decoder_input_ids[:, -1:]		,		_A		,		decoder_attention_mask=_A		,		past_key_values=outputs_cache.past_key_values		,		decoder_position_ids=_A		,		)

          UpperCAmelCase__     :						int						=							model.decode(_A		,		_A					)

          UpperCAmelCase__     :						Dict						=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
          self.parent.assertTrue(diff < 1e-3		,		msg=f"""Max diff is {diff}"""					)






      def lowercase_				(   self							:       Tuple		,		_A							:       List[Any]		,		_A							:       Tuple		,		_A							:       Tuple					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							20
          UpperCAmelCase__     :						Optional[int]						=							model_class_name(_A					)

          UpperCAmelCase__     :						Optional[int]						=							model.encode(inputs_dict['''input_ids''']					)

          UpperCAmelCase__		,		UpperCAmelCase__     :						Optional[int]						=							(
              inputs_dict['''decoder_input_ids'''],
              inputs_dict['''decoder_attention_mask'''],
          )

          UpperCAmelCase__     :						Any						=							jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
              ]		,		axis=-1		,		)

          UpperCAmelCase__     :						List[Any]						=							model.init_cache(decoder_input_ids.shape[0]		,		_A		,		_A					)
          UpperCAmelCase__     :						Optional[Any]						=							jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]		,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,		)

          UpperCAmelCase__     :						int						=							model.decode(
              decoder_input_ids[:, :-1]		,		_A		,		decoder_attention_mask=_A		,		past_key_values=_A		,		decoder_position_ids=_A		,		)
          UpperCAmelCase__     :						List[Any]						=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,		dtype='''i4'''					)
          UpperCAmelCase__     :						Any						=							model.decode(
              decoder_input_ids[:, -1:]		,		_A		,		past_key_values=outputs_cache.past_key_values		,		decoder_attention_mask=_A		,		decoder_position_ids=_A		,		)

          UpperCAmelCase__     :						List[str]						=							model.decode(_A		,		_A		,		decoder_attention_mask=_A					)

          UpperCAmelCase__     :						str						=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
          self.parent.assertTrue(diff < 1e-3		,		msg=f"""Max diff is {diff}"""					)




@require_flax
class      lowerCamelCase_  (      unittest.TestCase ):
      lowerCAmelCase__							=	9_9






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							np.array(
              [
                  [71, 82, 18, 33, 46, 91, 2],
                  [68, 34, 26, 58, 30, 82, 2],
                  [5, 97, 17, 39, 94, 40, 2],
                  [76, 83, 94, 25, 70, 78, 2],
                  [87, 59, 41, 35, 48, 66, 2],
                  [55, 13, 16, 58, 5, 2, 1],  # note padding
                  [64, 27, 31, 51, 12, 75, 2],
                  [52, 64, 86, 17, 83, 39, 2],
                  [48, 61, 9, 24, 71, 82, 2],
                  [26, 1, 60, 48, 22, 13, 2],
                  [21, 5, 62, 28, 14, 76, 2],
                  [45, 98, 37, 86, 59, 48, 2],
                  [70, 70, 50, 9, 28, 0, 2],
              ]		,		dtype=np.intaa		,		)

          UpperCAmelCase__     :						int						=							input_ids.shape[0]
          UpperCAmelCase__     :						List[str]						=							BlenderbotConfig(
              vocab_size=self.vocab_size		,		d_model=24		,		encoder_layers=2		,		decoder_layers=2		,		encoder_attention_heads=2		,		decoder_attention_heads=2		,		encoder_ffn_dim=32		,		decoder_ffn_dim=32		,		max_position_embeddings=48		,		eos_token_id=2		,		pad_token_id=1		,		bos_token_id=0		,		)
          return config, input_ids, batch_size






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__     :						Union[str, Any]						=							self._get_config_and_data()
          UpperCAmelCase__     :						Any						=							FlaxBlenderbotForConditionalGeneration(_A					)
          UpperCAmelCase__     :						Optional[int]						=							lm_model(input_ids=_A					)
          UpperCAmelCase__     :						Dict						=							(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['''logits'''].shape		,		_A					)






      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							BlenderbotConfig(
              vocab_size=self.vocab_size		,		d_model=14		,		encoder_layers=2		,		decoder_layers=2		,		encoder_attention_heads=2		,		decoder_attention_heads=2		,		encoder_ffn_dim=8		,		decoder_ffn_dim=8		,		max_position_embeddings=48		,		)
          UpperCAmelCase__     :						Union[str, Any]						=							FlaxBlenderbotForConditionalGeneration(_A					)
          UpperCAmelCase__     :						str						=							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,		dtype=np.intaa					)
          UpperCAmelCase__     :						Any						=							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,		dtype=np.intaa					)
          UpperCAmelCase__     :						Tuple						=							lm_model(input_ids=_A		,		decoder_input_ids=_A					)
          UpperCAmelCase__     :						int						=							(*summary.shape, config.vocab_size)
          self.assertEqual(outputs['''logits'''].shape		,		_A					)






      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,		dtype=np.intaa					)
          UpperCAmelCase__     :						Union[str, Any]						=							shift_tokens_right(_A		,		1		,		2					)
          UpperCAmelCase__     :						str						=							np.equal(_A		,		1					).astype(np.floataa					).sum()
          UpperCAmelCase__     :						Dict						=							np.equal(_A		,		1					).astype(np.floataa					).sum()
          self.assertEqual(shifted.shape		,		input_ids.shape					)
          self.assertEqual(_A		,		n_pad_before - 1					)
          self.assertTrue(np.equal(shifted[:, 0]		,		2					).all()					)




@require_flax
class      lowerCamelCase_  (      __a	,			unittest.TestCase	,			__a ):
      lowerCAmelCase__							=	True
      lowerCAmelCase__							=	(
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      lowerCAmelCase__							=	(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							FlaxBlenderbotModelTester(self					)






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__		,		UpperCAmelCase__     :						List[str]						=							self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(_A		,		_A		,		_A					)






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__		,		UpperCAmelCase__     :						Union[str, Any]						=							self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(_A		,		_A		,		_A					)






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__		,		UpperCAmelCase__     :						List[str]						=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__					):
                  UpperCAmelCase__     :						Dict						=							self._prepare_for_class(_A		,		_A					)
                  UpperCAmelCase__     :						str						=							model_class(_A					)

                  @jax.jit
                  def encode_jitted(_A							:       Any		,		_A							:       Tuple=None		,		**_A							:       Optional[int]					):
                      return model.encode(input_ids=_A		,		attention_mask=_A					)

                  with self.subTest('''JIT Enabled'''					):
                      UpperCAmelCase__     :						Optional[Any]						=							encode_jitted(**_A					).to_tuple()

                  with self.subTest('''JIT Disabled'''					):
                      with jax.disable_jit():
                          UpperCAmelCase__     :						Tuple						=							encode_jitted(**_A					).to_tuple()

                  self.assertEqual(len(_A					)		,		len(_A					)					)
                  for jitted_output, output in zip(_A		,		_A					):
                      self.assertEqual(jitted_output.shape		,		output.shape					)






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          UpperCAmelCase__		,		UpperCAmelCase__     :						int						=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__					):
                  UpperCAmelCase__     :						List[str]						=							model_class(_A					)
                  UpperCAmelCase__     :						Tuple						=							model.encode(inputs_dict['''input_ids''']		,		inputs_dict['''attention_mask''']					)

                  UpperCAmelCase__     :						Tuple						=							{
                      '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                      '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                      '''encoder_outputs''': encoder_outputs,
                  }

                  @jax.jit
                  def decode_jitted(_A							:       Optional[int]		,		_A							:       List[Any]		,		_A							:       int					):
                      return model.decode(
                          decoder_input_ids=_A		,		decoder_attention_mask=_A		,		encoder_outputs=_A		,		)

                  with self.subTest('''JIT Enabled'''					):
                      UpperCAmelCase__     :						Any						=							decode_jitted(**_A					).to_tuple()

                  with self.subTest('''JIT Disabled'''					):
                      with jax.disable_jit():
                          UpperCAmelCase__     :						Optional[int]						=							decode_jitted(**_A					).to_tuple()

                  self.assertEqual(len(_A					)		,		len(_A					)					)
                  for jitted_output, output in zip(_A		,		_A					):
                      self.assertEqual(jitted_output.shape		,		output.shape					)






      @slow
      def lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              UpperCAmelCase__     :						Union[str, Any]						=							model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''					)
              # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
              UpperCAmelCase__     :						Tuple						=							np.ones((1, 1)					) * model.config.eos_token_id
              UpperCAmelCase__     :						Union[str, Any]						=							model(_A					)
              self.assertIsNotNone(_A					)






      @unittest.skipUnless(jax_device != '''cpu'''		,		'''3B test too slow on CPU.'''					)
      @slow
      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
          UpperCAmelCase__     :						int						=							{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

          UpperCAmelCase__     :						str						=							FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''		,		from_pt=_A					)
          UpperCAmelCase__     :						Optional[Any]						=							BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''					)

          UpperCAmelCase__     :						Optional[Any]						=							['''Sam''']
          UpperCAmelCase__     :						Dict						=							tokenizer(_A		,		return_tensors='''jax'''					)

          UpperCAmelCase__     :						List[str]						=							model.generate(**_A		,		**_A					)
          UpperCAmelCase__     :						Dict						=							'''Sam is a great name. It means "sun" in Gaelic.'''

          UpperCAmelCase__     :						Any						=							tokenizer.batch_decode(_A		,		**_A					)
          assert generated_txt[0].strip() == tgt_text

'''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     A :





							def __init__(   self					:				List[str]  ,		__a					:				List[Any]  ,		__a					:				Dict=None  ,		__a					:				str=None  ,		__a					:				List[Any]=None  ,		__a					:				Union[str, Any]="resnet50"  ,		__a					:				List[Any]=3  ,		__a					:				List[Any]=3_2  ,		__a					:				List[Any]=3  ,		__a					:				Optional[int]=True  ,		__a					:				str=True  ,		)				->						Any:
								__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    snake_case__    (   self					:				Optional[Any]						)				->						Optional[int]:
								__UpperCAmelCase      					=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
								__UpperCAmelCase      					=      self.get_config()

								return config, pixel_values





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






							def    snake_case__    (   self					:				Dict						)				->						Tuple:
								__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     A ( UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						unittest.TestCase ):
							a_										=   (TimmBackbone,) if is_torch_available() else ()
							a_										=   {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
							a_										=   False
							a_										=   False
							a_										=   False
							a_										=   False





							def    snake_case__    (   self					:				Optional[int]						)				->						List[str]:
								__UpperCAmelCase      					=      TimmBackboneModelTester(self						)
								__UpperCAmelCase      					=      ConfigTester(self  ,		config_class=__a  ,		has_text_modality=__a						)





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

								__UpperCAmelCase      					=      AutoBackbone.from_pretrained(__a  ,		use_timm_backbone=__a						)
								__UpperCAmelCase      					=      AutoBackbone.from_pretrained(__a						)

								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(__a  ,		use_timm_backbone=__a  ,		out_indices=[1, 2, 3]						)
								__UpperCAmelCase      					=      AutoBackbone.from_pretrained(__a  ,		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    snake_case__    (   self					:				str						)				->						Optional[Any]:
								pass





							@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''						)
							def    snake_case__    (   self					:				str						)				->						List[Any]:
								pass





							@unittest.skip('''TimmBackbone initialization is managed on the timm side'''						)
							def    snake_case__    (   self					:				str						)				->						int:
								pass





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





							@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''						)
							def    snake_case__    (   self					:				Optional[int]						)				->						Dict:
								pass





							@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''						)
							def    snake_case__    (   self					:				str						)				->						Dict:
								pass





							@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''						)
							def    snake_case__    (   self					:				Optional[Any]						)				->						Union[str, Any]:
								pass





							@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''						)
							def    snake_case__    (   self					:				Union[str, Any]						)				->						Dict:
								pass





							@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''						)
							def    snake_case__    (   self					:				List[str]						)				->						Union[str, Any]:
								pass





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





							@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''						)
							def    snake_case__    (   self					:				Tuple						)				->						int:
								pass





							@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''						)
							def    snake_case__    (   self					:				Union[str, Any]						)				->						Union[str, Any]:
								pass





							@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''						)
							def    snake_case__    (   self					:				List[str]						)				->						Dict:
								pass





							@unittest.skip('''Safetensors is not supported by timm.'''						)
							def    snake_case__    (   self					:				int						)				->						int:
								pass





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





							def    snake_case__    (   self					:				Optional[Any]						)				->						Optional[int]:
								__UpperCAmelCase					,      __UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__UpperCAmelCase      					=      model_class(__a						)
									__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]  ,		__a						)





							def    snake_case__    (   self					:				Any						)				->						int:
								__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(__a						)
								model.to(__a						)

								__UpperCAmelCase      					=      self._prepare_for_class(__a  ,		__a						)
								__UpperCAmelCase      					=      model(**__a						)
								__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=__a						)

								self.assertIsNotNone(hidden_states.grad						)

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






							def    snake_case__    (   self					:				Any						)				->						Union[str, Any]:
								__UpperCAmelCase					,      __UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__UpperCAmelCase      					=      model_class(__a						)
									model.to(__a						)
									model.eval()
									__UpperCAmelCase      					=      model(**__a						)

									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(__a						)
									__UpperCAmelCase      					=      None
									__UpperCAmelCase      					=      model_class(__a						)
									model.to(__a						)
									model.eval()
									__UpperCAmelCase      					=      model(**__a						)

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

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