




import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.generation import (
       FlaxForcedBOSTokenLogitsProcessor,
       FlaxForcedEOSTokenLogitsProcessor,
       FlaxLogitsProcessorList,
       FlaxMinLengthLogitsProcessor,
       FlaxTemperatureLogitsWarper,
       FlaxTopKLogitsWarper,
       FlaxTopPLogitsWarper,
   )





@require_flax
class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int		):
       SCREAMING_SNAKE_CASE_      		=     jnp.ones((batch_size, length)		) / length
       return scores



 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     20

       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(batch_size=2							,							length=_lowerCAmelCase		)

       # tweak scores to not be uniform anymore
       SCREAMING_SNAKE_CASE_      		=     scores.at[1, 5].set((1 / length) + 0.1		)  # peak, 1st batch
       SCREAMING_SNAKE_CASE_      		=     scores.at[1, 10].set((1 / length) - 0.4		)  # valley, 1st batch

       # compute softmax
       SCREAMING_SNAKE_CASE_      		=     jax.nn.softmax(_lowerCAmelCase							,							axis=-1		)

       SCREAMING_SNAKE_CASE_      		=     FlaxTemperatureLogitsWarper(temperature=0.5		)
       SCREAMING_SNAKE_CASE_      		=     FlaxTemperatureLogitsWarper(temperature=1.3		)

       SCREAMING_SNAKE_CASE_      		=     jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase							,							scores.copy()							,							cur_len=_lowerCAmelCase		)							,							axis=-1		)
       SCREAMING_SNAKE_CASE_      		=     jax.nn.softmax(temp_dist_warper_smoother(_lowerCAmelCase							,							scores.copy()							,							cur_len=_lowerCAmelCase		)							,							axis=-1		)

       # uniform distribution stays uniform
       self.assertTrue(jnp.allclose(probs[0, :]							,							warped_prob_sharp[0, :]							,							atol=1E-3		)		)
       self.assertTrue(jnp.allclose(probs[0, :]							,							warped_prob_smooth[0, :]							,							atol=1E-3		)		)

       # sharp peaks get higher, valleys get lower
       self.assertLess(probs[1, :].max()							,							warped_prob_sharp[1, :].max()		)
       self.assertGreater(probs[1, :].min()							,							warped_prob_sharp[1, :].min()		)

       # smooth peaks get lower, valleys get higher
       self.assertGreater(probs[1, :].max()							,							warped_prob_smooth[1, :].max()		)
       self.assertLess(probs[1, :].min()							,							warped_prob_smooth[1, :].min()		)



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     10
       SCREAMING_SNAKE_CASE_      		=     2

       # create ramp distribution
       SCREAMING_SNAKE_CASE_      		=     np.broadcast_to(np.arange(_lowerCAmelCase		)[None, :]							,							(batch_size, vocab_size)		).copy()
       SCREAMING_SNAKE_CASE_      		=     ramp_logits[1:, : vocab_size // 2] + vocab_size

       SCREAMING_SNAKE_CASE_      		=     FlaxTopKLogitsWarper(3		)

       SCREAMING_SNAKE_CASE_      		=     top_k_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)

       # check that correct tokens are filtered
       self.assertListEqual(jnp.isinf(scores[0]		).tolist()							,							7 * [True] + 3 * [False]		)
       self.assertListEqual(jnp.isinf(scores[1]		).tolist()							,							2 * [True] + 3 * [False] + 5 * [True]		)

       # check special case
       SCREAMING_SNAKE_CASE_      		=     5
       SCREAMING_SNAKE_CASE_      		=     FlaxTopKLogitsWarper(top_k=1							,							filter_value=0.0							,							min_tokens_to_keep=3		)

       SCREAMING_SNAKE_CASE_      		=     np.broadcast_to(np.arange(_lowerCAmelCase		)[None, :]							,							(batch_size, length)		).copy()
       SCREAMING_SNAKE_CASE_      		=     top_k_warp_safety_check(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)

       # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
       self.assertListEqual((scores == 0.0).sum(axis=-1		).tolist()							,							[2, 2]		)



 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     10
       SCREAMING_SNAKE_CASE_      		=     2

       # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
       SCREAMING_SNAKE_CASE_      		=     np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]		)		)

       SCREAMING_SNAKE_CASE_      		=     FlaxTopPLogitsWarper(0.8		)
       SCREAMING_SNAKE_CASE_      		=     np.exp(top_p_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)		)

       # dist should be filtered to keep min num values so that sum is >= top_p
       # exp (-inf) => 0
       SCREAMING_SNAKE_CASE_      		=     np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]		)
       self.assertTrue(np.allclose(_lowerCAmelCase							,							_lowerCAmelCase							,							atol=1E-3		)		)

       # check edge cases with negative and extreme logits
       SCREAMING_SNAKE_CASE_      		=     np.broadcast_to(np.arange(_lowerCAmelCase		)[None, :]							,							(batch_size, vocab_size)		).copy() - (
           vocab_size // 2
       )

       # make ramp_logits more extreme
       SCREAMING_SNAKE_CASE_      		=     ramp_logits[1] * 100.0

       # make sure at least 2 tokens are kept
       SCREAMING_SNAKE_CASE_      		=     FlaxTopPLogitsWarper(0.9							,							min_tokens_to_keep=2							,							filter_value=0.0		)
       SCREAMING_SNAKE_CASE_      		=     top_p_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)

       # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
       self.assertListEqual((filtered_dist != 0.0).sum(axis=-1		).tolist()							,							[3, 2]		)



 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     20
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     0

       SCREAMING_SNAKE_CASE_      		=     FlaxMinLengthLogitsProcessor(min_length=10							,							eos_token_id=_lowerCAmelCase		)

       # check that min length is applied at length 5
       SCREAMING_SNAKE_CASE_      		=     ids_tensor((batch_size, 20)							,							vocab_size=20		)
       SCREAMING_SNAKE_CASE_      		=     5
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     min_dist_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()							,							4 * [-float('inf'		)]		)

       # check that min length is not applied anymore at length 15
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     15
       SCREAMING_SNAKE_CASE_      		=     min_dist_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertFalse(jnp.isinf(_lowerCAmelCase		).any()		)



 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     20
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     0

       SCREAMING_SNAKE_CASE_      		=     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase		)

       # check that all scores are -inf except the bos_token_id score
       SCREAMING_SNAKE_CASE_      		=     ids_tensor((batch_size, 1)							,							vocab_size=20		)
       SCREAMING_SNAKE_CASE_      		=     1
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     logits_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]		).all()		)
       self.assertListEqual(scores[:, bos_token_id].tolist()							,							4 * [0]		)  # score for bos_token_id shold be zero

       # check that bos_token_id is not forced if current length is greater than 1
       SCREAMING_SNAKE_CASE_      		=     3
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     logits_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertFalse(jnp.isinf(_lowerCAmelCase		).any()		)



 def  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     20
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     0
       SCREAMING_SNAKE_CASE_      		=     5

       SCREAMING_SNAKE_CASE_      		=     FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase							,							eos_token_id=_lowerCAmelCase		)

       # check that all scores are -inf except the eos_token_id when max_length is reached
       SCREAMING_SNAKE_CASE_      		=     ids_tensor((batch_size, 4)							,							vocab_size=20		)
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     logits_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]		).all()		)
       self.assertListEqual(scores[:, eos_token_id].tolist()							,							4 * [0]		)  # score for eos_token_id should be zero

       # check that eos_token_id is not forced if max_length is not reached
       SCREAMING_SNAKE_CASE_      		=     3
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     logits_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertFalse(jnp.isinf(_lowerCAmelCase		).any()		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     10
       SCREAMING_SNAKE_CASE_      		=     15
       SCREAMING_SNAKE_CASE_      		=     2
       SCREAMING_SNAKE_CASE_      		=     1
       SCREAMING_SNAKE_CASE_      		=     15

       # dummy input_ids and scores
       SCREAMING_SNAKE_CASE_      		=     ids_tensor((batch_size, sequence_length)							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     input_ids.copy()

       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     scores.copy()

       # instantiate all dist processors
       SCREAMING_SNAKE_CASE_      		=     FlaxTemperatureLogitsWarper(temperature=0.5		)
       SCREAMING_SNAKE_CASE_      		=     FlaxTopKLogitsWarper(3		)
       SCREAMING_SNAKE_CASE_      		=     FlaxTopPLogitsWarper(0.8		)

       # instantiate all logits processors
       SCREAMING_SNAKE_CASE_      		=     FlaxMinLengthLogitsProcessor(min_length=10							,							eos_token_id=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase							,							eos_token_id=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     10

       # no processor list
       SCREAMING_SNAKE_CASE_      		=     temp_dist_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     top_k_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     top_p_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     min_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     bos_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     eos_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)

       # with processor list
       SCREAMING_SNAKE_CASE_      		=     FlaxLogitsProcessorList(
           [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
       SCREAMING_SNAKE_CASE_      		=     processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)

       # scores should be equal
       self.assertTrue(jnp.allclose(_lowerCAmelCase							,							_lowerCAmelCase							,							atol=1E-3		)		)

       # input_ids should never be changed
       self.assertListEqual(input_ids.tolist()							,							input_ids_comp.tolist()		)




 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     10
       SCREAMING_SNAKE_CASE_      		=     15
       SCREAMING_SNAKE_CASE_      		=     2
       SCREAMING_SNAKE_CASE_      		=     1
       SCREAMING_SNAKE_CASE_      		=     15

       # dummy input_ids and scores
       SCREAMING_SNAKE_CASE_      		=     ids_tensor((batch_size, sequence_length)							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     input_ids.copy()

       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     scores.copy()

       # instantiate all dist processors
       SCREAMING_SNAKE_CASE_      		=     FlaxTemperatureLogitsWarper(temperature=0.5		)
       SCREAMING_SNAKE_CASE_      		=     FlaxTopKLogitsWarper(3		)
       SCREAMING_SNAKE_CASE_      		=     FlaxTopPLogitsWarper(0.8		)

       # instantiate all logits processors
       SCREAMING_SNAKE_CASE_      		=     FlaxMinLengthLogitsProcessor(min_length=10							,							eos_token_id=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase							,							eos_token_id=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     10

       # no processor list
       def run_no_processor_list(_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						int		):
             SCREAMING_SNAKE_CASE_      		=     temp_dist_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     top_k_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     top_p_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     min_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     bos_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     eos_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             return scores

       # with processor list
       def run_processor_list(_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]		):
             SCREAMING_SNAKE_CASE_      		=     FlaxLogitsProcessorList(
                 [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
             SCREAMING_SNAKE_CASE_      		=     processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             return scores

       SCREAMING_SNAKE_CASE_      		=     jax.jit(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     jax.jit(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     jitted_run_no_processor_list(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     jitted_run_processor_list(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)

       # scores should be equal
       self.assertTrue(jnp.allclose(_lowerCAmelCase							,							_lowerCAmelCase							,							atol=1E-3		)		)

       # input_ids should never be changed
       self.assertListEqual(input_ids.tolist()							,							input_ids_comp.tolist()		)
'''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						)
