
"""simple docstring"""




import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType




@require_torch
class 	UpperCamelCase      :

				"""simple docstring"""



				UpperCAmelCase_							: int		    =							()
				UpperCAmelCase_							: str		    =							True


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.config_class

									# test default config
									SCREAMING_SNAKE_CASE           =      config_class()
									self.assertIsNotNone(lowercase__     )
									SCREAMING_SNAKE_CASE           =      ['stem'] + [f'''stage{idx}''' for idx in range(1						,      len(config.depths     ) + 1     )]
									self.assertEqual(config.stage_names						,      lowercase__     )
									self.assertTrue(set(config.out_features     ).issubset(set(config.stage_names     )     )     )

									# Test out_features and out_indices are correctly set
									# out_features and out_indices both None
									SCREAMING_SNAKE_CASE           =      config_class(out_features=lowercase__						,      out_indices=lowercase__     )
									self.assertEqual(config.out_features						,      [config.stage_names[-1]]     )
									self.assertEqual(config.out_indices						,      [len(config.stage_names     ) - 1]     )

									# out_features and out_indices both set
									SCREAMING_SNAKE_CASE           =      config_class(out_features=['stem', 'stage1']						,      out_indices=[0, 1]     )
									self.assertEqual(config.out_features						,      ['stem', 'stage1']     )
									self.assertEqual(config.out_indices						,      [0, 1]     )

									# Only out_features set
									SCREAMING_SNAKE_CASE           =      config_class(out_features=['stage1', 'stage3']     )
									self.assertEqual(config.out_features						,      ['stage1', 'stage3']     )
									self.assertEqual(config.out_indices						,      [1, 3]     )

									# Only out_indices set
									SCREAMING_SNAKE_CASE           =      config_class(out_indices=[0, 2]     )
									self.assertEqual(config.out_features						,      [config.stage_names[0], config.stage_names[2]]     )
									self.assertEqual(config.out_indices						,      [0, 2]     )

									# Error raised when out_indices do not correspond to out_features
									with self.assertRaises(lowercase__     ):
														SCREAMING_SNAKE_CASE           =      config_class(out_features=['stage1', 'stage2']						,      out_indices=[0, 2]     )


				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE           =      model_class(lowercase__     )
														SCREAMING_SNAKE_CASE           =      inspect.signature(model.forward     )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														SCREAMING_SNAKE_CASE           =      [*signature.parameters.keys()]
														SCREAMING_SNAKE_CASE           =      ['pixel_values']
														self.assertListEqual(arg_names[:1]						,      lowercase__     )


				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE           =      model_class(lowercase__     )
														self.assertEqual(len(model.channels     )						,      len(config.out_features     )     )
														SCREAMING_SNAKE_CASE           =      model.num_features
														SCREAMING_SNAKE_CASE           =      [config.stage_names.index(lowercase__     ) for feat in config.out_features]
														SCREAMING_SNAKE_CASE           =      [num_features[idx] for idx in out_indices]
														self.assertListEqual(model.channels						,      lowercase__     )

														SCREAMING_SNAKE_CASE           =      copy.deepcopy(lowercase__     )
														SCREAMING_SNAKE_CASE           =      None
														SCREAMING_SNAKE_CASE           =      model_class(lowercase__     )
														self.assertEqual(len(model.channels     )						,      1     )
														self.assertListEqual(model.channels						,      [num_features[-1]]     )

														SCREAMING_SNAKE_CASE           =      copy.deepcopy(lowercase__     )
														SCREAMING_SNAKE_CASE           =      None
														SCREAMING_SNAKE_CASE           =      model_class(lowercase__     )
														self.assertEqual(len(model.channels     )						,      1     )
														self.assertListEqual(model.channels						,      [num_features[-1]]     )


				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE           =      model_class(lowercase__     )
														model.to(lowercase__     )
														model.eval()
														SCREAMING_SNAKE_CASE           =      model(**lowercase__     )

														self.assertEqual(len(result.feature_maps     )						,      len(config.out_features     )     )
														self.assertEqual(len(model.channels     )						,      len(config.out_features     )     )
														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
														SCREAMING_SNAKE_CASE           =      copy.deepcopy(lowercase__     )
														SCREAMING_SNAKE_CASE           =      None
														SCREAMING_SNAKE_CASE           =      model_class(lowercase__     )
														model.to(lowercase__     )
														model.eval()
														SCREAMING_SNAKE_CASE           =      model(**lowercase__     )

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

														SCREAMING_SNAKE_CASE           =      copy.deepcopy(lowercase__     )
														SCREAMING_SNAKE_CASE           =      None
														SCREAMING_SNAKE_CASE           =      model_class(lowercase__     )
														model.to(lowercase__     )
														model.eval()
														SCREAMING_SNAKE_CASE           =      model(**lowercase__     )

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

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


				def 				A   (							self     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      self.model_tester.prepare_config_and_inputs_for_common()

									for backbone_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE           =      backbone_class(lowercase__     )

														self.assertTrue(hasattr(lowercase__						,      'backbone_type'     )     )
														self.assertTrue(hasattr(lowercase__						,      'stage_names'     )     )
														self.assertTrue(hasattr(lowercase__						,      'num_features'     )     )
														self.assertTrue(hasattr(lowercase__						,      'out_indices'     )     )
														self.assertTrue(hasattr(lowercase__						,      'out_features'     )     )
														self.assertTrue(hasattr(lowercase__						,      'out_feature_channels'     )     )
														self.assertTrue(hasattr(lowercase__						,      'channels'     )     )

														self.assertIsInstance(backbone.backbone_type						,      lowercase__     )
														# Verify num_features has been initialized in the backbone init
														self.assertIsNotNone(backbone.num_features     )
														self.assertTrue(len(backbone.channels     ) == len(backbone.out_indices     )     )
														self.assertTrue(len(backbone.stage_names     ) == len(backbone.num_features     )     )
														self.assertTrue(len(backbone.channels     ) <= len(backbone.num_features     )     )
														self.assertTrue(len(backbone.out_feature_channels     ) == len(backbone.stage_names     )     )




				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE           =      inputs_dict['pixel_values'].shape[0]

									for backbone_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE           =      backbone_class(lowercase__     )
														backbone.to(lowercase__     )
														backbone.eval()

														SCREAMING_SNAKE_CASE           =      backbone(**lowercase__     )

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

														# Test output_hidden_states=True
														SCREAMING_SNAKE_CASE           =      backbone(**lowercase__						,      output_hidden_states=lowercase__     )
														self.assertIsNotNone(outputs.hidden_states     )
														self.assertTrue(len(outputs.hidden_states     )						,      len(backbone.stage_names     )     )
														for hidden_state, n_channels in zip(outputs.hidden_states						,      backbone.channels     ):
																			self.assertTrue(hidden_state.shape[:2]						,      (batch_size, n_channels)     )

														# Test output_attentions=True
														if self.has_attentions:
																			SCREAMING_SNAKE_CASE           =      backbone(**lowercase__						,      output_attentions=lowercase__     )
																			self.assertIsNotNone(outputs.attentions     )


"""simple docstring"""




import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


snake_case					  =						[nn.LayerNorm]

snake_case					  =						logging.get_logger(__name__)

snake_case					  =						version.parse(version.parse(torch.__version__).base_version)

snake_case					  =						parsed_torch_version_base >= version.parse('2.0')
snake_case					  =						parsed_torch_version_base >= version.parse('1.12')
snake_case					  =						parsed_torch_version_base >= version.parse('1.11')
snake_case					  =						parsed_torch_version_base < version.parse('1.11')

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	self     ):
					from torch import _softmax_backward_data

					if is_torch_less_than_1_11:
										return _softmax_backward_data(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	parent.dim,	self     )
					else:
										return _softmax_backward_data(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	parent.dim,	self.dtype     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 0     ):
					SCREAMING_SNAKE_CASE           =      index.to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      layer.weight.index_select(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ).clone().detach()
					if layer.bias is not None:
										if dim == 1:
															SCREAMING_SNAKE_CASE           =      layer.bias.clone().detach()
										else:
															SCREAMING_SNAKE_CASE           =      layer.bias[index].clone().detach()
					SCREAMING_SNAKE_CASE           =      list(layer.weight.size()     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      nn.Linear(new_size[1],	new_size[0],	bias=layer.bias is not None     ).to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      False
					new_layer.weight.copy_(W.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					if layer.bias is not None:
										SCREAMING_SNAKE_CASE           =      False
										new_layer.bias.copy_(b.contiguous()     )
										SCREAMING_SNAKE_CASE           =      True
					return new_layer




class 	UpperCamelCase      (			nn.Module ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__     )       ->       List[Any]:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =      nf
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.empty(lowercase__						,      lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.zeros(lowercase__     )     )
									nn.init.normal_(self.weight						,      std=0.02     )




				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      x.size()[:-1] + (self.nf,)
									SCREAMING_SNAKE_CASE           =      torch.addmm(self.bias						,      x.view(-1						,      x.size(-1     )     )						,      self.weight     )
									SCREAMING_SNAKE_CASE           =      x.view(lowercase__     )
									return x






def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 1     ):
					SCREAMING_SNAKE_CASE           =      index.to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      layer.weight.index_select(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ).clone().detach()
					if dim == 0:
										SCREAMING_SNAKE_CASE           =      layer.bias.clone().detach()
					else:
										SCREAMING_SNAKE_CASE           =      layer.bias[index].clone().detach()
					SCREAMING_SNAKE_CASE           =      list(layer.weight.size()     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      ConvaD(new_size[1],	new_size[0]     ).to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      False
					new_layer.weight.copy_(W.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					SCREAMING_SNAKE_CASE           =      False
					new_layer.bias.copy_(b.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					return new_layer

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					if isinstance(SCREAMING_SNAKE_CASE_,	nn.Linear     ):
										return prune_linear_layer(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	dim=0 if dim is None else dim     )
					elif isinstance(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
										return prune_convad_layer(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	dim=1 if dim is None else dim     )
					else:
										raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	*SCREAMING_SNAKE_CASE_     ):
					assert len(SCREAMING_SNAKE_CASE_     ) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

					# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
					SCREAMING_SNAKE_CASE           =      len(inspect.signature(SCREAMING_SNAKE_CASE_     ).parameters     )
					if num_args_in_forward_chunk_fn != len(SCREAMING_SNAKE_CASE_     ):
										raise ValueError(
										    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(SCREAMING_SNAKE_CASE_     )} input '''
										    'tensors are given'     )

					if chunk_size > 0:
										SCREAMING_SNAKE_CASE           =      input_tensors[0].shape[chunk_dim]
										for input_tensor in input_tensors:
															if input_tensor.shape[chunk_dim] != tensor_shape:
																				raise ValueError(
																				    f'''All input tenors have to be of the same shape: {tensor_shape}, '''
																				    f'''found shape {input_tensor.shape[chunk_dim]}'''     )

										if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
															raise ValueError(
															    f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
															    f'''size {chunk_size}'''     )

										SCREAMING_SNAKE_CASE           =      input_tensors[0].shape[chunk_dim] // chunk_size

										# chunk input tensor into tuples
										SCREAMING_SNAKE_CASE           =      tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE_,	dim=SCREAMING_SNAKE_CASE_     ) for input_tensor in input_tensors     )
										# apply forward fn to every tuple
										SCREAMING_SNAKE_CASE           =      tuple(forward_fn(*SCREAMING_SNAKE_CASE_     ) for input_tensors_chunk in zip(*SCREAMING_SNAKE_CASE_     )     )
										# concatenate output at same dimension
										return torch.cat(SCREAMING_SNAKE_CASE_,	dim=SCREAMING_SNAKE_CASE_     )

					return forward_fn(*SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      torch.ones(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      set(SCREAMING_SNAKE_CASE_     ) - already_pruned_heads  # Convert to set and remove already pruned heads
					for head in heads:
										# Compute how many pruned heads are before the head and move the index accordingly
										SCREAMING_SNAKE_CASE           =      head - sum(1 if h < head else 0 for h in already_pruned_heads     )
										SCREAMING_SNAKE_CASE           =      0
					SCREAMING_SNAKE_CASE           =      mask.view(-1     ).contiguous().eq(1     )
					SCREAMING_SNAKE_CASE           =      torch.arange(len(SCREAMING_SNAKE_CASE_     )     )[mask].long()
					return heads, index

def     UpperCamelCase_    (						*SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					return torch.meshgrid(*SCREAMING_SNAKE_CASE_,	indexing=SCREAMING_SNAKE_CASE_     )



def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE_     ), storage_size(SCREAMING_SNAKE_CASE_     )
