





"""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


a__					:     int							=				[nn.LayerNorm]

a__					:     List[str]							=				logging.get_logger(__name__)

a__					:     List[str]							=				version.parse(version.parse(torch.__version__).base_version)

a__					:     Tuple							=				parsed_torch_version_base >= version.parse("""2.0""")
a__					:     Tuple							=				parsed_torch_version_base >= version.parse("""1.12""")
a__					:     Dict							=				parsed_torch_version_base >= version.parse("""1.11""")
a__					:     Dict							=				parsed_torch_version_base < version.parse("""1.11""")





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

 '''simple docstring'''






 from torch import _softmax_backward_data

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





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase = 0	)    ->  nn.Linear:

 '''simple docstring'''






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



class      lowercase_				(     nn.Module							):

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase = 1	)    ->  ConvaD:

 '''simple docstring'''






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





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase = None	)    ->  Union[nn.Linear, ConvaD]:

 '''simple docstring'''






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





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     *_UpperCAmelCase	)    ->  torch.Tensor:

 '''simple docstring'''






 assert len(_UpperCAmelCase	) > 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
 snake_case__    							=				len(inspect.signature(_UpperCAmelCase	).parameters	)
 if num_args_in_forward_chunk_fn != len(_UpperCAmelCase	):
  raise ValueError(
      f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_UpperCAmelCase	)} input "
      "tensors are given"	)

 if chunk_size > 0:
  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}"	)

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

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

 return forward_fn(*_UpperCAmelCase	)





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Tuple[Set[int], torch.LongTensor]:

 '''simple docstring'''






 snake_case__    							=				torch.ones(_UpperCAmelCase	,     _UpperCAmelCase	)
 snake_case__    							=				set(_UpperCAmelCase	) - 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
  snake_case__    							=				head - sum(1 if h < head else 0 for h in already_pruned_heads	)
  snake_case__    							=				0
 snake_case__    							=				mask.view(-1	).contiguous().eq(1	)
 snake_case__    							=				torch.arange(len(_UpperCAmelCase	)	)[mask].long()
 return heads, index





def 							a__			(  *_UpperCAmelCase	,     _UpperCAmelCase = None	)    ->  Tuple[torch.Tensor, ...]:

 '''simple docstring'''






 return torch.meshgrid(*_UpperCAmelCase	,     indexing=_UpperCAmelCase	)





def 							a__			(  _UpperCAmelCase	)    ->  Tuple[torch.device, int, int]:

 '''simple docstring'''






 return tensor.device, storage_ptr(_UpperCAmelCase	), storage_size(_UpperCAmelCase	)








"""simple docstring"""


import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
   from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import PoolFormerImageProcessor



class      lowercase_				(     __lowerCAmelCase							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.config_class(**self.inputs_dict     )
        self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,					"hidden_sizes"     )     )
        self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,					"num_encoder_blocks"     )     )



class      lowercase_				:

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE=13   ,					__SCREAMING_SNAKE_CASE=64   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=4   ,					__SCREAMING_SNAKE_CASE=[2, 2, 2, 2]   ,					__SCREAMING_SNAKE_CASE=[8, 4, 2, 1]   ,					__SCREAMING_SNAKE_CASE=[16, 32, 64, 128]   ,					__SCREAMING_SNAKE_CASE=[1, 4, 8, 16]   ,					__SCREAMING_SNAKE_CASE=False   ,					__SCREAMING_SNAKE_CASE=True   ,					__SCREAMING_SNAKE_CASE="gelu"   ,					__SCREAMING_SNAKE_CASE=0.1   ,					__SCREAMING_SNAKE_CASE=0.02   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=None   ,					):






        '''simple docstring'''

        snake_case__    							=				parent
        snake_case__    							=				batch_size
        snake_case__    							=				image_size
        snake_case__    							=				num_channels
        snake_case__    							=				num_encoder_blocks
        snake_case__    							=				sr_ratios
        snake_case__    							=				depths
        snake_case__    							=				hidden_sizes
        snake_case__    							=				downsampling_rates
        snake_case__    							=				is_training
        snake_case__    							=				use_labels
        snake_case__    							=				hidden_act
        snake_case__    							=				hidden_dropout_prob
        snake_case__    							=				initializer_range
        snake_case__    							=				num_labels
        snake_case__    							=				scope

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

        snake_case__    							=				None
        if self.use_labels:
         snake_case__    							=				ids_tensor([self.batch_size, self.image_size, self.image_size]   ,					self.num_labels     )

        snake_case__    							=				PoolFormerConfig(
            image_size=self.image_size   ,					num_channels=self.num_channels   ,					num_encoder_blocks=self.num_encoder_blocks   ,					depths=self.depths   ,					hidden_sizes=self.hidden_sizes   ,					hidden_act=self.hidden_act   ,					hidden_dropout_prob=self.hidden_dropout_prob   ,					initializer_range=self.initializer_range   ,					)

        return config, pixel_values, labels

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				PoolFormerModel(config=__SCREAMING_SNAKE_CASE     )
        model.to(__SCREAMING_SNAKE_CASE     )
        model.eval()
        snake_case__    							=				model(__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				snake_case__    							=				self.image_size // 32.0
        self.parent.assertEqual(
            result.last_hidden_state.shape   ,					(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.prepare_config_and_inputs()
        snake_case__							,   snake_case__							,   snake_case__    							=				config_and_inputs
        snake_case__    							=				{"pixel_values": pixel_values}
        return config, inputs_dict



@require_torch
class      lowercase_				(     __lowerCAmelCase				,__lowerCAmelCase				,unittest.TestCase							):
       snake_case__				:							Optional[Any]											=     (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
       snake_case__				:							Dict											=     (
           {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
           if is_torch_available()
           else {}
       )

       snake_case__				:							Optional[int]											=     False
       snake_case__				:							Optional[Any]											=     False
       snake_case__				:							List[Any]											=     False
       snake_case__				:							Any											=     False
       snake_case__				:							Tuple											=     False

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				PoolFormerModelTester(self     )
        snake_case__    							=				PoolFormerConfigTester(self   ,					config_class=__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        self.config_tester.run_common_tests()

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )

       @unittest.skip("PoolFormer does not use inputs_embeds"     )
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        pass

       @unittest.skip("PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"     )
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        pass

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        def check_hidden_states_output(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):
         snake_case__    							=				model_class(__SCREAMING_SNAKE_CASE     )
         model.to(__SCREAMING_SNAKE_CASE     )
         model.eval()

         with torch.no_grad():
          snake_case__    							=				model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )     )

         snake_case__    							=				outputs.hidden_states

         snake_case__    							=				self.model_tester.num_encoder_blocks
         self.assertEqual(len(__SCREAMING_SNAKE_CASE     )   ,					__SCREAMING_SNAKE_CASE     )

         # verify the first hidden states (first block)
         self.assertListEqual(
             list(hidden_states[0].shape[-3:]     )   ,					[
                 self.model_tester.hidden_sizes[0],
                 self.model_tester.image_size // 4,
                 self.model_tester.image_size // 4,
             ]   ,					)

        snake_case__							,   snake_case__    							=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         snake_case__    							=				True
         check_hidden_states_output(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case__    							=				True

         check_hidden_states_output(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        if not self.model_tester.is_training:
         return

        snake_case__							,   snake_case__    							=				self.model_tester.prepare_config_and_inputs_for_common()
        snake_case__    							=				True

        for model_class in self.all_model_classes:
         if model_class in get_values(__SCREAMING_SNAKE_CASE     ):
          continue
         snake_case__    							=				model_class(__SCREAMING_SNAKE_CASE     )
         model.to(__SCREAMING_SNAKE_CASE     )
         model.train()
         snake_case__    							=				self._prepare_for_class(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					return_labels=__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				model(**__SCREAMING_SNAKE_CASE     ).loss
         loss.backward()

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__							,   snake_case__    							=				self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case__    							=				["pixel_values"]
         self.assertListEqual(arg_names[:1]   ,					__SCREAMING_SNAKE_CASE     )

       @slow
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case__    							=				PoolFormerModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
         self.assertIsNotNone(__SCREAMING_SNAKE_CASE     )





def 							a__			(  )    ->  int:

 '''simple docstring'''






 snake_case__    							=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
 return image



@require_torch
class      lowercase_				(     unittest.TestCase							):

       @slow
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				PoolFormerImageProcessor()
        snake_case__    							=				PoolFormerForImageClassification.from_pretrained("sail/poolformer_s12"     ).to(__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				image_processor(images=prepare_img()   ,					return_tensors="pt"     ).to(__SCREAMING_SNAKE_CASE     )

        # forward pass
        with torch.no_grad():
         snake_case__    							=				model(**__SCREAMING_SNAKE_CASE     )

        # verify the logits
        snake_case__    							=				torch.Size((1, 1000)     )
        self.assertEqual(outputs.logits.shape   ,					__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				torch.tensor([-0.6113, 0.1685, -0.0492]     ).to(__SCREAMING_SNAKE_CASE     )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,					__SCREAMING_SNAKE_CASE   ,					atol=1e-4     )     )

