






import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import MaskFormerSwinBackbone
   from transformers.models.maskformer import MaskFormerSwinModel


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						List[Any]       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						Optional[Any]=13       ,			UpperCAmelCase_							:						List[Any]=32       ,			UpperCAmelCase_							:						Dict=2       ,			UpperCAmelCase_							:						Optional[Any]=3       ,			UpperCAmelCase_							:						int=16       ,			UpperCAmelCase_							:						Any=[1, 2, 1]       ,			UpperCAmelCase_							:						Any=[2, 2, 4]       ,			UpperCAmelCase_							:						Tuple=2       ,			UpperCAmelCase_							:						Union[str, Any]=2.0       ,			UpperCAmelCase_							:						Any=True       ,			UpperCAmelCase_							:						int=0.0       ,			UpperCAmelCase_							:						str=0.0       ,			UpperCAmelCase_							:						Any=0.1       ,			UpperCAmelCase_							:						List[Any]="gelu"       ,			UpperCAmelCase_							:						int=False       ,			UpperCAmelCase_							:						Optional[int]=True       ,			UpperCAmelCase_							:						Optional[Any]=0.02       ,			UpperCAmelCase_							:						int=1E-5       ,			UpperCAmelCase_							:						Dict=True       ,			UpperCAmelCase_							:						List[str]=None       ,			UpperCAmelCase_							:						List[Any]=True       ,			UpperCAmelCase_							:						Optional[Any]=10       ,			UpperCAmelCase_							:						Optional[Any]=8       ,			UpperCAmelCase_							:						Dict=["stage1", "stage2", "stage3"]       ,			UpperCAmelCase_							:						Optional[int]=[1, 2, 3]       ,			):
       SCREAMING_SNAKE_CASE							:   Any						    =     parent
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     batch_size
       SCREAMING_SNAKE_CASE							:   List[str]						    =     image_size
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     patch_size
       SCREAMING_SNAKE_CASE							:   List[str]						    =     num_channels
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     embed_dim
       SCREAMING_SNAKE_CASE							:   Tuple						    =     depths
       SCREAMING_SNAKE_CASE							:   List[str]						    =     num_heads
       SCREAMING_SNAKE_CASE							:   Any						    =     window_size
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     mlp_ratio
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     qkv_bias
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     hidden_dropout_prob
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE							:   List[str]						    =     drop_path_rate
       SCREAMING_SNAKE_CASE							:   int						    =     hidden_act
       SCREAMING_SNAKE_CASE							:   int						    =     use_absolute_embeddings
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     patch_norm
       SCREAMING_SNAKE_CASE							:   Dict						    =     layer_norm_eps
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     initializer_range
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     is_training
       SCREAMING_SNAKE_CASE							:   str						    =     scope
       SCREAMING_SNAKE_CASE							:   Dict						    =     use_labels
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     type_sequence_label_size
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     encoder_stride
       SCREAMING_SNAKE_CASE							:   int						    =     out_features
       SCREAMING_SNAKE_CASE							:   int						    =     out_indices


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     None
       if self.use_labels:
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     ids_tensor([self.batch_size]       ,			self.type_sequence_label_size   )

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.get_config()

       return config, pixel_values, labels


     def 					_A							(					self							:						List[str]   ):
       return MaskFormerSwinConfig(
           image_size=self.image_size       ,			patch_size=self.patch_size       ,			num_channels=self.num_channels       ,			embed_dim=self.embed_dim       ,			depths=self.depths       ,			num_heads=self.num_heads       ,			window_size=self.window_size       ,			mlp_ratio=self.mlp_ratio       ,			qkv_bias=self.qkv_bias       ,			hidden_dropout_prob=self.hidden_dropout_prob       ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,			drop_path_rate=self.drop_path_rate       ,			hidden_act=self.hidden_act       ,			use_absolute_embeddings=self.use_absolute_embeddings       ,			path_norm=self.patch_norm       ,			layer_norm_eps=self.layer_norm_eps       ,			initializer_range=self.initializer_range       ,			encoder_stride=self.encoder_stride       ,			out_features=self.out_features       ,			out_indices=self.out_indices       ,			)


     def 					_A							(					self							:						List[Any]       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     MaskFormerSwinModel(config=UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   Tuple						    =     model(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   int						    =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     int(config.embed_dim * 2 ** (len(config.depths   ) - 1)   )

       self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, expected_seq_len, expected_dim)   )


     def 					_A							(					self							:						List[Any]       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   int						    =     MaskFormerSwinBackbone(config=UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   str						    =     model(UpperCAmelCase_   )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps   )       ,			len(config.out_features   )   )
       self.parent.assertListEqual(list(result.feature_maps[0].shape   )       ,			[13, 16, 16, 16]   )

       # verify channels
       self.parent.assertEqual(len(model.channels   )       ,			len(config.out_features   )   )
       self.parent.assertListEqual(model.channels       ,			[16, 32, 64]   )

       # verify ValueError
       with self.parent.assertRaises(UpperCAmelCase_   ):
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     ["stem"]
         SCREAMING_SNAKE_CASE							:   List[str]						    =     MaskFormerSwinBackbone(config=UpperCAmelCase_   )






     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   int						    =     self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     config_and_inputs
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     {"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[Any]								=						(
         (
             MaskFormerSwinModel,
             MaskFormerSwinBackbone,
         )
         if is_torch_available()
         else ()
     )
     UpperCamelCase_				:		Tuple								=						{'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
     UpperCamelCase_				:		Any								=						False
     UpperCamelCase_				:		Tuple								=						False
     UpperCamelCase_				:		int								=						False
     UpperCamelCase_				:		int								=						False
     UpperCamelCase_				:		int								=						False


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     MaskFormerSwinModelTester(self   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     ConfigTester(self       ,			config_class=UpperCAmelCase_       ,			embed_dim=37   )


     @require_torch_multi_gpu
     @unittest.skip(
         reason=(
             "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
             " `nn.DataParallel`"
         )   )
     def 					_A							(					self							:						Optional[Any]   ):
       pass


     def 					_A							(					self							:						Union[str, Any]   ):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()


     def 					_A							(					self							:						Union[str, Any]   ):
       return


     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase_   )


     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*UpperCAmelCase_   )


     @unittest.skip("Swin does not use inputs_embeds"   )
     def 					_A							(					self							:						Tuple   ):
       pass


     @unittest.skip("Swin does not support feedforward chunking"   )
     def 					_A							(					self							:						Any   ):
       pass


     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Any						    =     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   List[str]						    =     model_class(UpperCAmelCase_   )
         self.assertIsInstance(model.get_input_embeddings()       ,			(nn.Module)   )
         SCREAMING_SNAKE_CASE							:   List[str]						    =     model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(UpperCAmelCase_       ,			nn.Linear   )   )


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   List[str]						    =     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     model_class(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Dict						    =     inspect.signature(model.forward   )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         SCREAMING_SNAKE_CASE							:   List[str]						    =     [*signature.parameters.keys()]

         SCREAMING_SNAKE_CASE							:   Dict						    =     ["pixel_values"]
         self.assertListEqual(arg_names[:1]       ,			UpperCAmelCase_   )


     @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions"   )
     def 					_A							(					self							:						Optional[Any]   ):
       pass


     @unittest.skip(reason="MaskFormerSwin is only used as an internal backbone"   )
     def 					_A							(					self							:						List[str]   ):
       pass


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						Optional[Any]       ,			UpperCAmelCase_							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model_class(UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()

       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   List[str]						    =     model(**self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )   )

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     outputs.hidden_states

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     getattr(
           self.model_tester       ,			"expected_num_hidden_layers"       ,			len(self.model_tester.depths   ) + 1   )
       self.assertEqual(len(UpperCAmelCase_   )       ,			UpperCAmelCase_   )

       # Swin has a different seq_length
       SCREAMING_SNAKE_CASE							:   Any						    =     (
           config.patch_size
           if isinstance(config.patch_size       ,			collections.abc.Iterable   )
           else (config.patch_size, config.patch_size)
       )

       SCREAMING_SNAKE_CASE							:   Any						    =     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]   )       ,			[num_patches, self.model_tester.embed_dim]       ,			)


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   str						    =     self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size       ,			collections.abc.Iterable   )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   Dict						    =     True
         self.check_hidden_states_output(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     True

         self.check_hidden_states_output(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     3

       SCREAMING_SNAKE_CASE							:   Dict						    =     (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size       ,			collections.abc.Iterable   )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     (
           config.patch_size
           if isinstance(config.patch_size       ,			collections.abc.Iterable   )
           else (config.patch_size, config.patch_size)
       )

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       SCREAMING_SNAKE_CASE							:   Dict						    =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     True
         self.check_hidden_states_output(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			(padded_height, padded_width)   )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         SCREAMING_SNAKE_CASE							:   Any						    =     True
         self.check_hidden_states_output(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			(padded_height, padded_width)   )


     @unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints"   )
     def 					_A							(					self							:						List[Any]   ):
       pass


     @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"   )
     def 					_A							(					self							:						int   ):
       pass


     @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"   )
     def 					_A							(					self							:						Optional[int]   ):
       pass






     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.model_tester.prepare_config_and_inputs_for_common()

       def set_nan_tensor_to_zero(UpperCAmelCase_							:						Any   ):
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     0
         return t

       def check_equivalence(UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						Optional[Any]       ,			UpperCAmelCase_							:						Optional[Any]={}   ):
         with torch.no_grad():
           SCREAMING_SNAKE_CASE							:   List[Any]						    =     model(**UpperCAmelCase_       ,			return_dict=UpperCAmelCase_       ,			**UpperCAmelCase_   )
           SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model(**UpperCAmelCase_       ,			return_dict=UpperCAmelCase_       ,			**UpperCAmelCase_   ).to_tuple()

           def recursive_check(UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Tuple   ):
             if isinstance(UpperCAmelCase_       ,			(List, Tuple)   ):
               for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase_       ,			UpperCAmelCase_   ):
                 recursive_check(UpperCAmelCase_       ,			UpperCAmelCase_   )
             elif isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
               for tuple_iterable_value, dict_iterable_value in zip(
                   tuple_object.values()       ,			dict_object.values()   ):
                 recursive_check(UpperCAmelCase_       ,			UpperCAmelCase_   )
             elif tuple_object is None:
               return
             else:
               self.assertTrue(
                   torch.allclose(
                       set_nan_tensor_to_zero(UpperCAmelCase_   )       ,			set_nan_tensor_to_zero(UpperCAmelCase_   )       ,			atol=1E-5   )       ,			msg=(
                       "Tuple and dict output are not equal. Difference:"
                       f''' {torch.max(torch.abs(tuple_object - dict_object   )   )}. Tuple has `nan`:'''
                       f''' {torch.isnan(UpperCAmelCase_   ).any()} and `inf`: {torch.isinf(UpperCAmelCase_   )}. Dict has'''
                       f''' `nan`: {torch.isnan(UpperCAmelCase_   ).any()} and `inf`: {torch.isinf(UpperCAmelCase_   )}.'''
                   )       ,			)

           recursive_check(UpperCAmelCase_       ,			UpperCAmelCase_   )

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   Any						    =     model_class(UpperCAmelCase_   )
         model.to(UpperCAmelCase_   )
         model.eval()

         SCREAMING_SNAKE_CASE							:   Tuple						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Any						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )
         check_equivalence(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )

         SCREAMING_SNAKE_CASE							:   Dict						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_       ,			return_labels=UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Dict						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_       ,			return_labels=UpperCAmelCase_   )
         check_equivalence(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )

         SCREAMING_SNAKE_CASE							:   List[str]						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   List[str]						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )
         check_equivalence(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			{"output_hidden_states": True}   )

         SCREAMING_SNAKE_CASE							:   int						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_       ,			return_labels=UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_       ,			return_labels=UpperCAmelCase_   )
         check_equivalence(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			{"output_hidden_states": True}   )





@require_torch
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase   ,		lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		List[Any]								=						(MaskFormerSwinBackbone,) if is_torch_available() else ()
     UpperCamelCase_				:		Optional[int]								=						MaskFormerSwinConfig


     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   str						    =     MaskFormerSwinModelTester(self   )






     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE							:   int						    =     inputs_dict["pixel_values"].shape[0]

       for backbone_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     backbone_class(UpperCAmelCase_   )
         backbone.to(UpperCAmelCase_   )
         backbone.eval()

         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     backbone(**UpperCAmelCase_   )

         # Test default outputs and verify feature maps
         self.assertIsInstance(outputs.feature_maps       ,			UpperCAmelCase_   )
         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							:   Optional[int]						    =     backbone(**UpperCAmelCase_       ,			output_hidden_states=UpperCAmelCase_   )
         self.assertIsNotNone(outputs.hidden_states   )
         self.assertTrue(len(outputs.hidden_states   )       ,			len(backbone.stage_names   )   )
         # We skip the stem layer
         for hidden_states, n_channels in zip(outputs.hidden_states[1:]       ,			backbone.channels   ):
           for hidden_state in hidden_states:
             # Hidden states are in the format (batch_size, (height * width), n_channels)
             SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   str						    =     hidden_state.shape
             self.assertTrue((h_batch_size, h_n_channels)       ,			(batch_size, n_channels)   )

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












import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	return 1.0 / (1.0 + np.exp(-_outputs       ))





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	a									=				np.max(_outputs				,     axis=-1				,     keepdims=__lowerCamelCase       )
	a									=				np.exp(_outputs - maxes       )
	return shifted_exp / shifted_exp.sum(axis=-1				,     keepdims=__lowerCamelCase       )

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''sigmoid'''
							UpperCamelCase__   	=    '''softmax'''
							UpperCamelCase__   	=    '''none'''







@add_end_docstrings(
    __magic_name__ ,			r'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    ''' ,			)
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    ClassificationFunction.NONE
							def __init__(		self		:List[str]						, **__magic_name__		:List[Any]							):







								'''simple docstring'''







								super().__init__(**__magic_name__							)

								self.check_model_type(
								    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
								    if self.framework == """tf"""
								    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING							)
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int=None						, __magic_name__		:Any=None						, __magic_name__		:Union[str, Any]=""						, **__magic_name__		:Tuple							):







								'''simple docstring'''







								a									=				tokenizer_kwargs

								a									=				{}
								if hasattr(self.model.config						, """return_all_scores"""							) and return_all_scores is None:
									a									=				self.model.config.return_all_scores

								if isinstance(__magic_name__						, __magic_name__							) or top_k is None:
									a									=				top_k
									a									=				False
								elif return_all_scores is not None:
									warnings.warn(
									    """`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"""
									    """ `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."""						, __magic_name__						, )
									if return_all_scores:
										a									=				None
									else:
										a									=				1

								if isinstance(__magic_name__						, __magic_name__							):
									a									=				ClassificationFunction[function_to_apply.upper()]

								if function_to_apply is not None:
									a									=				function_to_apply
								return preprocess_params, {}, postprocess_params
							def __call__(		self		:Dict						, *__magic_name__		:Optional[int]						, **__magic_name__		:Optional[Any]							):







								'''simple docstring'''







								a									=				super().__call__(*__magic_name__						, **__magic_name__							)
								# TODO try and retrieve it in a nicer way from _sanitize_parameters.
								a									=				"""top_k""" not in kwargs
								if isinstance(args[0]						, __magic_name__							) and _legacy:
									# This pipeline is odd, and return a list when single item is run
									return [result]
								else:
									return result
							def  lowerCamelCase__				(		self		:Union[str, Any]						, __magic_name__		:Optional[Any]						, **__magic_name__		:Optional[Any]							):







								'''simple docstring'''







								a									=				self.framework
								if isinstance(__magic_name__						, __magic_name__							):
									return self.tokenizer(**__magic_name__						, return_tensors=__magic_name__						, **__magic_name__							)
								elif isinstance(__magic_name__						, __magic_name__							) and len(__magic_name__							) == 1 and isinstance(inputs[0]						, __magic_name__							) and len(inputs[0]							) == 2:
									# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
									return self.tokenizer(
									    text=inputs[0][0]						, text_pair=inputs[0][1]						, return_tensors=__magic_name__						, **__magic_name__							)
								elif isinstance(__magic_name__						, __magic_name__							):
									# This is likely an invalid usage of the pipeline attempting to pass text pairs.
									raise ValueError(
									    """The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"""
									    """ dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."""							)
								return self.tokenizer(__magic_name__						, return_tensors=__magic_name__						, **__magic_name__							)
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:Tuple							):







								'''simple docstring'''







								return self.model(**__magic_name__							)





							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Union[str, Any]						, __magic_name__		:int=None						, __magic_name__		:Union[str, Any]=1						, __magic_name__		:Tuple=True							):







								'''simple docstring'''







								if function_to_apply is None:
									if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
										a									=				ClassificationFunction.SIGMOID
									elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
										a									=				ClassificationFunction.SOFTMAX
									elif hasattr(self.model.config						, """function_to_apply"""							) and function_to_apply is None:
										a									=				self.model.config.function_to_apply
									else:
										a									=				ClassificationFunction.NONE

								a									=				model_outputs["""logits"""][0]
								a									=				outputs.numpy()

								if function_to_apply == ClassificationFunction.SIGMOID:
									a									=				sigmoid(__magic_name__							)
								elif function_to_apply == ClassificationFunction.SOFTMAX:
									a									=				softmax(__magic_name__							)
								elif function_to_apply == ClassificationFunction.NONE:
									a									=				outputs
								else:
									raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}'							)

								if top_k == 1 and _legacy:
									return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

								a									=				[
								    {"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(__magic_name__							)
								]
								if not _legacy:
									dict_scores.sort(key=lambda __magic_name__							: x["score"]						, reverse=__magic_name__							)
									if top_k is not None:
										a									=				dict_scores[:top_k]
								return dict_scores

