"""simple docstring"""





import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
       import torch
       from torch import nn

       from transformers import DonutSwinModel
       from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


class 					UpperCAmelCase_     :


      def __init__(       self		,      a		,      a=1_3		,      a=3_2		,      a=2		,      a=3		,      a=1_6		,      a=[1, 2, 1]		,      a=[2, 2, 4]		,      a=2		,      a=2.0		,      a=True		,      a=0.0		,      a=0.0		,      a=0.1		,      a="gelu"		,      a=False		,      a=True		,      a=0.02		,      a=1e-5		,      a=True		,      a=None		,      a=True		,      a=1_0		,      a=8		,      )	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     parent
             lowercase__      :		Union[str, Any]          =     batch_size
             lowercase__      :		int          =     image_size
             lowercase__      :		Tuple          =     patch_size
             lowercase__      :		Optional[int]          =     num_channels
             lowercase__      :		List[str]          =     embed_dim
             lowercase__      :		Dict          =     depths
             lowercase__      :		Union[str, Any]          =     num_heads
             lowercase__      :		Tuple          =     window_size
             lowercase__      :		Tuple          =     mlp_ratio
             lowercase__      :		Tuple          =     qkv_bias
             lowercase__      :		Dict          =     hidden_dropout_prob
             lowercase__      :		List[Any]          =     attention_probs_dropout_prob
             lowercase__      :		Optional[int]          =     drop_path_rate
             lowercase__      :		Union[str, Any]          =     hidden_act
             lowercase__      :		Optional[int]          =     use_absolute_embeddings
             lowercase__      :		List[str]          =     patch_norm
             lowercase__      :		Optional[Any]          =     layer_norm_eps
             lowercase__      :		str          =     initializer_range
             lowercase__      :		Optional[Any]          =     is_training
             lowercase__      :		Union[str, Any]          =     scope
             lowercase__      :		List[str]          =     use_labels
             lowercase__      :		int          =     type_sequence_label_size
             lowercase__      :		Dict          =     encoder_stride


      def 		_UpperCAmelCase	(       self							)	-> Any:
             lowercase__      :		Optional[Any]          =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__      :		Tuple          =     None
             if self.use_labels:
                    lowercase__      :		int          =     ids_tensor([self.batch_size]		,      self.type_sequence_label_size							)

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

             return config, pixel_values, labels


      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             return DonutSwinConfig(
                 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		,      )


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a							)	-> str:
             lowercase__      :		Tuple          =     DonutSwinModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__      :		Dict          =     model(a							)

             lowercase__      :		Optional[int]          =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
             lowercase__      :		int          =     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 		_UpperCAmelCase	(       self							)	-> Any:
             lowercase__      :		Dict          =     self.prepare_config_and_inputs()
             (
                 (
                 lowercase__
             )   ,				(
                 lowercase__
             )   ,				(
                 lowercase__
             )   ,				
             )      :		Dict          =     config_and_inputs
             lowercase__      :		Union[str, Any]          =     {'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class 					UpperCAmelCase_     (   _a			,							_a			,							unittest.TestCase):
      lowerCamelCase__		:   str			  =  (DonutSwinModel,) if is_torch_available() else ()
      lowerCamelCase__		:   int			  =  {"feature-extraction": DonutSwinModel} if is_torch_available() else {}
      lowerCamelCase__		:   Dict			  =  True

      lowerCamelCase__		:   Any			  =  False
      lowerCamelCase__		:   Dict			  =  False
      lowerCamelCase__		:   Tuple			  =  False


      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		Dict          =     DonutSwinModelTester(self							)
             lowercase__      :		Dict          =     ConfigTester(self		,      config_class=a		,      embed_dim=3_7							)


      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             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 		_UpperCAmelCase	(       self							)	-> Any:
             return


      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             lowercase__      :		int          =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 		_UpperCAmelCase	(       self							)	-> str:
             # DonutSwin does not use inputs_embeds
             pass


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             lowercase__   ,				lowercase__      :		Any          =     self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__      :		List[Any]          =     model_class(a							)
                    self.assertIsInstance(model.get_input_embeddings()		,      (nn.Module)							)
                    lowercase__      :		Tuple          =     model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(a		,      nn.Linear							)							)


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             lowercase__   ,				lowercase__      :		List[str]          =     self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__      :		List[Any]          =     model_class(a							)
                    lowercase__      :		Union[str, Any]          =     inspect.signature(model.forward							)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    lowercase__      :		Dict          =     [*signature.parameters.keys()]

                    lowercase__      :		Dict          =     ['pixel_values']
                    self.assertListEqual(arg_names[:1]		,      a							)


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__   ,				lowercase__      :		Union[str, Any]          =     self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__      :		Any          =     True

             for model_class in self.all_model_classes:
                    lowercase__      :		Union[str, Any]          =     True
                    lowercase__      :		int          =     False
                    lowercase__      :		List[Any]          =     True
                    lowercase__      :		Union[str, Any]          =     model_class(a							)
                    model.to(a							)
                    model.eval()
                    with torch.no_grad():
                           lowercase__      :		int          =     model(**self._prepare_for_class(a		,      a							)							)
                    lowercase__      :		str          =     outputs.attentions
                    lowercase__      :		List[str]          =     len(self.model_tester.depths							)
                    self.assertEqual(len(a							)		,      a							)

                    # check that output_attentions also work using config
                    del inputs_dict["output_attentions"]
                    lowercase__      :		Any          =     True
                    lowercase__      :		Any          =     config.window_size**2
                    lowercase__      :		Optional[int]          =     model_class(a							)
                    model.to(a							)
                    model.eval()
                    with torch.no_grad():
                           lowercase__      :		Optional[Any]          =     model(**self._prepare_for_class(a		,      a							)							)
                    lowercase__      :		Optional[int]          =     outputs.attentions
                    self.assertEqual(len(a							)		,      a							)

                    self.assertListEqual(
                        list(attentions[0].shape[-3:]							)		,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,      )
                    lowercase__      :		List[Any]          =     len(a							)

                    # Check attention is always last and order is fine
                    lowercase__      :		Optional[int]          =     True
                    lowercase__      :		Dict          =     True
                    lowercase__      :		List[str]          =     model_class(a							)
                    model.to(a							)
                    model.eval()
                    with torch.no_grad():
                           lowercase__      :		Tuple          =     model(**self._prepare_for_class(a		,      a							)							)

                    if hasattr(self.model_tester		,      'num_hidden_states_types'							):
                           lowercase__      :		Dict          =     self.model_tester.num_hidden_states_types
                    else:
                           # also another +1 for reshaped_hidden_states
                           lowercase__      :		Union[str, Any]          =     2
                    self.assertEqual(out_len + added_hidden_states		,      len(a							)							)

                    lowercase__      :		Tuple          =     outputs.attentions

                    self.assertEqual(len(a							)		,      a							)

                    self.assertListEqual(
                        list(self_attentions[0].shape[-3:]							)		,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,      )


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a							)	-> int:
             lowercase__      :		Tuple          =     model_class(a							)
             model.to(a							)
             model.eval()

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

             lowercase__      :		Tuple          =     outputs.hidden_states

             lowercase__      :		Optional[Any]          =     getattr(
                 self.model_tester		,      'expected_num_hidden_layers'		,      len(self.model_tester.depths							) + 1							)
             self.assertEqual(len(a							)		,      a							)

             # DonutSwin has a different seq_length
             lowercase__      :		int          =     (
                 config.patch_size
                 if isinstance(config.patch_size		,      collections.abc.Iterable							)
                 else (config.patch_size, config.patch_size)
             )

             lowercase__      :		Union[str, 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]		,      )

             lowercase__      :		Tuple          =     outputs.reshaped_hidden_states
             self.assertEqual(len(a							)		,      a							)

             lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__      :		Any          =     reshaped_hidden_states[0].shape
             lowercase__      :		int          =     (
                 reshaped_hidden_states[0].view(a		,      a		,      height * width							).permute(0		,      2		,      1							)
             )
             self.assertListEqual(
                 list(reshaped_hidden_states.shape[-2:]							)		,      [num_patches, self.model_tester.embed_dim]		,      )


      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__   ,				lowercase__      :		List[Any]          =     self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__      :		Tuple          =     (
                 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:
                    lowercase__      :		Optional[Any]          =     True
                    self.check_hidden_states_output(a		,      a		,      a		,      a							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__      :		Tuple          =     True

                    self.check_hidden_states_output(a		,      a		,      a		,      a							)


      def 		_UpperCAmelCase	(       self							)	-> str:
             lowercase__   ,				lowercase__      :		Tuple          =     self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__      :		Optional[Any]          =     3

             lowercase__      :		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)
             )
             lowercase__      :		Any          =     (
                 config.patch_size
                 if isinstance(config.patch_size		,      collections.abc.Iterable							)
                 else (config.patch_size, config.patch_size)
             )

             lowercase__      :		Any          =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
             lowercase__      :		Tuple          =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

             for model_class in self.all_model_classes:
                    lowercase__      :		List[Any]          =     True
                    self.check_hidden_states_output(a		,      a		,      a		,      (padded_height, padded_width)							)

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


      @slow
      def 		_UpperCAmelCase	(       self							)	-> str:
             for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__      :		Optional[Any]          =     DonutSwinModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             lowercase__   ,				lowercase__      :		List[str]          =     self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__      :		int          =     _config_zero_init(a							)
             for model_class in self.all_model_classes:
                    lowercase__      :		Union[str, Any]          =     model_class(config=a							)
                    for name, param in model.named_parameters():
                           if "embeddings" not in name and param.requires_grad:
                                  self.assertIn(
                                      ((param.data.mean() * 1e9).round() / 1e9).item()		,      [0.0, 1.0]		,      msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,      )


"""simple docstring"""





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase       :						Optional[int]     =   logging.get_logger(__name__)

_UpperCamelCase       :						Any     =   {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[Any]			  =  "oneformer"
      lowerCamelCase__		:   Union[str, Any]			  =  {"hidden_size": "hidden_dim"}


      def __init__(       self		,      a = None		,      a = 2_5_5		,      a = 1_5_0		,      a = 0.1		,      a = 2.0		,      a = 5.0		,      a = 5.0		,      a = 0.5		,      a = 0.07		,      a = 1_2_5_4_4		,      a = 3.0		,      a = 0.75		,      a = 0.02		,      a = 1.0		,      a = 1e-05		,      a = False		,      a = True		,      a = True		,      a = [4, 8, 1_6, 3_2]		,      a = 7_7		,      a = 2_5_6		,      a = 7_7		,      a = 6		,      a = 4_9_4_0_8		,      a = 2		,      a = 1_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 1_0_2_4		,      a = "GN"		,      a = 6		,      a = 1_0		,      a = True		,      a = 8		,      a = 0.1		,      a = 2_0_4_8		,      a = False		,      a = False		,      a = 2		,      a = 4		,      **a		,      )	-> List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'							)
                    lowercase__      :		List[str]          =     CONFIG_MAPPING['swin'](
                        image_size=2_2_4		,      in_channels=3		,      patch_size=4		,      embed_dim=9_6		,      depths=[2, 2, 6, 2]		,      num_heads=[3, 6, 1_2, 2_4]		,      window_size=7		,      drop_path_rate=0.3		,      use_absolute_embeddings=a		,      out_features=['stage1', 'stage2', 'stage3', 'stage4']		,      )
             elif isinstance(a		,      a							):
                    lowercase__      :		Union[str, Any]          =     backbone_config.get('model_type'							)
                    lowercase__      :		Optional[int]          =     CONFIG_MAPPING[backbone_model_type]
                    lowercase__      :		Optional[Any]          =     config_class.from_dict(a							)

             lowercase__      :		Tuple          =     backbone_config

             lowercase__      :		Optional[Any]          =     ignore_value
             lowercase__      :		int          =     num_queries
             lowercase__      :		str          =     no_object_weight
             lowercase__      :		str          =     class_weight
             lowercase__      :		Dict          =     mask_weight
             lowercase__      :		str          =     dice_weight
             lowercase__      :		Union[str, Any]          =     contrastive_weight
             lowercase__      :		Tuple          =     contrastive_temperature
             lowercase__      :		List[str]          =     train_num_points
             lowercase__      :		Tuple          =     oversample_ratio
             lowercase__      :		Tuple          =     importance_sample_ratio
             lowercase__      :		Optional[int]          =     init_std
             lowercase__      :		Optional[int]          =     init_xavier_std
             lowercase__      :		List[str]          =     layer_norm_eps
             lowercase__      :		str          =     is_training
             lowercase__      :		Dict          =     use_auxiliary_loss
             lowercase__      :		Any          =     output_auxiliary_logits
             lowercase__      :		str          =     strides
             lowercase__      :		Any          =     task_seq_len
             lowercase__      :		List[Any]          =     text_encoder_width
             lowercase__      :		Dict          =     text_encoder_context_length
             lowercase__      :		Any          =     text_encoder_num_layers
             lowercase__      :		Any          =     text_encoder_vocab_size
             lowercase__      :		str          =     text_encoder_proj_layers
             lowercase__      :		str          =     text_encoder_n_ctx
             lowercase__      :		List[Any]          =     conv_dim
             lowercase__      :		Optional[Any]          =     mask_dim
             lowercase__      :		List[Any]          =     hidden_dim
             lowercase__      :		int          =     encoder_feedforward_dim
             lowercase__      :		List[str]          =     norm
             lowercase__      :		Union[str, Any]          =     encoder_layers
             lowercase__      :		List[Any]          =     decoder_layers
             lowercase__      :		int          =     use_task_norm
             lowercase__      :		Union[str, Any]          =     num_attention_heads
             lowercase__      :		Union[str, Any]          =     dropout
             lowercase__      :		Any          =     dim_feedforward
             lowercase__      :		Optional[int]          =     pre_norm
             lowercase__      :		Tuple          =     enforce_input_proj
             lowercase__      :		List[Any]          =     query_dec_layers
             lowercase__      :		Optional[int]          =     common_stride
             lowercase__      :		str          =     decoder_layers

             super().__init__(**a							)


      def 		_UpperCAmelCase	(       self							)	-> Dict[str, any]:
             lowercase__      :		str          =     copy.deepcopy(self.__dict__							)
             lowercase__      :		Union[str, Any]          =     self.backbone_config.to_dict()
             lowercase__      :		str          =     self.__class__.model_type
             return output

