


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 A						:






      def __init__(      self		:  Optional[Any]    ,     lowercase_		:  Any    ,     lowercase_		:  Dict=13    ,     lowercase_		:  List[Any]=32    ,     lowercase_		:  Dict=2    ,     lowercase_		:  Any=3    ,     lowercase_		:  List[str]=16    ,     lowercase_		:  Optional[Any]=[1, 2, 1]    ,     lowercase_		:  Optional[Any]=[2, 2, 4]    ,     lowercase_		:  List[str]=2    ,     lowercase_		:  Any=2.0    ,     lowercase_		:  Any=True    ,     lowercase_		:  List[Any]=0.0    ,     lowercase_		:  Dict=0.0    ,     lowercase_		:  List[Any]=0.1    ,     lowercase_		:  Union[str, Any]="gelu"    ,     lowercase_		:  Optional[int]=False    ,     lowercase_		:  Optional[Any]=True    ,     lowercase_		:  str=0.02    ,     lowercase_		:  Tuple=1E-5    ,     lowercase_		:  str=True    ,     lowercase_		:  Tuple=None    ,     lowercase_		:  Union[str, Any]=True    ,     lowercase_		:  Optional[Any]=10    ,     lowercase_		:  List[Any]=8    ,     )      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	int						=parent
             _lowerCamelCase :	List[str]						=batch_size
             _lowerCamelCase :	str						=image_size
             _lowerCamelCase :	List[str]						=patch_size
             _lowerCamelCase :	Tuple						=num_channels
             _lowerCamelCase :	List[str]						=embed_dim
             _lowerCamelCase :	int						=depths
             _lowerCamelCase :	Optional[Any]						=num_heads
             _lowerCamelCase :	Optional[int]						=window_size
             _lowerCamelCase :	Union[str, Any]						=mlp_ratio
             _lowerCamelCase :	Tuple						=qkv_bias
             _lowerCamelCase :	List[str]						=hidden_dropout_prob
             _lowerCamelCase :	str						=attention_probs_dropout_prob
             _lowerCamelCase :	List[Any]						=drop_path_rate
             _lowerCamelCase :	Any						=hidden_act
             _lowerCamelCase :	Optional[Any]						=use_absolute_embeddings
             _lowerCamelCase :	List[Any]						=patch_norm
             _lowerCamelCase :	List[str]						=layer_norm_eps
             _lowerCamelCase :	str						=initializer_range
             _lowerCamelCase :	Union[str, Any]						=is_training
             _lowerCamelCase :	List[Any]						=scope
             _lowerCamelCase :	str						=use_labels
             _lowerCamelCase :	Any						=type_sequence_label_size
             _lowerCamelCase :	Union[str, Any]						=encoder_stride






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

             _lowerCamelCase :	Dict						=self.get_config()

             return config, pixel_values, labels






      def  lowerCamelCase (      self		:  List[str]				)      ->		Union[str, Any]:
             """simple docstring"""




             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  lowerCamelCase (      self		:  Union[str, Any]    ,     lowercase_		:  int    ,     lowercase_		:  Optional[int]    ,     lowercase_		:  List[Any]				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	str						=DonutSwinModel(config=lowercase_				)
             model.to(lowercase_				)
             model.eval()
             _lowerCamelCase :	Union[str, Any]						=model(lowercase_				)

             _lowerCamelCase :	List[str]						=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
             _lowerCamelCase :	str						=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  lowerCamelCase (      self		:  int				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	Tuple						=self.prepare_config_and_inputs()
             (
                 (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  
             ) :	Dict						=config_and_inputs
             _lowerCamelCase :	Optional[int]						={'pixel_values': pixel_values}
             return config, inputs_dict




@require_torch
class A						(     UpperCamelCase_					,							UpperCamelCase_					,							unittest.TestCase  ):
      UpperCamelCase__							:						List[str]   =(DonutSwinModel,) if is_torch_available() else ()
      UpperCamelCase__							:						Union[str, Any]   ={'feature-extraction': DonutSwinModel} if is_torch_available() else {}
      UpperCamelCase__							:						Any   =True

      UpperCamelCase__							:						Optional[Any]   =False
      UpperCamelCase__							:						Any   =False
      UpperCamelCase__							:						Tuple   =False






      def  lowerCamelCase (      self		:  List[Any]				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=DonutSwinModelTester(self				)
             _lowerCamelCase :	Tuple						=ConfigTester(self    ,     config_class=lowercase_    ,     embed_dim=37				)






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		Optional[int]:
             """simple docstring"""




             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  lowerCamelCase (      self		:  List[str]				)      ->		int:
             """simple docstring"""




             return






      def  lowerCamelCase (      self		:  List[str]				)      ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase :	List[str]						=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase_				)






      def  lowerCamelCase (      self		:  int				)      ->		str:
             """simple docstring"""




             pass






      def  lowerCamelCase (      self		:  Any				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			,  _lowerCamelCase :	Any						=self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _lowerCamelCase :	Tuple						=model_class(lowercase_				)
                    self.assertIsInstance(model.get_input_embeddings()    ,     (nn.Module)				)
                    _lowerCamelCase :	Tuple						=model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(lowercase_    ,     nn.Linear				)				)






      def  lowerCamelCase (      self		:  List[str]				)      ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			,  _lowerCamelCase :	Union[str, Any]						=self.model_tester.prepare_config_and_inputs_for_common()

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

                    _lowerCamelCase :	Union[str, Any]						=['pixel_values']
                    self.assertListEqual(arg_names[:1]    ,     lowercase_				)






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




             _lowerCamelCase			,  _lowerCamelCase :	Any						=self.model_tester.prepare_config_and_inputs_for_common()
             _lowerCamelCase :	Optional[int]						=True

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

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

                    self.assertListEqual(
                        list(attentions[0].shape[-3:]				)    ,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,     )
                    _lowerCamelCase :	int						=len(lowercase_				)

                    # Check attention is always last and order is fine
                    _lowerCamelCase :	str						=True
                    _lowerCamelCase :	Dict						=True
                    _lowerCamelCase :	Any						=model_class(lowercase_				)
                    model.to(lowercase_				)
                    model.eval()
                    with torch.no_grad():
                           _lowerCamelCase :	int						=model(**self._prepare_for_class(lowercase_    ,     lowercase_				)				)

                    if hasattr(self.model_tester    ,     'num_hidden_states_types'				):
                           _lowerCamelCase :	Any						=self.model_tester.num_hidden_states_types
                    else:
                           # also another +1 for reshaped_hidden_states
                           _lowerCamelCase :	Optional[Any]						=2
                    self.assertEqual(out_len + added_hidden_states    ,     len(lowercase_				)				)

                    _lowerCamelCase :	Any						=outputs.attentions

                    self.assertEqual(len(lowercase_				)    ,     lowercase_				)

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






      def  lowerCamelCase (      self		:  Dict    ,     lowercase_		:  Dict    ,     lowercase_		:  List[str]    ,     lowercase_		:  int    ,     lowercase_		:  Optional[Any]				)      ->		int:
             """simple docstring"""




             _lowerCamelCase :	List[str]						=model_class(lowercase_				)
             model.to(lowercase_				)
             model.eval()

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

             _lowerCamelCase :	Any						=outputs.hidden_states

             _lowerCamelCase :	List[Any]						=getattr(
                 self.model_tester    ,     'expected_num_hidden_layers'    ,     len(self.model_tester.depths				) + 1				)
             self.assertEqual(len(lowercase_				)    ,     lowercase_				)

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

             _lowerCamelCase :	List[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]    ,     )

             _lowerCamelCase :	List[str]						=outputs.reshaped_hidden_states
             self.assertEqual(len(lowercase_				)    ,     lowercase_				)

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






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




             _lowerCamelCase			,  _lowerCamelCase :	int						=self.model_tester.prepare_config_and_inputs_for_common()

             _lowerCamelCase :	Optional[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:
                    _lowerCamelCase :	Union[str, Any]						=True
                    self.check_hidden_states_output(lowercase_    ,     lowercase_    ,     lowercase_    ,     lowercase_				)

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

                    self.check_hidden_states_output(lowercase_    ,     lowercase_    ,     lowercase_    ,     lowercase_				)






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			,  _lowerCamelCase :	List[str]						=self.model_tester.prepare_config_and_inputs_for_common()
             _lowerCamelCase :	Union[str, Any]						=3

             _lowerCamelCase :	List[str]						=(
                 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)
             )
             _lowerCamelCase :	str						=(
                 config.patch_size
                 if isinstance(config.patch_size    ,     collections.abc.Iterable				)
                 else (config.patch_size, config.patch_size)
             )

             _lowerCamelCase :	Dict						=image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
             _lowerCamelCase :	Optional[Any]						=image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

             for model_class in self.all_model_classes:
                    _lowerCamelCase :	Tuple						=True
                    self.check_hidden_states_output(lowercase_    ,     lowercase_    ,     lowercase_    ,     (padded_height, padded_width)				)

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






      @slow
      def  lowerCamelCase (      self		:  Tuple				)      ->		Optional[Any]:
             """simple docstring"""




             for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase :	Optional[Any]						=DonutSwinModel.from_pretrained(lowercase_				)
                    self.assertIsNotNone(lowercase_				)






      def  lowerCamelCase (      self		:  str				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			,  _lowerCamelCase :	List[Any]						=self.model_tester.prepare_config_and_inputs_for_common()

             _lowerCamelCase :	List[Any]						=_config_zero_init(lowercase_				)
             for model_class in self.all_model_classes:
                    _lowerCamelCase :	int						=model_class(config=lowercase_				)
                    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'''    ,     )






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Optional[Any]   ='AutoFeatureExtractor'
      UpperCamelCase__							:						List[str]   ='Speech2Text2Tokenizer'






      def __init__(      self		:  str    ,     lowercase_		:  Dict    ,     lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




             super().__init__(lowercase_    ,     lowercase_				)
             _lowerCamelCase :	Optional[Any]						=self.feature_extractor
             _lowerCamelCase :	Tuple						=False






      def __call__(      self		:  Tuple    ,     *lowercase_		:  str    ,     **lowercase_		:  List[Any]				)      ->		List[str]:
             """simple docstring"""




             if self._in_target_context_manager:
                    return self.current_processor(*lowercase_    ,     **lowercase_				)

             if "raw_speech" in kwargs:
                    warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'				)
                    _lowerCamelCase :	List[str]						=kwargs.pop('raw_speech'				)
             else:
                    _lowerCamelCase :	str						=kwargs.pop('audio'    ,     lowercase_				)
             _lowerCamelCase :	Optional[Any]						=kwargs.pop('sampling_rate'    ,     lowercase_				)
             _lowerCamelCase :	List[str]						=kwargs.pop('text'    ,     lowercase_				)
             if len(lowercase_				) > 0:
                    _lowerCamelCase :	Dict						=args[0]
                    _lowerCamelCase :	Optional[Any]						=args[1:]

             if audio is None and text is None:
                    raise ValueError('You need to specify either an `audio` or `text` input to process.'				)

             if audio is not None:
                    _lowerCamelCase :	List[Any]						=self.feature_extractor(lowercase_    ,     *lowercase_    ,     sampling_rate=lowercase_    ,     **lowercase_				)
             if text is not None:
                    _lowerCamelCase :	Tuple						=self.tokenizer(lowercase_    ,     **lowercase_				)

             if text is None:
                    return inputs
             elif audio is None:
                    return encodings
             else:
                    _lowerCamelCase :	Dict						=encodings['input_ids']
                    return inputs






      def  lowerCamelCase (      self		:  str    ,     *lowercase_		:  List[Any]    ,     **lowercase_		:  Any				)      ->		Union[str, Any]:
             """simple docstring"""




             return self.tokenizer.batch_decode(*lowercase_    ,     **lowercase_				)






      def  lowerCamelCase (      self		:  Optional[int]    ,     *lowercase_		:  Dict    ,     **lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




             return self.tokenizer.decode(*lowercase_    ,     **lowercase_				)






      @contextmanager
      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		Tuple:
             """simple docstring"""




             warnings.warn(
                 '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
                 'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
                 'your audio inputs, or in a separate call.'				)
             _lowerCamelCase :	Optional[Any]						=True
             _lowerCamelCase :	List[Any]						=self.tokenizer
             yield
             _lowerCamelCase :	Optional[int]						=self.feature_extractor
             _lowerCamelCase :	List[str]						=False


