


'''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			:						int,       a_			:						Union[str, Any],       a_			:						Dict=13,       a_			:						List[str]=32,       a_			:						int=2,       a_			:						List[str]=3,       a_			:						List[Any]=16,       a_			:						List[Any]=[1, 2, 1],       a_			:						Dict=[2, 2, 4],       a_			:						Dict=2,       a_			:						List[str]=2.0,       a_			:						List[Any]=True,       a_			:						Dict=0.0,       a_			:						Tuple=0.0,       a_			:						str=0.1,       a_			:						Any="gelu",       a_			:						Union[str, Any]=False,       a_			:						List[str]=True,       a_			:						Optional[Any]=0.02,       a_			:						Optional[int]=1e-5,       a_			:						Union[str, Any]=True,       a_			:						List[Any]=None,       a_			:						Optional[int]=True,       a_			:						Tuple=10,       a_			:						Dict=8,       ):





    """simple docstring"""



    UpperCamelCase__			    =    parent
    UpperCamelCase__			    =    batch_size
    UpperCamelCase__			    =    image_size
    UpperCamelCase__			    =    patch_size
    UpperCamelCase__			    =    num_channels
    UpperCamelCase__			    =    embed_dim
    UpperCamelCase__			    =    depths
    UpperCamelCase__			    =    num_heads
    UpperCamelCase__			    =    window_size
    UpperCamelCase__			    =    mlp_ratio
    UpperCamelCase__			    =    qkv_bias
    UpperCamelCase__			    =    hidden_dropout_prob
    UpperCamelCase__			    =    attention_probs_dropout_prob
    UpperCamelCase__			    =    drop_path_rate
    UpperCamelCase__			    =    hidden_act
    UpperCamelCase__			    =    use_absolute_embeddings
    UpperCamelCase__			    =    patch_norm
    UpperCamelCase__			    =    layer_norm_eps
    UpperCamelCase__			    =    initializer_range
    UpperCamelCase__			    =    is_training
    UpperCamelCase__			    =    scope
    UpperCamelCase__			    =    use_labels
    UpperCamelCase__			    =    type_sequence_label_size
    UpperCamelCase__			    =    encoder_stride

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



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

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

    UpperCamelCase__			    =    self.get_config()

    return config, pixel_values, labels

 def 		lowercase_    (			self			:						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 		lowercase_    (			self			:						Tuple,       a_			:						Tuple,       a_			:						Dict,       a_			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    DonutSwinModel(config=a_       )
    model.to(a_       )
    model.eval()
    UpperCamelCase__			    =    model(a_       )

    UpperCamelCase__			    =    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
    UpperCamelCase__			    =    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 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



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







@require_torch
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       List[str]            =       (DonutSwinModel,) if is_torch_available() else ()
 _lowerCamelCase			:       Tuple            =       {'feature-extraction': DonutSwinModel} if is_torch_available() else {}
 _lowerCamelCase			:       str            =       True

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

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    DonutSwinModelTester(self       )
    UpperCamelCase__			    =    ConfigTester(self,       config_class=a_,       embed_dim=37       )

 def 		lowercase_    (			self			:						Any       ):





    """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 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    return

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a_       )

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    pass

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

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

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCamelCase__			    =    ["pixel_values"]
       self.assertListEqual(arg_names[:1],       a_       )

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



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

    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    False
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )
       UpperCamelCase__			    =    outputs.attentions
       UpperCamelCase__			    =    len(self.model_tester.depths       )
       self.assertEqual(len(a_       ),       a_       )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    config.window_size**2
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )
       UpperCamelCase__			    =    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],       )
       UpperCamelCase__			    =    len(a_       )

       # Check attention is always last and order is fine
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )

       if hasattr(self.model_tester,       "num_hidden_states_types"       ):
          UpperCamelCase__			    =    self.model_tester.num_hidden_states_types
       else:
          # also another +1 for reshaped_hidden_states
          UpperCamelCase__			    =    2
       self.assertEqual(out_len + added_hidden_states,       len(a_       )       )

       UpperCamelCase__			    =    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 		lowercase_    (			self			:						Any,       a_			:						Dict,       a_			:						Union[str, Any],       a_			:						Optional[Any],       a_			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    model_class(a_       )
    model.to(a_       )
    model.eval()

    with torch.no_grad():
       UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )

    UpperCamelCase__			    =    outputs.hidden_states

    UpperCamelCase__			    =    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
    UpperCamelCase__			    =    (
        config.patch_size
        if isinstance(config.patch_size,       collections.abc.Iterable       )
        else (config.patch_size, config.patch_size)
    )

    UpperCamelCase__			    =    (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],       )

    UpperCamelCase__			    =    outputs.reshaped_hidden_states
    self.assertEqual(len(a_       ),       a_       )

    UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    reshaped_hidden_states[0].shape
    UpperCamelCase__			    =    (
        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 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

    UpperCamelCase__			    =    (
        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:
       UpperCamelCase__			    =    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"]
       UpperCamelCase__			    =    True

       self.check_hidden_states_output(a_,       a_,       a_,       a_       )

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase__			    =    3

    UpperCamelCase__			    =    (
        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)
    )
    UpperCamelCase__			    =    (
        config.patch_size
        if isinstance(config.patch_size,       collections.abc.Iterable       )
        else (config.patch_size, config.patch_size)
    )

    UpperCamelCase__			    =    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
    UpperCamelCase__			    =    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    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"]
       UpperCamelCase__			    =    True
       self.check_hidden_states_output(a_,       a_,       a_,       (padded_height, padded_width)       )

 @slow
 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCamelCase__			    =    DonutSwinModel.from_pretrained(a_       )
       self.assertIsNotNone(a_       )

 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

    UpperCamelCase__			    =    _config_zero_init(a_       )
    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    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 torch

from transformers import AutoTokenizer


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					AutoTokenizer.from_pretrained(UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):

								if isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					None
												for l in x:
																SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
																    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)
																SCREAMING_SNAKE_CASE__  				=					torch.tensor([len(UpperCAmelCase__  )]  )
																if d is not None:
																				for k in d.keys():
																								SCREAMING_SNAKE_CASE__  				=					torch.cat((d[k], t[k])							,		0  )
																else:
																				SCREAMING_SNAKE_CASE__  				=					t

												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"  )  )
												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"  )  )

								elif isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
												    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)

								else:
												raise Exception(
												    "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
												    " strings` for supports are supported."  )

								return d






				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__=0.70  ):
								SCREAMING_SNAKE_CASE__  				=					[]
								for idx in range(len(W_query["input_ids"]  )  ):
												SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					range(p_start.shape[1]  )

												SCREAMING_SNAKE_CASE__  				=					[]
												for start_id in start_indexes:
																for end_id in end_indexes:
																				if start_id < end_id:
																								output.append(
																								    (
																								        start_id,
																								        end_id,
																								        p_start[idx][start_id].item(),
																								        p_end[idx][end_id].item(),
																								    )  )

												output.sort(key=lambda UpperCAmelCase__  : (tup[2] * tup[3])							,		reverse=UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					[]
												for k in range(len(UpperCAmelCase__  )  ):
																if output[k][2] * output[k][3] >= thresh:
																				SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					output[k][0], output[k][1]
																				SCREAMING_SNAKE_CASE__  				=					self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]  )
																				temp.append((decoded, output[k][2] * output[k][3])  )

												final_outputs.append(UpperCAmelCase__  )

								return final_outputs
