





import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil






@pytest.fixture
def 				_lowerCAmelCase			(	_a :       Union[str, Any]      )						->      List[Any]:
     lowerCAmelCase_       :							int			 =		tmp_path / """text.txt"""
     lowerCAmelCase_       :							Tuple			 =		textwrap.dedent(
         """\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        """      )
     with open(_a       , """w"""       , encoding="""utf-8"""      ) as f:
          f.write(_a      )
     return str(_a      )






@pytest.fixture
def 				_lowerCAmelCase			(	_a :       Optional[int]       , _a :       Dict      )						->      Optional[Any]:
     lowerCAmelCase_       :							str			 =		tmp_path / """text_with_image.txt"""
     with open(_a       , """w"""       , encoding="""utf-8"""      ) as f:
          f.write(_a      )
     return str(_a      )






@pytest.mark.parametrize("""keep_linebreaks"""       , [True, False]      )
def 				_lowerCAmelCase			(	_a :       str       , _a :       Dict      )						->      List[str]:
     with open(_a       , encoding="""utf-8"""      ) as f:
          lowerCAmelCase_       :							Optional[Any]			 =		f.read().splitlines(keepends=_a      )
     lowerCAmelCase_       :							str			 =		Text(keep_linebreaks=_a       , encoding="""utf-8"""      )
     lowerCAmelCase_       :							Any			 =		text._generate_tables([[text_file]]      )
     lowerCAmelCase_       :							Optional[int]			 =		pa.concat_tables([table for _, table in generator]      ).to_pydict()["""text"""]
     assert generated_content == expected_content






@require_pil
def 				_lowerCAmelCase			(	_a :       Dict      )						->      str:
     with open(_a       , encoding="""utf-8"""      ) as f:
          lowerCAmelCase_       :							Dict			 =		f.read().splitlines()[0]
     lowerCAmelCase_       :							str			 =		Text(encoding="""utf-8"""       , features=Features({"""image""": Image()}      )      )
     lowerCAmelCase_       :							Tuple			 =		text._generate_tables([[text_file_with_image]]      )
     lowerCAmelCase_       :							List[Any]			 =		pa.concat_tables([table for _, table in generator]      )
     assert pa_table.schema.field("""image"""      ).type == Image()()
     lowerCAmelCase_       :							List[Any]			 =		pa_table.to_pydict()["""image"""]
     assert generated_content == [{"path": image_file, "bytes": None}]







@pytest.mark.parametrize("""sample_by"""       , ["""line""", """paragraph""", """document"""]      )
def 				_lowerCAmelCase			(	_a :       Tuple       , _a :       Tuple      )						->      Optional[Any]:
     with open(_a       , encoding="""utf-8"""      ) as f:
          lowerCAmelCase_       :							Union[str, Any]			 =		f.read()
     if sample_by == "line":
          lowerCAmelCase_       :							Optional[Any]			 =		expected_content.splitlines()
     elif sample_by == "paragraph":
          lowerCAmelCase_       :							List[Any]			 =		expected_content.split("""\n\n"""      )
     elif sample_by == "document":
          lowerCAmelCase_       :							int			 =		[expected_content]
     lowerCAmelCase_       :							Dict			 =		Text(sample_by=_a       , encoding="""utf-8"""       , chunksize=1_00      )
     lowerCAmelCase_       :							Union[str, Any]			 =		text._generate_tables([[text_file]]      )
     lowerCAmelCase_       :							int			 =		pa.concat_tables([table for _, table in generator]      ).to_pydict()["""text"""]
     assert generated_content == expected_content







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   lowercase__     :







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







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

       lowerCAmelCase_       :							str			 =		None
       if self.use_labels:
            lowerCAmelCase_       :							Dict			 =		ids_tensor([self.batch_size]     ,    self.type_sequence_label_size    )

       lowerCAmelCase_       :							List[Any]			 =		self.get_config()

       return config, pixel_values, labels







  def 						UpperCAmelCase__		(						self    ):
       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     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							Optional[Any]			 =		DonutSwinModel(config=_lowercase    )
       model.to(_lowercase    )
       model.eval()
       lowerCAmelCase_       :							Optional[Any]			 =		model(_lowercase    )

       lowerCAmelCase_       :							Optional[int]			 =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
       lowerCAmelCase_       :							Tuple			 =		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    ):
       lowerCAmelCase_       :							str			 =		self.prepare_config_and_inputs()
       (
           (
           lowerCAmelCase_
       ) ,	(
           lowerCAmelCase_
       ) ,	(
           lowerCAmelCase_
       ) ,	
       )       :							List[str]			 =		config_and_inputs
       lowerCAmelCase_       :							int			 =		{"""pixel_values""": pixel_values}
       return config, inputs_dict





@require_torch
class   lowercase__     (				__A  ,       __A  ,       unittest.TestCase	):
  __UpperCamelCase						  =	(DonutSwinModel,) if is_torch_available() else ()
  __UpperCamelCase						  =	{"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
  __UpperCamelCase						  =	True

  __UpperCamelCase						  =	False
  __UpperCamelCase						  =	False
  __UpperCamelCase						  =	False







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Tuple			 =		DonutSwinModelTester(self    )
       lowerCAmelCase_       :							Optional[Any]			 =		ConfigTester(self     ,    config_class=_lowercase     ,    embed_dim=37    )







  def 						UpperCAmelCase__		(						self    ):
       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    ):
       return







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Tuple			 =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       # DonutSwin does not use inputs_embeds
       pass







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							str			 =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            lowerCAmelCase_       :							Optional[Any]			 =		model_class(_lowercase    )
            self.assertIsInstance(model.get_input_embeddings()     ,    (nn.Module)    )
            lowerCAmelCase_       :							int			 =		model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(_lowercase     ,    nn.Linear    )    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							List[Any]			 =		self.model_tester.prepare_config_and_inputs_for_common()

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

            lowerCAmelCase_       :							Union[str, Any]			 =		["""pixel_values"""]
            self.assertListEqual(arg_names[:1]     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							Optional[Any]			 =		self.model_tester.prepare_config_and_inputs_for_common()
       lowerCAmelCase_       :							Tuple			 =		True

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

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            lowerCAmelCase_       :							int			 =		True
            lowerCAmelCase_       :							Union[str, Any]			 =		config.window_size**2
            lowerCAmelCase_       :							Tuple			 =		model_class(_lowercase    )
            model.to(_lowercase    )
            model.eval()
            with torch.no_grad():
                 lowerCAmelCase_       :							Any			 =		model(**self._prepare_for_class(_lowercase     ,    _lowercase    )    )
            lowerCAmelCase_       :							int			 =		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_       :							Any			 =		len(_lowercase    )

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

            if hasattr(self.model_tester     ,    """num_hidden_states_types"""    ):
                 lowerCAmelCase_       :							Optional[Any]			 =		self.model_tester.num_hidden_states_types
            else:
                 # also another +1 for reshaped_hidden_states
                 lowerCAmelCase_       :							Union[str, 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 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							Optional[Any]			 =		model_class(_lowercase    )
       model.to(_lowercase    )
       model.eval()

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

       lowerCAmelCase_       :							List[Any]			 =		outputs.hidden_states

       lowerCAmelCase_       :							str			 =		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_       :							int			 =		(
           config.patch_size
           if isinstance(config.patch_size     ,    collections.abc.Iterable    )
           else (config.patch_size, config.patch_size)
       )

       lowerCAmelCase_       :							List[str]			 =		(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[Any]			 =		outputs.reshaped_hidden_states
       self.assertEqual(len(_lowercase    )     ,    _lowercase    )

       lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_       :							Optional[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 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							Dict			 =		self.model_tester.prepare_config_and_inputs_for_common()

       lowerCAmelCase_       :							int			 =		(
           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_       :							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_       :							Any			 =		True

            self.check_hidden_states_output(_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							int			 =		self.model_tester.prepare_config_and_inputs_for_common()
       lowerCAmelCase_       :							int			 =		3

       lowerCAmelCase_       :							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_       :							Optional[int]			 =		(
           config.patch_size
           if isinstance(config.patch_size     ,    collections.abc.Iterable    )
           else (config.patch_size, config.patch_size)
       )

       lowerCAmelCase_       :							int			 =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       lowerCAmelCase_       :							int			 =		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_       :							Union[str, Any]			 =		True
            self.check_hidden_states_output(_lowercase     ,    _lowercase     ,    _lowercase     ,    (padded_height, padded_width)    )







  @slow
  def 						UpperCAmelCase__		(						self    ):
       for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowerCAmelCase_       :							str			 =		DonutSwinModel.from_pretrained(_lowercase    )
            self.assertIsNotNone(_lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							List[str]			 =		self.model_tester.prepare_config_and_inputs_for_common()

       lowerCAmelCase_       :							str			 =		_config_zero_init(_lowercase    )
       for model_class in self.all_model_classes:
            lowerCAmelCase_       :							Union[str, Any]			 =		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'     ,    )
