
'''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 			A							:



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=13       ,     lowerCamelCase__=32       ,     lowerCamelCase__=2       ,     lowerCamelCase__=3       ,     lowerCamelCase__=16       ,     lowerCamelCase__=[1, 2, 1]       ,     lowerCamelCase__=[2, 2, 4]       ,     lowerCamelCase__=2       ,     lowerCamelCase__=2.0       ,     lowerCamelCase__=True       ,     lowerCamelCase__=0.0       ,     lowerCamelCase__=0.0       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__="gelu"       ,     lowerCamelCase__=False       ,     lowerCamelCase__=True       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=1e-5       ,     lowerCamelCase__=True       ,     lowerCamelCase__=None       ,     lowerCamelCase__=True       ,     lowerCamelCase__=10       ,     lowerCamelCase__=8       ,     )			->		Tuple:




									'''simple docstring'''



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



			def A__ (					self       )			->		int:




									'''simple docstring'''



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

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

									lowercase__							 =       self.get_config()

									return config, pixel_values, labels



			def A__ (					self       )			->		List[str]:




									'''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 A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		str:




									'''simple docstring'''



									lowercase__							 =       DonutSwinModel(config=lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()
									lowercase__							 =       model(lowerCamelCase__       )

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




									'''simple docstring'''



									lowercase__							 =       self.prepare_config_and_inputs()
									(
									    lowercase__       
									)							 =       config_and_inputs
									lowercase__							 =       {'''pixel_values''': pixel_values}
									return config, inputs_dict





@require_torch
class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
			lowerCamelCase							:							Optional[int]       		=  (DonutSwinModel,) if is_torch_available() else ()
			lowerCamelCase							:							List[Any]       		=  {"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
			lowerCamelCase							:							int       		=  True

			lowerCamelCase							:							Optional[int]       		=  False
			lowerCamelCase							:							str       		=  False
			lowerCamelCase							:							Optional[int]       		=  False



			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__							 =       DonutSwinModelTester(self       )
									lowercase__							 =       ConfigTester(self       ,     config_class=lowerCamelCase__       ,     embed_dim=37       )



			def A__ (					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 A__ (					self       )			->		str:




									'''simple docstring'''



									return



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__       )



			def A__ (					self       )			->		Tuple:




									'''simple docstring'''



									pass



			def A__ (					self       )			->		Optional[Any]:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()

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



			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()

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

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



			def A__ (					self       )			->		str:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
									lowercase__							 =       True

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

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

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

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

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

															lowercase__							 =       outputs.attentions

															self.assertEqual(len(lowerCamelCase__       )       ,     lowerCamelCase__       )

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



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Dict:




									'''simple docstring'''



									lowercase__							 =       model_class(lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															lowercase__							 =       model(**self._prepare_for_class(lowerCamelCase__       ,     lowerCamelCase__       )       )

									lowercase__							 =       outputs.hidden_states

									lowercase__							 =       getattr(
									    self.model_tester       ,     """expected_num_hidden_layers"""       ,     len(self.model_tester.depths       ) + 1       )
									self.assertEqual(len(lowerCamelCase__       )       ,     lowerCamelCase__       )

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

									lowercase__							 =       (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__							 =       outputs.reshaped_hidden_states
									self.assertEqual(len(lowerCamelCase__       )       ,     lowerCamelCase__       )

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



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()

									lowercase__							 =       (
									    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__							 =       True
															self.check_hidden_states_output(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )

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

															self.check_hidden_states_output(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
									lowercase__							 =       3

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

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

									for model_class in self.all_model_classes:
															lowercase__							 =       True
															self.check_hidden_states_output(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     (padded_height, padded_width)       )

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



			@slow
			def A__ (					self       )			->		int:




									'''simple docstring'''



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



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()

									lowercase__							 =       _config_zero_init(lowerCamelCase__       )
									for model_class in self.all_model_classes:
															lowercase__							 =       model_class(config=lowerCamelCase__       )
															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 bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       copy.deepcopy(lowercase__	)
						lowercase__							 =       sorted(lowercase__	)
						lowercase__							 =       [bisect.bisect_right(lowercase__		,		lowercase__	) for y in x]
						return quantized





def  _A		(							lowercase__		,		lowercase__=0	):
						lowercase__							 =       np.arange(start=3		,		stop=lowercase__		,		step=4	).tolist() if k > 0 else [10]
						lowercase__							 =       _quantize(lowercase__		,		lowercase__	)
						# count number of elements per group
						lowercase__							 =       np.unique(lowercase__		,		return_counts=lowercase__	)[1]
						lowercase__							 =       [0] + bins + [np.inf]
						logger.info("""Using {} as bins for aspect lengths quantization""".format(lowercase__	)	)
						logger.info("""Count of instances per bin: {}""".format(lowercase__	)	)
						return groups







class 			A							(       __UpperCAmelCase				):



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:




									'''simple docstring'''



									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
															raise ValueError(
															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
									lowercase__							 =       sampler
									lowercase__							 =       group_ids
									lowercase__							 =       batch_size



			def __iter__(					self       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       defaultdict(lowerCamelCase__       )
									lowercase__							 =       defaultdict(lowerCamelCase__       )

									lowercase__							 =       0
									for idx in self.sampler:
															lowercase__							 =       self.group_ids[idx]
															buffer_per_group[group_id].append(lowerCamelCase__       )
															samples_per_group[group_id].append(lowerCamelCase__       )
															if len(buffer_per_group[group_id]       ) == self.batch_size:
																					yield buffer_per_group[group_id]  # TODO
																					num_batches += 1
																					del buffer_per_group[group_id]
															assert len(buffer_per_group[group_id]       ) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									lowercase__							 =       len(self       )
									lowercase__							 =       expected_num_batches - num_batches
									if num_remaining > 0:
															# for the remaining batches, group the batches by similar lengths
															lowercase__							 =       []
															for group_id, idxs in sorted(buffer_per_group.items()       ,     key=lambda lowerCamelCase__       : x[0]       ):
																					batch_idx.extend(lowerCamelCase__       )
																					if len(lowerCamelCase__       ) >= self.batch_size:
																											yield batch_idx[: self.batch_size]
																											lowercase__							 =       batch_idx[self.batch_size :]
																											num_remaining -= 1
															if len(lowerCamelCase__       ) > 0:
																					yield batch_idx
																					num_remaining -= 1
									assert num_remaining == 0



			def __len__(					self       )			->		Any:




									'''simple docstring'''



									return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size






