

'''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 lowerCamelCase   :

				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,						)    ->							List[Any]:
					snake_case							=					parent
					snake_case							=					batch_size
					snake_case							=					image_size
					snake_case							=					patch_size
					snake_case							=					num_channels
					snake_case							=					embed_dim
					snake_case							=					depths
					snake_case							=					num_heads
					snake_case							=					window_size
					snake_case							=					mlp_ratio
					snake_case							=					qkv_bias
					snake_case							=					hidden_dropout_prob
					snake_case							=					attention_probs_dropout_prob
					snake_case							=					drop_path_rate
					snake_case							=					hidden_act
					snake_case							=					use_absolute_embeddings
					snake_case							=					patch_norm
					snake_case							=					layer_norm_eps
					snake_case							=					initializer_range
					snake_case							=					is_training
					snake_case							=					scope
					snake_case							=					use_labels
					snake_case							=					type_sequence_label_size
					snake_case							=					encoder_stride

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case							=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

					snake_case							=					self.get_config()

					return config, pixel_values, labels

				def 					_lowerCamelCase    (       self )    ->							Any:
					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,						lowercase_,						lowercase_,						lowercase_ )    ->							Tuple:
					snake_case							=					DonutSwinModel(config=lowercase_ )
					model.to(lowercase_ )
					model.eval()
					snake_case							=					model(lowercase_ )

					snake_case							=					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
					snake_case							=					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:
					snake_case							=					self.prepare_config_and_inputs()
					(
					    (
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			
					)							=					config_and_inputs
					snake_case							=					{'pixel_values': pixel_values}
					return config, inputs_dict







@require_torch
class lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = (DonutSwinModel,) if is_torch_available() else ()
				snake_case_							     = {'''feature-extraction''': DonutSwinModel} if is_torch_available() else {}
				snake_case_							     = True

				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					DonutSwinModelTester(self )
					snake_case							=					ConfigTester(self,						config_class=lowercase_,						embed_dim=37 )

				def 					_lowerCamelCase    (       self )    ->							Dict:
					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 )    ->							str:
					return

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					# DonutSwin does not use inputs_embeds
					pass

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

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

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

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

						snake_case							=					['pixel_values']
						self.assertListEqual(arg_names[:1],						lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()
					snake_case							=					True

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

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

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

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

						snake_case							=					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,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Dict:
					snake_case							=					model_class(lowercase_ )
					model.to(lowercase_ )
					model.eval()

					with torch.no_grad():
						snake_case							=					model(**self._prepare_for_class(lowercase_,						lowercase_ ) )

					snake_case							=					outputs.hidden_states

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

					snake_case							=					(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],						)

					snake_case							=					outputs.reshaped_hidden_states
					self.assertEqual(len(lowercase_ ),						lowercase_ )

					snake_case			,			snake_case			,			snake_case			,			snake_case							=					reshaped_hidden_states[0].shape
					snake_case							=					(
					    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 )    ->							List[str]:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

					snake_case							=					(
					    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:
						snake_case							=					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"]
						snake_case							=					True

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

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()
					snake_case							=					3

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

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

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

				@slow
				def 					_lowerCamelCase    (       self )    ->							Dict:
					for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case							=					DonutSwinModel.from_pretrained(lowercase_ )
						self.assertIsNotNone(lowercase_ )






				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

					snake_case							=					_config_zero_init(lowercase_ )
					for model_class in self.all_model_classes:
						snake_case							=					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''',						)









from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase		=						{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_phobert import PhobertTokenizer

else:
		import sys

		lowerCamelCase		=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




