



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		,							_snake_case		,							_snake_case=13		,							_snake_case=32		,							_snake_case=2		,							_snake_case=3		,							_snake_case=16		,							_snake_case=[1, 2, 1]		,							_snake_case=[2, 2, 4]		,							_snake_case=2		,							_snake_case=2.0		,							_snake_case=True		,							_snake_case=0.0		,							_snake_case=0.0		,							_snake_case=0.1		,							_snake_case="gelu"		,							_snake_case=False		,							_snake_case=True		,							_snake_case=0.02		,							_snake_case=1e-5		,							_snake_case=True		,							_snake_case=None		,							_snake_case=True		,							_snake_case=10		,							_snake_case=8		,							):



									"""simple docstring"""





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




				def 				snake_case  (				self						):



									"""simple docstring"""





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

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

									_lowerCAmelCase         =       self.get_config()

									return config, pixel_values, labels




				def 				snake_case  (				self						):



									"""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 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       DonutSwinModel(config=_snake_case						)
									model.to(_snake_case						)
									model.eval()
									_lowerCAmelCase         =       model(_snake_case						)

									_lowerCAmelCase         =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
									_lowerCAmelCase         =       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 				snake_case  (				self						):



									"""simple docstring"""





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





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

				__lowerCamelCase					=							False
				__lowerCamelCase					=							False
				__lowerCamelCase					=							False




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       DonutSwinModelTester(self						)
									_lowerCAmelCase         =       ConfigTester(self		,							config_class=_snake_case		,							embed_dim=37						)




				def 				snake_case  (				self						):



									"""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 				snake_case  (				self						):



									"""simple docstring"""





									return




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									pass




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs_for_common()

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs_for_common()

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

														_lowerCAmelCase         =       ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]		,							_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





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

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

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

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

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

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

														_lowerCAmelCase         =       outputs.attentions

														self.assertEqual(len(_snake_case						)		,							_snake_case						)

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




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       model_class(_snake_case						)
									model.to(_snake_case						)
									model.eval()

									with torch.no_grad():
														_lowerCAmelCase         =       model(**self._prepare_for_class(_snake_case		,							_snake_case						)						)

									_lowerCAmelCase         =       outputs.hidden_states

									_lowerCAmelCase         =       getattr(
									    self.model_tester		,							"""expected_num_hidden_layers"""		,							len(self.model_tester.depths						) + 1						)
									self.assertEqual(len(_snake_case						)		,							_snake_case						)

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

									_lowerCAmelCase         =       (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         =       outputs.reshaped_hidden_states
									self.assertEqual(len(_snake_case						)		,							_snake_case						)

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCAmelCase         =       (
									    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         =       True
														self.check_hidden_states_output(_snake_case		,							_snake_case		,							_snake_case		,							_snake_case						)

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

														self.check_hidden_states_output(_snake_case		,							_snake_case		,							_snake_case		,							_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





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

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

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

									for model_class in self.all_model_classes:
														_lowerCAmelCase         =       True
														self.check_hidden_states_output(_snake_case		,							_snake_case		,							_snake_case		,							(padded_height, padded_width)						)

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




				@slow
				def 				snake_case  (				self						):



									"""simple docstring"""





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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCAmelCase         =       _config_zero_init(_snake_case						)
									for model_class in self.all_model_classes:
														_lowerCAmelCase         =       model_class(config=_snake_case						)
														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 os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


A__       =			"""block_records.npy"""


A__       =			logging.get_logger(__name__)


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					import tensorflow.compat.va as tf

					_lowerCAmelCase         =       tf.data.TFRecordDataset(snake_case      ,		buffer_size=5_12 * 10_24 * 10_24     )
					_lowerCAmelCase         =       blocks_dataset.batch(snake_case      ,		drop_remainder=snake_case     )
					_lowerCAmelCase         =       next(blocks_dataset.take(1     ).as_numpy_iterator()     )

					return np_record





class    __lowerCAmelCase					:




				def __init__(				self		,							_snake_case		,							_snake_case		,							_snake_case=2		,							_snake_case=1000		,							_snake_case=100		,							_snake_case=100000		,							):



									"""simple docstring"""





									from scann.scann_ops.py.scann_ops_pybind import builder as Builder

									_lowerCAmelCase         =       Builder(db=_snake_case		,							num_neighbors=_snake_case		,							distance_measure="""dot_product"""						)
									_lowerCAmelCase         =       builder.tree(
									    num_leaves=_snake_case		,							num_leaves_to_search=_snake_case		,							training_sample_size=_snake_case						)
									_lowerCAmelCase         =       builder.score_ah(dimensions_per_block=_snake_case						)

									_lowerCAmelCase         =       builder.build()




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.searcher.search_batched(question_projection.detach().cpu()						)
									return retrieved_block_ids.astype("""int64"""						)





class    __lowerCAmelCase					:




				def __init__(				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									super().__init__()
									_lowerCAmelCase         =       block_records
									_lowerCAmelCase         =       tokenizer




				def __call__(				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case=None		,							_snake_case="pt"						):



									"""simple docstring"""





									_lowerCAmelCase         =       np.take(self.block_records		,							indices=_snake_case		,							axis=0						)

									_lowerCAmelCase         =       self.tokenizer.decode(question_input_ids[0]		,							skip_special_tokens=_snake_case						)

									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       []
									for retrieved_block in retrieved_blocks:
														text.append(_snake_case						)
														text_pair.append(retrieved_block.decode()						)

									_lowerCAmelCase         =       self.tokenizer(
									    _snake_case		,							_snake_case		,							padding=_snake_case		,							truncation=_snake_case		,							return_special_tokens_mask=_snake_case		,							max_length=_snake_case						)
									_lowerCAmelCase         =       concat_inputs.convert_to_tensors(_snake_case						)

									if answer_ids is not None:
														return self.block_has_answer(_snake_case		,							_snake_case						) + (concat_inputs_tensors,)
									else:
														return (None, None, None, concat_inputs_tensors)




				@classmethod
				def 				snake_case  (				cls		,							_snake_case		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





									if os.path.isdir(_snake_case						):
														_lowerCAmelCase         =       os.path.join(_snake_case		,							_REALM_BLOCK_RECORDS_FILENAME						)
									else:
														_lowerCAmelCase         =       hf_hub_download(
														    repo_id=_snake_case		,							filename=_REALM_BLOCK_RECORDS_FILENAME		,							**_snake_case						)
									_lowerCAmelCase         =       np.load(_snake_case		,							allow_pickle=_snake_case						)

									_lowerCAmelCase         =       AutoTokenizer.from_pretrained(_snake_case		,							*_snake_case		,							**_snake_case						)

									return cls(_snake_case		,							_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									np.save(os.path.join(_snake_case		,							_REALM_BLOCK_RECORDS_FILENAME						)		,							self.block_records						)
									# save tokenizer
									self.tokenizer.save_pretrained(_snake_case						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       0

									for input_id in concat_inputs.input_ids:
														_lowerCAmelCase         =       input_id.tolist()
														# Check answers between two [SEP] tokens
														_lowerCAmelCase         =       input_id_list.index(self.tokenizer.sep_token_id						)
														_lowerCAmelCase         =       first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id						)

														start_pos.append([]						)
														end_pos.append([]						)
														for answer in answer_ids:
																			for idx in range(first_sep_idx + 1		,							_snake_case						):
																								if answer[0] == input_id_list[idx]:
																													if input_id_list[idx : idx + len(_snake_case						)] == answer:
																																		start_pos[-1].append(_snake_case						)
																																		end_pos[-1].append(idx + len(_snake_case						) - 1						)

														if len(start_pos[-1]						) == 0:
																			has_answers.append(_snake_case						)
														else:
																			has_answers.append(_snake_case						)
																			if len(start_pos[-1]						) > max_answers:
																								_lowerCAmelCase         =       len(start_pos[-1]						)

        # Pad -1 to max_answers
									for start_pos_, end_pos_ in zip(_snake_case		,							_snake_case						):
														if len(start_pos_						) < max_answers:
																			_lowerCAmelCase         =       [-1] * (max_answers - len(start_pos_						))
																			start_pos_ += padded
																			end_pos_ += padded
									return has_answers, start_pos, end_pos



