


'''simple docstring'''

import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       LayoutLMvaForQuestionAnswering,
       LayoutLMvaForSequenceClassification,
       LayoutLMvaForTokenClassification,
       LayoutLMvaModel,
   )
   from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor





class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=2     ,							__UpperCamelCase=3     ,							__UpperCamelCase=4     ,							__UpperCamelCase=2     ,							__UpperCamelCase=7     ,							__UpperCamelCase=True     ,							__UpperCamelCase=True     ,							__UpperCamelCase=True     ,							__UpperCamelCase=True     ,							__UpperCamelCase=99     ,							__UpperCamelCase=36     ,							__UpperCamelCase=3     ,							__UpperCamelCase=4     ,							__UpperCamelCase=37     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=512     ,							__UpperCamelCase=16     ,							__UpperCamelCase=2     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=6     ,							__UpperCamelCase=6     ,							__UpperCamelCase=3     ,							__UpperCamelCase=4     ,							__UpperCamelCase=None     ,							__UpperCamelCase=1_000     ,							)       ->    Tuple:
          _a						=		parent
          _a						=		batch_size
          _a						=		num_channels
          _a						=		image_size
          _a						=		patch_size
          _a						=		text_seq_length
          _a						=		is_training
          _a						=		use_input_mask
          _a						=		use_token_type_ids
          _a						=		use_labels
          _a						=		vocab_size
          _a						=		hidden_size
          _a						=		num_hidden_layers
          _a						=		num_attention_heads
          _a						=		intermediate_size
          _a						=		hidden_act
          _a						=		hidden_dropout_prob
          _a						=		attention_probs_dropout_prob
          _a						=		max_position_embeddings
          _a						=		type_vocab_size
          _a						=		type_sequence_label_size
          _a						=		initializer_range
          _a						=		coordinate_size
          _a						=		shape_size
          _a						=		num_labels
          _a						=		num_choices
          _a						=		scope
          _a						=		range_bbox

          # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
          _a						=		text_seq_length
          _a						=		(image_size // patch_size) ** 2 + 1
          _a						=		self.text_seq_length + self.image_seq_length






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		ids_tensor([self.batch_size, self.text_seq_length]     ,							self.vocab_size		)

          _a						=		ids_tensor([self.batch_size, self.text_seq_length, 4]     ,							self.range_bbox		)
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]		):
               for j in range(bbox.shape[1]		):
                    if bbox[i, j, 3] < bbox[i, j, 1]:
                         _a						=		bbox[i, j, 3]
                         _a						=		bbox[i, j, 1]
                         _a						=		t
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                         _a						=		bbox[i, j, 2]
                         _a						=		bbox[i, j, 0]
                         _a						=		t

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

          _a						=		None
          if self.use_input_mask:
               _a						=		random_attention_mask([self.batch_size, self.text_seq_length]		)

          _a						=		None
          if self.use_token_type_ids:
               _a						=		ids_tensor([self.batch_size, self.text_seq_length]     ,							self.type_vocab_size		)

          _a						=		None
          _a						=		None
          if self.use_labels:
               _a						=		ids_tensor([self.batch_size]     ,							self.type_sequence_label_size		)
               _a						=		ids_tensor([self.batch_size, self.text_seq_length]     ,							self.num_labels		)

          _a						=		LayoutLMvaConfig(
              vocab_size=self.vocab_size     ,							hidden_size=self.hidden_size     ,							num_hidden_layers=self.num_hidden_layers     ,							num_attention_heads=self.num_attention_heads     ,							intermediate_size=self.intermediate_size     ,							hidden_act=self.hidden_act     ,							hidden_dropout_prob=self.hidden_dropout_prob     ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,							max_position_embeddings=self.max_position_embeddings     ,							type_vocab_size=self.type_vocab_size     ,							initializer_range=self.initializer_range     ,							coordinate_size=self.coordinate_size     ,							shape_size=self.shape_size     ,							input_size=self.image_size     ,							patch_size=self.patch_size     ,							)

          return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Optional[int]:
          _a						=		LayoutLMvaModel(config=__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()

          # text + image
          _a						=		model(__UpperCamelCase     ,							pixel_values=__UpperCamelCase		)
          _a						=		model(
              __UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase     ,							attention_mask=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase		)
          _a						=		model(__UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase		)
          _a						=		model(__UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase		)

          self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)		)

          # text only
          _a						=		model(__UpperCamelCase		)
          self.parent.assertEqual(
              result.last_hidden_state.shape     ,							(self.batch_size, self.text_seq_length, self.hidden_size)		)

          # image only
          _a						=		model(pixel_values=__UpperCamelCase		)
          self.parent.assertEqual(
              result.last_hidden_state.shape     ,							(self.batch_size, self.image_seq_length, self.hidden_size)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    List[Any]:
          _a						=		self.num_labels
          _a						=		LayoutLMvaForSequenceClassification(__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()
          _a						=		model(
              __UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase     ,							attention_mask=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase     ,							labels=__UpperCamelCase     ,							)
          self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          _a						=		self.num_labels
          _a						=		LayoutLMvaForTokenClassification(config=__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()
          _a						=		model(
              __UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase     ,							attention_mask=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase     ,							labels=__UpperCamelCase     ,							)
          self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.text_seq_length, self.num_labels)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Union[str, Any]:
          _a						=		LayoutLMvaForQuestionAnswering(config=__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()
          _a						=		model(
              __UpperCamelCase     ,							bbox=__UpperCamelCase     ,							pixel_values=__UpperCamelCase     ,							attention_mask=__UpperCamelCase     ,							token_type_ids=__UpperCamelCase     ,							start_positions=__UpperCamelCase     ,							end_positions=__UpperCamelCase     ,							)
          self.parent.assertEqual(result.start_logits.shape     ,							(self.batch_size, self.seq_length)		)
          self.parent.assertEqual(result.end_logits.shape     ,							(self.batch_size, self.seq_length)		)






     def  a_					(			self		)       ->    Optional[Any]:
          _a						=		self.prepare_config_and_inputs()
          (
              (
              _a
          )			,			(
              _a
          )			,			(
              _a
          )			,			(
              _a
          )			,			(
              _a
          )			,			(
              _a
          )			,			(
              _a
          )			,			(
              _a
          )			,			
          )						=		config_and_inputs
          _a						=		{
              "input_ids": input_ids,
              "bbox": bbox,
              "pixel_values": pixel_values,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict







@require_torch
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			lowerCamelCase__ ,			unittest.TestCase ):
     UpperCAmelCase 					=				False
     UpperCAmelCase 					=				False
     UpperCAmelCase 					=				False

     UpperCAmelCase 					=				(
         (
             LayoutLMvaModel,
             LayoutLMvaForSequenceClassification,
             LayoutLMvaForTokenClassification,
             LayoutLMvaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase 					=				(
         {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
         if is_torch_available()
         else {}
     )






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
          # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
          # the sequence dimension of the text embedding only.
          # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
          return True






     def  a_					(			self		)       ->    int:
          _a						=		LayoutLMvaModelTester(self		)
          _a						=		ConfigTester(self     ,							config_class=__UpperCamelCase     ,							hidden_size=37		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=False		)       ->    List[str]:
          _a						=		copy.deepcopy(__UpperCamelCase		)
          if model_class in get_values(__UpperCamelCase		):
               _a						=		{
                   k: v.unsqueeze(1		).expand(-1     ,							self.model_tester.num_choices     ,							-1		).contiguous()
                   if isinstance(__UpperCamelCase     ,							torch.Tensor		) and v.ndim > 1
                   else v
                   for k, v in inputs_dict.items()
               }
          if return_labels:
               if model_class in get_values(__UpperCamelCase		):
                    _a						=		torch.ones(self.model_tester.batch_size     ,							dtype=torch.long     ,							device=__UpperCamelCase		)
               elif model_class in get_values(__UpperCamelCase		):
                    _a						=		torch.zeros(
                        self.model_tester.batch_size     ,							dtype=torch.long     ,							device=__UpperCamelCase		)
                    _a						=		torch.zeros(
                        self.model_tester.batch_size     ,							dtype=torch.long     ,							device=__UpperCamelCase		)
               elif model_class in [
                   *get_values(__UpperCamelCase		),
               ]:
                    _a						=		torch.zeros(
                        self.model_tester.batch_size     ,							dtype=torch.long     ,							device=__UpperCamelCase		)
               elif model_class in [
                   *get_values(__UpperCamelCase		),
               ]:
                    _a						=		torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,							dtype=torch.long     ,							device=__UpperCamelCase     ,							)

          return inputs_dict






     def  a_					(			self		)       ->    Union[str, Any]:
          self.config_tester.run_common_tests()






     def  a_					(			self		)       ->    Any:
          _a						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCamelCase		)






     def  a_					(			self		)       ->    str:
          _a						=		self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               _a						=		type
               self.model_tester.create_and_check_model(*__UpperCamelCase		)






     def  a_					(			self		)       ->    List[str]:
          _a						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase		)






     def  a_					(			self		)       ->    Optional[Any]:
          _a						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase		)






     @slow
     def  a_					(			self		)       ->    Dict:
          for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a						=		LayoutLMvaModel.from_pretrained(__UpperCamelCase		)
               self.assertIsNotNone(__UpperCamelCase		)







def __UpperCamelCase   (			)      ->     int:







     '''simple docstring'''






     _a						=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
     return image






@require_torch
class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     @cached_property
     def  a_					(			self		)       ->    Tuple:
          return LayoutLMvaImageProcessor(apply_ocr=__UpperCamelCase		) if is_vision_available() else None






     @slow
     def  a_					(			self		)       ->    Any:
          _a						=		LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		).to(__UpperCamelCase		)

          _a						=		self.default_image_processor
          _a						=		prepare_img()
          _a						=		image_processor(images=__UpperCamelCase     ,							return_tensors="pt"		).pixel_values.to(__UpperCamelCase		)

          _a						=		torch.tensor([[1, 2]]		)
          _a						=		torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]		).unsqueeze(0		)

          # forward pass
          _a						=		model(
              input_ids=input_ids.to(__UpperCamelCase		)     ,							bbox=bbox.to(__UpperCamelCase		)     ,							pixel_values=pixel_values.to(__UpperCamelCase		)     ,							)

          # verify the logits
          _a						=		torch.Size((1, 199, 768)		)
          self.assertEqual(outputs.last_hidden_state.shape     ,							__UpperCamelCase		)

          _a						=		torch.tensor(
              [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]		).to(__UpperCamelCase		)

          self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,							__UpperCamelCase     ,							atol=1e-4		)		)





'''simple docstring'''

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def __UpperCamelCase   (			__lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Dict=1024				)      ->     str:







     '''simple docstring'''






     _a			,			_a						=		[], []

     _a						=		list(zip(__lowerCamelCase      ,  __lowerCamelCase				)				)
     _a			,			_a						=		sorted_examples[0]

     def is_too_big(__lowerCamelCase		: Union[str, Any]				):
          return tok(__lowerCamelCase      ,  return_tensors="pt"				).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]				):
          _a						=		new_src + " " + src
          _a						=		new_tgt + " " + tgt
          if is_too_big(__lowerCamelCase				) or is_too_big(__lowerCamelCase				):  # cant fit, finalize example
               finished_src.append(__lowerCamelCase				)
               finished_tgt.append(__lowerCamelCase				)
               _a			,			_a						=		src, tgt
          else:  # can fit, keep adding
               _a			,			_a						=		cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(__lowerCamelCase				)
          finished_tgt.append(__lowerCamelCase				)
     return finished_src, finished_tgt






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Path      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Optional[int]				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		Path(__lowerCamelCase				)
     save_path.mkdir(exist_ok=__lowerCamelCase				)
     for split in ["train"]:
          _a			,			_a						=		data_dir / F"{split}.source", data_dir / F"{split}.target"
          _a						=		[x.rstrip() for x in Path(__lowerCamelCase				).open().readlines()]
          _a						=		[x.rstrip() for x in Path(__lowerCamelCase				).open().readlines()]
          _a			,			_a						=		pack_examples(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)
          print(F"packed {split} split from {len(__lowerCamelCase				)} examples -> {len(__lowerCamelCase				)}."				)
          Path(save_path / F"{split}.source"				).open("w"				).write("\n".join(__lowerCamelCase				)				)
          Path(save_path / F"{split}.target"				).open("w"				).write("\n".join(__lowerCamelCase				)				)
     for split in ["val", "test"]:
          _a			,			_a						=		data_dir / F"{split}.source", data_dir / F"{split}.target"
          shutil.copyfile(__lowerCamelCase      ,  save_path / F"{split}.source"				)
          shutil.copyfile(__lowerCamelCase      ,  save_path / F"{split}.target"				)






def __UpperCamelCase   (			)      ->     Any:







     '''simple docstring'''






     _a						=		argparse.ArgumentParser()
     parser.add_argument("--tok_name"      ,  type=__lowerCamelCase      ,  help="like facebook/bart-large-cnn,t5-base, etc."				)
     parser.add_argument("--max_seq_len"      ,  type=__lowerCamelCase      ,  default=128				)
     parser.add_argument("--data_dir"      ,  type=__lowerCamelCase				)
     parser.add_argument("--save_path"      ,  type=__lowerCamelCase				)
     _a						=		parser.parse_args()
     _a						=		AutoTokenizer.from_pretrained(args.tok_name				)
     return pack_data_dir(__lowerCamelCase      ,  Path(args.data_dir				)      ,  args.max_seq_len      ,  args.save_path				)


if __name__ == "__main__":
   packer_cli()

