


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 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A			,   __A=2			,   __A=3			,   __A=4			,   __A=2			,   __A=7			,   __A=True			,   __A=True			,   __A=True			,   __A=True			,   __A=99			,   __A=36			,   __A=3			,   __A=4			,   __A=37			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=512			,   __A=16			,   __A=2			,   __A=0.02			,   __A=6			,   __A=6			,   __A=3			,   __A=4			,   __A=None			,   __A=1000			,   ) ->					Dict:
     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 				SCREAMING_SNAKE_CASE							(		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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A						) ->					Optional[int]:
     a					=LayoutLMvaModel(config=snake_case__						)
     model.to(snake_case__						)
     model.eval()

     # text + image
     a					=model(snake_case__			,   pixel_values=snake_case__						)
     a					=model(
         snake_case__			,   bbox=snake_case__			,   pixel_values=snake_case__			,   attention_mask=snake_case__			,   token_type_ids=snake_case__						)
     a					=model(snake_case__			,   bbox=snake_case__			,   pixel_values=snake_case__			,   token_type_ids=snake_case__						)
     a					=model(snake_case__			,   bbox=snake_case__			,   pixel_values=snake_case__						)

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

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

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A						) ->					Optional[int]:
     a					=self.num_labels
     a					=LayoutLMvaForSequenceClassification(snake_case__						)
     model.to(snake_case__						)
     model.eval()
     a					=model(
         snake_case__			,   bbox=snake_case__			,   pixel_values=snake_case__			,   attention_mask=snake_case__			,   token_type_ids=snake_case__			,   labels=snake_case__			,   )
     self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.num_labels)						)


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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A						) ->					Any:
     a					=LayoutLMvaForQuestionAnswering(config=snake_case__						)
     model.to(snake_case__						)
     model.eval()
     a					=model(
         snake_case__			,   bbox=snake_case__			,   pixel_values=snake_case__			,   attention_mask=snake_case__			,   token_type_ids=snake_case__			,   start_positions=snake_case__			,   end_positions=snake_case__			,   )
     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 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=self.prepare_config_and_inputs()
     (
         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 				__A    (	A_,   A_,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A						) ->					Any:
     return True


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=LayoutLMvaModelTester(self						)
     a					=ConfigTester(self			,   config_class=snake_case__			,   hidden_size=37						)


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

     return inputs_dict


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     self.config_tester.run_common_tests()


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__						)


 def 				SCREAMING_SNAKE_CASE							(		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(*snake_case__						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*snake_case__						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*snake_case__						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*snake_case__						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:


     for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a					=LayoutLMvaModel.from_pretrained(snake_case__						)
         self.assertIsNotNone(snake_case__						)

def _A						(						):







    """simple docstring"""
    a					=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image






@require_torch
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 @cached_property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     return LayoutLMvaImageProcessor(apply_ocr=snake_case__						) if is_vision_available() else None





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''						).to(snake_case__						)

     a					=self.default_image_processor
     a					=prepare_img()
     a					=image_processor(images=snake_case__			,   return_tensors='''pt'''						).pixel_values.to(snake_case__						)

     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(snake_case__						)			,   bbox=bbox.to(snake_case__						)			,   pixel_values=pixel_values.to(snake_case__						)			,   )

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

     a					=torch.tensor(
         [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]						).to(snake_case__						)

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



"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_rembert import RemBertTokenizer
else:
       lowerCamelCase_       :       Optional[int]      						=  None

lowerCamelCase_       :       Union[str, Any]      						=  logging.get_logger(__name__)
lowerCamelCase_       :       str      						=  {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_       :       int      						=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_       :       Optional[Any]      						=  {
    """google/rembert""": 2_5_6,
}

lowerCamelCase_       :       Optional[Any]      						=  """▁"""






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  VOCAB_FILES_NAMES
 __lowerCAmelCase			 =  PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase			 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase			 =  RemBertTokenizer


 def __init__(		self			,   __A=None			,   __A=None			,   __A=True			,   __A=True			,   __A=False			,   __A="[CLS]"			,   __A="[SEP]"			,   __A="<unk>"			,   __A="[SEP]"			,   __A="<pad>"			,   __A="[CLS]"			,   __A="[MASK]"			,   **__A			,   ) ->					Dict:
     # Mask token behave like a normal word, i.e. include the space before it
     a					=AddedToken(__A			,   lstrip=__A			,   rstrip=__A						) if isinstance(__A			,   __A						) else mask_token

     super().__init__(
         __A			,   tokenizer_file=__A			,   do_lower_case=__A			,   remove_space=__A			,   keep_accents=__A			,   bos_token=__A			,   eos_token=__A			,   unk_token=__A			,   sep_token=__A			,   pad_token=__A			,   cls_token=__A			,   mask_token=__A			,   **__A			,   )

     a					=do_lower_case
     a					=remove_space
     a					=keep_accents
     a					=vocab_file
     a					=False if not self.vocab_file else True


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					List[int]:
     a					=[self.sep_token_id]
     a					=[self.cls_token_id]
     if token_ids_a is None:
         return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None			,   __A = False						) ->					List[int]:

     if already_has_special_tokens:
         if token_ids_a is not None:
             raise ValueError(
                 '''You should not supply a second sequence if the provided sequence of '''
                 '''ids is already formatted with special tokens for the model.'''						)
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
         return [1] + ([0] * len(__A						)) + [1] + ([0] * len(__A						)) + [1]
     return [1] + ([0] * len(__A						)) + [1]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					List[int]:
     a					=[self.sep_token_id]
     a					=[self.cls_token_id]

     if token_ids_a is None:
         return len(cls + token_ids_a + sep						) * [0]
     return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					Tuple[str]:
     if not os.path.isdir(__A						):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(__A						)						)
         return
     a					=os.path.join(
         __A			,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

     if os.path.abspath(self.vocab_file						) != os.path.abspath(__A						):
         copyfile(self.vocab_file			,   __A						)

     return (out_vocab_file,)