"""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 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase=2  ,						lowerCamelCase=3  ,						lowerCamelCase=4  ,						lowerCamelCase=2  ,						lowerCamelCase=7  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=99  ,						lowerCamelCase=36  ,						lowerCamelCase=3  ,						lowerCamelCase=4  ,						lowerCamelCase=37  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=512  ,						lowerCamelCase=16  ,						lowerCamelCase=2  ,						lowerCamelCase=0.02  ,						lowerCamelCase=6  ,						lowerCamelCase=6  ,						lowerCamelCase=3  ,						lowerCamelCase=4  ,						lowerCamelCase=None  ,						lowerCamelCase=1000  ,						):
        __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		):
        __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  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  LayoutLMvaModel(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()

        # text + image
        __a     				=  model(lowerCamelCase  ,						pixel_values=lowerCamelCase		)
        __a     				=  model(
            lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						token_type_ids=lowerCamelCase		)
        __a     				=  model(lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=lowerCamelCase  ,						token_type_ids=lowerCamelCase		)
        __a     				=  model(lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=lowerCamelCase		)

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

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

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.num_labels
        __a     				=  LayoutLMvaForSequenceClassification(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(
            lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						token_type_ids=lowerCamelCase  ,						labels=lowerCamelCase  ,						)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.num_labels
        __a     				=  LayoutLMvaForTokenClassification(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(
            lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						token_type_ids=lowerCamelCase  ,						labels=lowerCamelCase  ,						)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.text_seq_length, self.num_labels)		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  LayoutLMvaForQuestionAnswering(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(
            lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						token_type_ids=lowerCamelCase  ,						start_positions=lowerCamelCase  ,						end_positions=lowerCamelCase  ,						)
        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		):
        __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 				snake_case__     (   snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			str           =		False
 _snake_case   :			int           =		False
 _snake_case   :			int           =		False

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        # `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		):
        __a     				=  LayoutLMvaModelTester(self		)
        __a     				=  ConfigTester(self  ,						config_class=lowerCamelCase  ,						hidden_size=37		)


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

        return inputs_dict


 def 		a__ (				self		):
        self.config_tester.run_common_tests()


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase		)


 def 		a__ (				self		):
        __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(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCamelCase		)






 @slow
 def 		a__ (				self		):
        for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a     				=  LayoutLMvaModel.from_pretrained(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)



def 					_lowerCamelCase(					):
       __a     				=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
       return image






@require_torch
class 				snake_case__     (   unittest.TestCase   ):


 @cached_property
 def 		a__ (				self		):
        return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase		) if is_vision_available() else None






 @slow
 def 		a__ (				self		):
        __a     				=  LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		).to(lowerCamelCase		)

        __a     				=  self.default_image_processor
        __a     				=  prepare_img()
        __a     				=  image_processor(images=lowerCamelCase  ,						return_tensors="pt"		).pixel_values.to(lowerCamelCase		)

        __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(lowerCamelCase		)  ,						bbox=bbox.to(lowerCamelCase		)  ,						pixel_values=pixel_values.to(lowerCamelCase		)  ,						)

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

        __a     				=  torch.tensor(
            [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]		).to(lowerCamelCase		)

        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,						lowerCamelCase  ,						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_barthez import BarthezTokenizer
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	None

SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:int			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__:Dict			=	{
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE__:List[str]			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		VOCAB_FILES_NAMES
 _snake_case   :			Optional[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			str           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Dict           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		BarthezTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        super().__init__(
            lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        __a     				=  [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __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 + sep + token_ids_a + sep		) * [0]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

        if not os.path.isdir(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

        return (out_vocab_file,)
