

'''simple docstring'''







import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


__lowerCamelCase						    =      (
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


__lowerCamelCase						    =      logging.get_logger(__name__)  # pylint: disable=invalid-name
def  UpperCAmelCase__      ( )     ->     List[Any]:
       A_		   = """https://pypi.org/pypi/diffusers/json"""
       A_		   = json.loads(request.urlopen(UpperCAmelCase__			).read()			)["""releases"""].keys()
       return sorted(UpperCAmelCase__,    key=lambda UpperCAmelCase__			: version.Version(UpperCAmelCase__			)			)
def  UpperCAmelCase__      ( )     ->     int:
       # This function has already been executed if HF_MODULES_CACHE already is in the Python path.
       if HF_MODULES_CACHE in sys.path:
              return

       sys.path.append(UpperCAmelCase__			)
       os.makedirs(UpperCAmelCase__,    exist_ok=UpperCAmelCase__			)
       A_		   = Path(UpperCAmelCase__			) / """__init__.py"""
       if not init_path.exists():
              init_path.touch()
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       init_hf_modules()
       A_		   = Path(UpperCAmelCase__			) / name
       # If the parent module does not exist yet, recursively create it.
       if not dynamic_module_path.parent.exists():
              create_dynamic_module(dynamic_module_path.parent			)
       os.makedirs(UpperCAmelCase__,    exist_ok=UpperCAmelCase__			)
       A_		   = dynamic_module_path / """__init__.py"""
       if not init_path.exists():
              init_path.touch()
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       with open(UpperCAmelCase__,    """r""",    encoding="""utf-8"""			) as f:
              A_		   = f.read()

       # Imports of the form `import .xxx`
       A_		   = re.findall("""^\s*import\s+\.(\S+)\s*$""",    UpperCAmelCase__,    flags=re.MULTILINE			)
       # Imports of the form `from .xxx import yyy`
       relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import""",    UpperCAmelCase__,    flags=re.MULTILINE			)
       # Unique-ify
       return list(set(UpperCAmelCase__			)			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Any:
       A_		   = False
       A_		   = [module_file]
       A_		   = []

       # Let's recurse through all relative imports
       while not no_change:
              A_		   = []
              for f in files_to_check:
                     new_imports.extend(get_relative_imports(UpperCAmelCase__			)			)

              A_		   = Path(UpperCAmelCase__			).parent
              A_		   = [str(module_path / m			) for m in new_imports]
              A_		   = [f for f in new_import_files if f not in all_relative_imports]
              A_		   = [F'''{f}.py''' for f in new_import_files]

              A_		   = len(UpperCAmelCase__			) == 0
              all_relative_imports.extend(UpperCAmelCase__			)

       return all_relative_imports
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[Any]:
       with open(UpperCAmelCase__,    """r""",    encoding="""utf-8"""			) as f:
              A_		   = f.read()

       # Imports of the form `import xxx`
       A_		   = re.findall("""^\s*import\s+(\S+)\s*$""",    UpperCAmelCase__,    flags=re.MULTILINE			)
       # Imports of the form `from xxx import yyy`
       imports += re.findall("""^\s*from\s+(\S+)\s+import""",    UpperCAmelCase__,    flags=re.MULTILINE			)
       # Only keep the top-level module
       A_		   = [imp.split("""."""			)[0] for imp in imports if not imp.startswith("""."""			)]

       # Unique-ify and test we got them all
       A_		   = list(set(UpperCAmelCase__			)			)
       A_		   = []
       for imp in imports:
              try:
                     importlib.import_module(UpperCAmelCase__			)
              except ImportError:
                     missing_packages.append(UpperCAmelCase__			)

       if len(UpperCAmelCase__			) > 0:
              raise ImportError(
                  """This modeling file requires the following packages that were not found in your environment: """
                  F'''{", ".join(UpperCAmelCase__			)}. Run `pip install {" ".join(UpperCAmelCase__			)}`'''			)

       return get_relative_imports(UpperCAmelCase__			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Dict:
       A_		   = module_path.replace(os.path.sep,    """."""			)
       A_		   = importlib.import_module(UpperCAmelCase__			)

       if class_name is None:
              return find_pipeline_class(UpperCAmelCase__			)
       return getattr(UpperCAmelCase__,    UpperCAmelCase__			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       from ..pipelines import DiffusionPipeline

       A_		   = dict(inspect.getmembers(UpperCAmelCase__,    inspect.isclass			)			)

       A_		   = None
       for cls_name, cls in cls_members.items():
              if (
                  cls_name != DiffusionPipeline.__name__
                  and issubclass(cls,    UpperCAmelCase__			)
                  and cls.__module__.split("""."""			)[0] != "diffusers"
              ):
                     if pipeline_class is not None:
                            raise ValueError(
                                F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
                                F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
                                F''' {loaded_module}.'''			)
                     A_		   = cls

       return pipeline_class
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__ = None,    UpperCAmelCase__ = False,    UpperCAmelCase__ = False,    UpperCAmelCase__ = None,    UpperCAmelCase__ = None,    UpperCAmelCase__ = None,    UpperCAmelCase__ = False,    )     ->     Union[str, Any]:
       A_		   = str(UpperCAmelCase__			)

       A_		   = os.path.join(UpperCAmelCase__,    UpperCAmelCase__			)

       if os.path.isfile(UpperCAmelCase__			):
              A_		   = module_file_or_url
              A_		   = """local"""
       elif pretrained_model_name_or_path.count("""/"""			) == 0:
              A_		   = get_diffusers_versions()
              # cut ".dev0"
              A_		   = """v""" + """.""".join(__version__.split("""."""			)[:3]			)

              # retrieve github version that matches
              if revision is None:
                     A_		   = latest_version if latest_version[1:] in available_versions else """main"""
                     logger.info(F'''Defaulting to latest_version: {revision}.'''			)
              elif revision in available_versions:
                     A_		   = F'''v{revision}'''
              elif revision == "main":
                     A_		   = revision
              else:
                     raise ValueError(
                         F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
                         F''' {", ".join(available_versions + ["main"]			)}.'''			)

              # community pipeline on GitHub
              A_		   = COMMUNITY_PIPELINES_URL.format(revision=UpperCAmelCase__,    pipeline=UpperCAmelCase__			)
              try:
                     A_		   = cached_download(
                         UpperCAmelCase__,    cache_dir=UpperCAmelCase__,    force_download=UpperCAmelCase__,    proxies=UpperCAmelCase__,    resume_download=UpperCAmelCase__,    local_files_only=UpperCAmelCase__,    use_auth_token=UpperCAmelCase__,    )
                     A_		   = """git"""
                     A_		   = pretrained_model_name_or_path + """.py"""
              except EnvironmentError:
                     logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''			)
                     raise
       else:
              try:
                     # Load from URL or cache if already cached
                     A_		   = hf_hub_download(
                         UpperCAmelCase__,    UpperCAmelCase__,    cache_dir=UpperCAmelCase__,    force_download=UpperCAmelCase__,    proxies=UpperCAmelCase__,    resume_download=UpperCAmelCase__,    local_files_only=UpperCAmelCase__,    use_auth_token=UpperCAmelCase__,    )
                     A_		   = os.path.join("""local""",    """--""".join(pretrained_model_name_or_path.split("""/"""			)			)			)
              except EnvironmentError:
                     logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''			)
                     raise

    # Check we have all the requirements in our environment
       A_		   = check_imports(UpperCAmelCase__			)

       # Now we move the module inside our cached dynamic modules.
       A_		   = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
       create_dynamic_module(UpperCAmelCase__			)
       A_		   = Path(UpperCAmelCase__			) / full_submodule
       if submodule == "local" or submodule == "git":
              # We always copy local files (we could hash the file to see if there was a change, and give them the name of
              # that hash, to only copy when there is a modification but it seems overkill for now).
              # The only reason we do the copy is to avoid putting too many folders in sys.path.
              shutil.copy(UpperCAmelCase__,    submodule_path / module_file			)
              for module_needed in modules_needed:
                     A_		   = F'''{module_needed}.py'''
                     shutil.copy(os.path.join(UpperCAmelCase__,    UpperCAmelCase__			),    submodule_path / module_needed			)
       else:
              # Get the commit hash
              # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
              if isinstance(UpperCAmelCase__,    UpperCAmelCase__			):
                     A_		   = use_auth_token
              elif use_auth_token is True:
                     A_		   = HfFolder.get_token()
              else:
                     A_		   = None

              A_		   = model_info(UpperCAmelCase__,    revision=UpperCAmelCase__,    token=UpperCAmelCase__			).sha

              # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
              # benefit of versioning.
              A_		   = submodule_path / commit_hash
              A_		   = full_submodule + os.path.sep + commit_hash
              create_dynamic_module(UpperCAmelCase__			)

              if not (submodule_path / module_file).exists():
                     shutil.copy(UpperCAmelCase__,    submodule_path / module_file			)
              # Make sure we also have every file with relative
              for module_needed in modules_needed:
                     if not (submodule_path / module_needed).exists():
                            get_cached_module_file(
                                UpperCAmelCase__,    F'''{module_needed}.py''',    cache_dir=UpperCAmelCase__,    force_download=UpperCAmelCase__,    resume_download=UpperCAmelCase__,    proxies=UpperCAmelCase__,    use_auth_token=UpperCAmelCase__,    revision=UpperCAmelCase__,    local_files_only=UpperCAmelCase__,    )
       return os.path.join(UpperCAmelCase__,    UpperCAmelCase__			)







def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__ = None,    UpperCAmelCase__ = None,    UpperCAmelCase__ = False,    UpperCAmelCase__ = False,    UpperCAmelCase__ = None,    UpperCAmelCase__ = None,    UpperCAmelCase__ = None,    UpperCAmelCase__ = False,    **UpperCAmelCase__,    )     ->     Union[str, Any]:
       A_		   = get_cached_module_file(
           UpperCAmelCase__,    UpperCAmelCase__,    cache_dir=UpperCAmelCase__,    force_download=UpperCAmelCase__,    resume_download=UpperCAmelCase__,    proxies=UpperCAmelCase__,    use_auth_token=UpperCAmelCase__,    revision=UpperCAmelCase__,    local_files_only=UpperCAmelCase__,    )
       return get_class_in_module(UpperCAmelCase__,    final_module.replace(""".py""",    """"""			)			)



'''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    A__ :



    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__=1000						,			)  -> Optional[int]:





           '''simple docstring'''
           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    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> str:





           '''simple docstring'''
           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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> str:





           '''simple docstring'''
           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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> int:





           '''simple docstring'''
           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    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           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    A__ ( _snake_case  ,  _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						False
    lowercase		   =						False
    lowercase		   =						False

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



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           # `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    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = LayoutLMvaModelTester(self    )
           A_		   = ConfigTester(self						,			config_class=UpperCamelCase__						,			hidden_size=37    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=False    )  -> Union[str, Any]:





           '''simple docstring'''
           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    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           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    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__    )



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__    )



    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__    )







    @slow
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_		   = LayoutLMvaModel.from_pretrained(UpperCamelCase__    )
                  self.assertIsNotNone(UpperCamelCase__    )

def  UpperCAmelCase__      ( )     ->     Dict:
       A_		   = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
       return image







@require_torch
class    A__ ( unittest.TestCase     ):



    @cached_property
    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__    ) if is_vision_available() else None







    @slow
    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           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(
               [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]    ).to(UpperCamelCase__    )

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