


import logging
from pathlib import Path
from typing import Any, List, Tuple

import yaml


# loading package files: https://stackoverflow.com/a/20885799
try:
       import importlib.resources as pkg_resources
except ImportError:
       # Try backported to PY<37 `importlib_resources`.
       import importlib_resources as pkg_resources

from . import resources


__lowerCamelCase   	=  '''https://github.com/huggingface/datasets/tree/main/src/datasets/utils'''
__lowerCamelCase   	=  f'''{BASE_REF_URL}/{__file__}'''
__lowerCamelCase   	=  logging.getLogger(__name__)



def 			_a      (    __UpperCamelCase			):
     a_     :  Optional[int]				      =     pkg_resources.read_text(UpperCamelCase_	,	UpperCamelCase_			)
     return yaml.safe_load(UpperCamelCase_			), F'''{BASE_REF_URL}/resources/{resource}'''


__lowerCamelCase	,							__lowerCamelCase   	=  load_yaml_resource('''readme_structure.yaml''')

__lowerCamelCase   	=  [
    '''[Needs More Information]''',
    '''[More Information Needed]''',
    '''(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)''',
]

# Dictionary representation of section/readme, error_list, warning_list
__lowerCamelCase   	=  Tuple[dict, List[str], List[str]]


class 	a__						:

       def __init__( self   :  Union[str, Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  List[str] = None   ,				lowerCamelCase_   :  bool = False     ):
            a_     :  str				      =     name
            a_     :  Any				      =     level
            a_     :  str				      =     lines
            a_     :  List[str]				      =     """"""
            a_     :  Tuple				      =     True
            a_     :  Any				      =     {}
            a_     :  Optional[int]				      =     []
            a_     :  Tuple				      =     []
            if self.lines is not None:
                 self.parse(suppress_parsing_errors=UpperCAmelCase__     )

       def     UpperCAmelCase( self   :  int   ,				lowerCamelCase_   :  bool = False     ):
            a_     :  Any				      =     """"""
            a_     :  int				      =     []
            a_     :  List[Any]				      =     False
            for line in self.lines:
                 if line.strip(""" \n"""     ) == "":
                      continue
                 elif line.strip(""" \n"""     )[:3] == "```":
                      a_     :  Tuple				      =     not code_start
                 elif line.split()[0] == self.level + "#" and not code_start:
                      if current_sub_level != "":
                           a_     :  Optional[int]				      =     Section(UpperCAmelCase__   ,				self.level + """#"""   ,				UpperCAmelCase__     )
                           a_     :  Tuple				      =     []
                      else:
                           if current_lines != []:
                                self.text += "".join(UpperCAmelCase__     ).strip()
                                if self.text != "" and self.text not in FILLER_TEXT:
                                     a_     :  Dict				      =     False
                                a_     :  List[Any]				      =     []

                      a_     :  List[Any]				      =     """ """.join(line.split()[1:]     ).strip(""" \n"""     )
                 else:
                      current_lines.append(UpperCAmelCase__     )
            else:
                 if current_sub_level != "":
                      if current_sub_level in self.content:
                           self.parsing_error_list.append(
                               F'''Multiple sections with the same heading `{current_sub_level}` have been found. Please keep only one of these sections.'''     )
                      a_     :  str				      =     Section(UpperCAmelCase__   ,				self.level + """#"""   ,				UpperCAmelCase__     )
                 else:
                      if current_lines != []:
                           self.text += "".join(UpperCAmelCase__     ).strip()
                           if self.text != "" and self.text not in FILLER_TEXT:
                                a_     :  List[str]				      =     False

            if self.level == "" and not suppress_parsing_errors:
                 if self.parsing_error_list != [] or self.parsing_warning_list != []:
                      a_     :  Optional[Any]				      =     """\n""".join("""-\t""" + x for x in self.parsing_error_list + self.parsing_warning_list     )
                      a_     :  Optional[Any]				      =     F'''The following issues were found while parsing the README at `{self.name}`:\n''' + errors
                      raise ValueError(UpperCAmelCase__     )

       def     UpperCAmelCase( self   :  Tuple   ,				lowerCamelCase_   :  dict     ):
            a_     :  Tuple				      =     []
            a_     :  Tuple				      =     []
            if structure["allow_empty"] is False:
                 # If content is expected
                 if self.is_empty_text and self.content == {}:
                      # If no content is found, mention it in the error_list
                      error_list.append(F'''Expected some content in section `{self.name}` but it is empty.'''     )

            if structure["allow_empty_text"] is False:
                 # If some text is expected
                 if self.is_empty_text:
                      # If no text is found, mention it in the error_list
                      error_list.append(
                          F'''Expected some text in section `{self.name}` but it is empty (text in subsections are ignored).'''     )
                  # Subsections Validation
            if structure["subsections"] is not None:
                 # If subsections are expected
                 if self.content == {}:
                      # If no subsections are present
                      a_     :  str				      =     [subsection["""name"""] for subsection in structure["""subsections"""]]
                      # Mention the expected values in the error_list
                      error_list.append(
                          F'''Section `{self.name}` expected the following subsections: {", ".join(["`"+x+"`" for x in values]     )}. Found \'None\'.'''     )
                 else:
                      # If some subsections are present
                      a_     :  Optional[Any]				      =     [subsection["""name"""] for subsection in structure["""subsections"""]]
                      a_     :  Optional[int]				      =     False
                      for idx, name in enumerate(UpperCAmelCase__     ):
                           if name not in self.content:
                                # If the expected subsection is not present
                                error_list.append(F'''Section `{self.name}` is missing subsection: `{name}`.'''     )
                                a_     :  int				      =     True
                           else:
                                # If the subsection is present, validate subsection, return the result
                                # and concat the errors from subsection to section error_list

                                # Skip sublevel validation if current level is `###`
                                if self.level == "###":
                                     continue
                                else:
                                     a_      ,      a_      ,      a_     :  List[str]				      =     self.content[name].validate(
                                         structure["""subsections"""][idx]     )
                                error_list += subsec_error_list
                                warning_list += subsec_warning_list

                      if has_missing_subsections:  # we only allow to have extra subsections if all the other ones are here
                           for name in self.content:
                                if name not in structure_names:
                                     # If an extra subsection is present
                                     warning_list.append(
                                         F'''`{self.name}` has an extra subsection: `{name}`. Skipping further validation checks for this subsection as expected structure is unknown.'''     )
            if error_list:
                 # If there are errors, do not return the dictionary as it is invalid
                 return {}, error_list, warning_list
            else:
                 return self.to_dict(), error_list, warning_list

       def     UpperCAmelCase( self   :  Optional[int]     ):
            return {
                "name": self.name,
                "text": self.text,
                "is_empty_text": self.is_empty_text,
                "subsections": [value.to_dict() for value in self.content.values()],
            }


class 	a__						(    lowerCAmelCase_     ):  # Level 0

       def __init__( self   :  str   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  dict = None   ,				lowerCamelCase_   :  bool = False     ):
            super().__init__(name=UpperCAmelCase__   ,				level=""""""     )  # Not using lines here as we need to use a child class parse
            a_     :  List[str]				      =     structure
            a_     :  Union[str, Any]				      =     -2
            a_     :  Tuple				      =     0
            a_     :  Any				      =     lines
            if self.lines is not None:
                 self.parse(suppress_parsing_errors=UpperCAmelCase__     )

       def     UpperCAmelCase( self   :  Any     ):
            if self.structure is None:
                 a_      ,      a_      ,      a_     :  List[Any]				      =     self._validate(UpperCAmelCase__     )
            else:
                 a_      ,      a_      ,      a_     :  Dict				      =     self._validate(self.structure     )
            if error_list != [] or warning_list != []:
                 a_     :  List[Any]				      =     """\n""".join(["""-\t""" + x for x in error_list + warning_list]     )
                 a_     :  Union[str, Any]				      =     F'''The following issues were found for the README at `{self.name}`:\n''' + errors
                 raise ValueError(UpperCAmelCase__     )

       @classmethod
       def     UpperCAmelCase( cls   :  int   ,				lowerCamelCase_   :  Path   ,				lowerCamelCase_   :  dict = None   ,				lowerCamelCase_   :  bool = False     ):
            with open(UpperCAmelCase__   ,				encoding="""utf-8"""     ) as f:
                 a_     :  str				      =     f.readlines()
            return cls(UpperCAmelCase__   ,				UpperCAmelCase__   ,				UpperCAmelCase__   ,				suppress_parsing_errors=UpperCAmelCase__     )

       @classmethod
       def     UpperCAmelCase( cls   :  List[Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  dict = None   ,				lowerCamelCase_   :  str = "root"   ,				lowerCamelCase_   :  bool = False     ):
            a_     :  Optional[Any]				      =     string.split("""\n"""     )
            return cls(UpperCAmelCase__   ,				UpperCAmelCase__   ,				UpperCAmelCase__   ,				suppress_parsing_errors=UpperCAmelCase__     )

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				lowerCamelCase_   :  bool = False     ):
            # Skip Tags
            a_     :  Any				      =     0

            for line in self.lines:
                 self.yaml_tags_line_count += 1
                 if line.strip(""" \n"""     ) == "---":
                      self.tag_count += 1
                      if self.tag_count == 2:
                           break
                 line_count += 1
            if self.tag_count == 2:
                 a_     :  List[str]				      =     self.lines[line_count + 1 :]  # Get the last + 1 th item.
            else:
                 a_     :  List[Any]				      =     self.lines[self.tag_count :]
            super().parse(suppress_parsing_errors=UpperCAmelCase__     )

       def __str__( self   :  Union[str, Any]     ):
            return str(self.to_dict()     )

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Optional[int]     ):
            a_     :  Union[str, Any]				      =     []
            a_     :  Optional[int]				      =     []
            if self.yaml_tags_line_count == 0:
                 warning_list.append("""Empty YAML markers are present in the README."""     )
            elif self.tag_count == 0:
                 warning_list.append("""No YAML markers are present in the README."""     )
            elif self.tag_count == 1:
                 warning_list.append("""Only the start of YAML tags present in the README."""     )
            # Check how many first level sections are present.
            a_     :  int				      =     len(self.content.keys()     )
            if num_first_level_keys > 1:
                 # If more than one, add to the error list, continue
                 error_list.append(
                     F'''The README has several first-level headings: {", ".join(["`"+x+"`" for x in list(self.content.keys()     )]     )}. Only one heading is expected. Skipping further validation for this README.'''     )
            elif num_first_level_keys < 1:
                 # If less than one, append error.
                 error_list.append(
                     """The README has no first-level headings. One heading is expected. Skipping further validation for this README."""     )

            else:
                 # If one exactly
                 a_     :  int				      =     list(self.content.keys()     )[0]  # Get the key
                 if start_key.startswith("""Dataset Card for"""     ):  # Check correct start
                      # If the starting is correct, validate all the sections
                      a_      ,      a_      ,      a_     :  Tuple				      =     self.content[start_key].validate(
                          readme_structure["""subsections"""][0]     )
                      error_list += sec_error_list
                      warning_list += sec_warning_list
                 else:
                      # If not found, append error
                      error_list.append(
                          """No first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."""     )
            if error_list:
                 # If there are errors, do not return the dictionary as it is invalid
                 return {}, error_list, warning_list
            else:
                 return self.to_dict(), error_list, warning_list


if __name__ == "__main__":
       from argparse import ArgumentParser

       __lowerCamelCase   	=  ArgumentParser(usage='''Validate the content (excluding YAML tags) of a README.md file.''')
       ap.add_argument('''readme_filepath''')
       __lowerCamelCase   	=  ap.parse_args()
       __lowerCamelCase   	=  Path(args.readme_filepath)
       __lowerCamelCase   	=  ReadMe.from_readme(readme_filepath)






"""simple docstring"""





import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
   import torch

   from transformers import (
       LayoutLMForMaskedLM,
       LayoutLMForQuestionAnswering,
       LayoutLMForSequenceClassification,
       LayoutLMForTokenClassification,
       LayoutLMModel,
   )




class    _lowerCAmelCase    :







 """simple docstring"""







 def __init__(     self				:     Optional[int],   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     Dict=1_3,   UpperCAmelCase__				:     Tuple=7,   UpperCAmelCase__				:     Union[str, Any]=True,   UpperCAmelCase__				:     Tuple=True,   UpperCAmelCase__				:     Dict=True,   UpperCAmelCase__				:     List[str]=True,   UpperCAmelCase__				:     Optional[int]=9_9,   UpperCAmelCase__				:     Union[str, Any]=3_2,   UpperCAmelCase__				:     str=5,   UpperCAmelCase__				:     int=4,   UpperCAmelCase__				:     Any=3_7,   UpperCAmelCase__				:     Tuple="gelu",   UpperCAmelCase__				:     str=0.1,   UpperCAmelCase__				:     Tuple=0.1,   UpperCAmelCase__				:     Any=5_1_2,   UpperCAmelCase__				:     Optional[Any]=1_6,   UpperCAmelCase__				:     Optional[Any]=2,   UpperCAmelCase__				:     Tuple=0.02,   UpperCAmelCase__				:     List[str]=3,   UpperCAmelCase__				:     Any=4,   UpperCAmelCase__				:     Dict=None,   UpperCAmelCase__				:     Dict=1_0_0_0,   ):
    __lowercase								=		parent
    __lowercase								=		batch_size
    __lowercase								=		seq_length
    __lowercase								=		is_training
    __lowercase								=		use_input_mask
    __lowercase								=		use_token_type_ids
    __lowercase								=		use_labels
    __lowercase								=		vocab_size
    __lowercase								=		hidden_size
    __lowercase								=		num_hidden_layers
    __lowercase								=		num_attention_heads
    __lowercase								=		intermediate_size
    __lowercase								=		hidden_act
    __lowercase								=		hidden_dropout_prob
    __lowercase								=		attention_probs_dropout_prob
    __lowercase								=		max_position_embeddings
    __lowercase								=		type_vocab_size
    __lowercase								=		type_sequence_label_size
    __lowercase								=		initializer_range
    __lowercase								=		num_labels
    __lowercase								=		num_choices
    __lowercase								=		scope
    __lowercase								=		range_bbox


 def    _lowercase					(     self				:     Union[str, Any]      ):
    __lowercase								=		ids_tensor([self.batch_size, self.seq_length],   self.vocab_size      )

    __lowercase								=		ids_tensor([self.batch_size, self.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]:
             __lowercase								=		bbox[i, j, 3]
             __lowercase								=		bbox[i, j, 1]
             __lowercase								=		t
          if bbox[i, j, 2] < bbox[i, j, 0]:
             __lowercase								=		bbox[i, j, 2]
             __lowercase								=		bbox[i, j, 0]
             __lowercase								=		t

    __lowercase								=		None
    if self.use_input_mask:
       __lowercase								=		ids_tensor([self.batch_size, self.seq_length],   vocab_size=2      )

    __lowercase								=		None
    if self.use_token_type_ids:
       __lowercase								=		ids_tensor([self.batch_size, self.seq_length],   self.type_vocab_size      )

    __lowercase								=		None
    __lowercase								=		None
    __lowercase								=		None
    if self.use_labels:
       __lowercase								=		ids_tensor([self.batch_size],   self.type_sequence_label_size      )
       __lowercase								=		ids_tensor([self.batch_size, self.seq_length],   self.num_labels      )
       __lowercase								=		ids_tensor([self.batch_size],   self.num_choices      )

    __lowercase								=		self.get_config()

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


 def    _lowercase					(     self				:     Tuple      ):
    return LayoutLMConfig(
        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,   )


 def    _lowercase					(     self				:     Optional[Any],   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     Optional[int],   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     Any      ):
    __lowercase								=		LayoutLMModel(config=UpperCAmelCase__      )
    model.to(UpperCAmelCase__      )
    model.eval()
    __lowercase								=		model(UpperCAmelCase__,   UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   token_type_ids=UpperCAmelCase__      )
    __lowercase								=		model(UpperCAmelCase__,   UpperCAmelCase__,   token_type_ids=UpperCAmelCase__      )
    __lowercase								=		model(UpperCAmelCase__,   UpperCAmelCase__      )
    self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)      )
    self.parent.assertEqual(result.pooler_output.shape,   (self.batch_size, self.hidden_size)      )


 def    _lowercase					(     self				:     Tuple,   UpperCAmelCase__				:     Tuple,   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     Optional[int],   UpperCAmelCase__				:     Dict      ):
    __lowercase								=		LayoutLMForMaskedLM(config=UpperCAmelCase__      )
    model.to(UpperCAmelCase__      )
    model.eval()
    __lowercase								=		model(UpperCAmelCase__,   UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   token_type_ids=UpperCAmelCase__,   labels=UpperCAmelCase__      )
    self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)      )


 def    _lowercase					(     self				:     Tuple,   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Optional[int],   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Optional[int]      ):
    __lowercase								=		self.num_labels
    __lowercase								=		LayoutLMForSequenceClassification(UpperCAmelCase__      )
    model.to(UpperCAmelCase__      )
    model.eval()
    __lowercase								=		model(
        UpperCAmelCase__,   UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   token_type_ids=UpperCAmelCase__,   labels=UpperCAmelCase__      )
    self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)      )


 def    _lowercase					(     self				:     Any,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Any      ):
    __lowercase								=		self.num_labels
    __lowercase								=		LayoutLMForTokenClassification(config=UpperCAmelCase__      )
    model.to(UpperCAmelCase__      )
    model.eval()
    __lowercase								=		model(UpperCAmelCase__,   UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   token_type_ids=UpperCAmelCase__,   labels=UpperCAmelCase__      )
    self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels)      )


 def    _lowercase					(     self				:     Optional[Any],   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Optional[int],   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Dict      ):
    __lowercase								=		LayoutLMForQuestionAnswering(config=UpperCAmelCase__      )
    model.to(UpperCAmelCase__      )
    model.eval()
    __lowercase								=		model(
        UpperCAmelCase__,   bbox=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    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		self.prepare_config_and_inputs()
    (
        (
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,
    )								=		config_and_inputs
    __lowercase								=		{
        "input_ids": input_ids,
        "bbox": bbox,
        "token_type_ids": token_type_ids,
        "attention_mask": input_mask,
    }
    return config, inputs_dict







@require_torch
class    _lowerCAmelCase    (      lowercase	,lowercase	,unittest.TestCase  ):







 """simple docstring"""







 __UpperCAmelCase      :   Dict  						=      (
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 __UpperCAmelCase      :   Union[str, Any]  						=      (
     {
         "document-question-answering": LayoutLMForQuestionAnswering,
         "feature-extraction": LayoutLMModel,
         "fill-mask": LayoutLMForMaskedLM,
         "text-classification": LayoutLMForSequenceClassification,
         "token-classification": LayoutLMForTokenClassification,
         "zero-shot": LayoutLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __UpperCAmelCase      :   int  						=      True


 def    _lowercase					(     self				:     str      ):
    __lowercase								=		LayoutLMModelTester(self      )
    __lowercase								=		ConfigTester(self,   config_class=UpperCAmelCase__,   hidden_size=3_7      )


 def    _lowercase					(     self				:     int      ):
    self.config_tester.run_common_tests()


 def    _lowercase					(     self				:     List[str]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__      )


 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
       __lowercase								=		type
       self.model_tester.create_and_check_model(*UpperCAmelCase__      )


 def    _lowercase					(     self				:     List[Any]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__      )


 def    _lowercase					(     self				:     Optional[Any]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__      )


 def    _lowercase					(     self				:     Union[str, Any]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__      )


 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__      )



 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
 def    _lowercase					(     self				:     Tuple      ):
    pass







def     _A    (    )      ->		Optional[int]:
   '''simple docstring'''


   __lowercase								=		torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]],     device=UpperCamelCase_)  # noqa: E231
   __lowercase								=		torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],],     device=UpperCamelCase_)  # noqa: E231
   __lowercase								=		torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]],     device=UpperCamelCase_)  # noqa: E231
   __lowercase								=		torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],     device=UpperCamelCase_)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   __lowercase								=		torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]],     device=UpperCamelCase_)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels




@require_torch
class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 @slow
 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"      ).to(UpperCAmelCase__      )

    __lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase								=		prepare_layoutlm_batch_inputs()

    # forward pass
    __lowercase								=		model(input_ids=UpperCAmelCase__,   bbox=UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   token_type_ids=UpperCAmelCase__      )

    # test the sequence output on [0, :3, :3]
    __lowercase								=		torch.tensor(
        [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]],   device=UpperCAmelCase__,   )

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

    # test the pooled output on [1, :3]
    __lowercase								=		torch.tensor([-0.6_580, -0.0_214, 0.8_552],   device=UpperCAmelCase__      )

    self.assertTrue(torch.allclose(outputs.pooler_output[1, :3],   UpperCAmelCase__,   atol=1E-3      )      )


 @slow
 def    _lowercase					(     self				:     str      ):
    # initialize model with randomly initialized sequence classification head
    __lowercase								=		LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased",   num_labels=2      ).to(
        UpperCAmelCase__      )

    __lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase								=		prepare_layoutlm_batch_inputs()

    # forward pass
    __lowercase								=		model(
        input_ids=UpperCAmelCase__,   bbox=UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   token_type_ids=UpperCAmelCase__,   labels=torch.tensor([1, 1],   device=UpperCAmelCase__      ),   )

    # test whether we get a loss as a scalar
    __lowercase								=		outputs.loss
    __lowercase								=		torch.Size([]      )
    self.assertEqual(loss.shape,   UpperCAmelCase__      )

    # test the shape of the logits
    __lowercase								=		outputs.logits
    __lowercase								=		torch.Size((2, 2)      )
    self.assertEqual(logits.shape,   UpperCAmelCase__      )


 @slow
 def    _lowercase					(     self				:     List[str]      ):
    # initialize model with randomly initialized token classification head
    __lowercase								=		LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased",   num_labels=1_3      ).to(
        UpperCAmelCase__      )

    __lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase								=		prepare_layoutlm_batch_inputs()

    # forward pass
    __lowercase								=		model(
        input_ids=UpperCAmelCase__,   bbox=UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   token_type_ids=UpperCAmelCase__,   labels=UpperCAmelCase__      )

    # test the loss calculation to be around 2.65
    # expected_loss = torch.tensor(2.65, device=torch_device)

    # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
    # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

    # test the shape of the logits
    __lowercase								=		outputs.logits
    __lowercase								=		torch.Size((2, 2_5, 1_3)      )
    self.assertEqual(logits.shape,   UpperCAmelCase__      )



 @slow
 def    _lowercase					(     self				:     Dict      ):
    # initialize model with randomly initialized token classification head
    __lowercase								=		LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"      ).to(UpperCAmelCase__      )

    __lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase								=		prepare_layoutlm_batch_inputs()

    # forward pass
    __lowercase								=		model(input_ids=UpperCAmelCase__,   bbox=UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   token_type_ids=UpperCAmelCase__      )

    # test the shape of the logits
    __lowercase								=		torch.Size((2, 2_5)      )
    self.assertEqual(outputs.start_logits.shape,   UpperCAmelCase__      )
    self.assertEqual(outputs.end_logits.shape,   UpperCAmelCase__      )




