






"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase			=      logging.get_logger(__name__)

UpperCamelCase			=      {
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}






def     A(  snake_case_						,       snake_case_						,       snake_case_						,       snake_case_						,       snake_case_		):
							"""simple docstring"""





							for attribute in key.split("."		):
														lowercase__:    Tuple        =      getattr(snake_case_						,       snake_case_		)

							if weight_type is not None:
														lowercase__:    Tuple        =      getattr(snake_case_						,       snake_case_		).shape
							else:
														lowercase__:    str        =      hf_pointer.shape

							assert hf_shape == value.shape, (
							    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
							    F""" {value.shape} for {full_name}"""
							)

							if weight_type == "weight":
														lowercase__:    Union[str, Any]        =      value
							elif weight_type == "weight_g":
														lowercase__:    Any        =      value
							elif weight_type == "weight_v":
														lowercase__:    Dict        =      value
							elif weight_type == "bias":
														lowercase__:    int        =      value
							else:
														lowercase__:    Any        =      value

							logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""		)






def     A(  snake_case_						,       snake_case_						,       snake_case_		):
							"""simple docstring"""





							lowercase__:    Optional[int]        =      []
							lowercase__:    Tuple        =      fairseq_model.state_dict()

							lowercase__:    List[str]        =      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														lowercase__:    int        =      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    snake_case_						,       snake_case_						,       snake_case_						,       snake_case_						,       hf_model.config.feat_extract_norm == "group"						,       )
																					lowercase__:    Optional[int]        =      True
														else:
																					for key, mapped_key in MAPPING.items():
																												lowercase__:    Any        =      """sew_d.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

																												if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
																																			lowercase__:    Any        =      True
																																			if "*" in mapped_key:
																																										lowercase__:    Any        =      name.split(snake_case_		)[0].split("."		)[-2]
																																										if not layer_index.isnumeric():
																																																	continue
																																										lowercase__:    Optional[Any]        =      mapped_key.replace("*"						,       snake_case_		)
																																			if "weight_g" in name:
																																										lowercase__:    Optional[Any]        =      """weight_g"""
																																			elif "weight_v" in name:
																																										lowercase__:    Any        =      """weight_v"""
																																			elif "weight" in name:
																																										lowercase__:    Any        =      """weight"""
																																			elif "bias" in name:
																																										lowercase__:    Optional[int]        =      """bias"""
																																			else:
																																										lowercase__:    Dict        =      None
																																			set_recursively(snake_case_						,       snake_case_						,       snake_case_						,       snake_case_						,       snake_case_		)
																												continue
														if not is_used:
																					unused_weights.append(snake_case_		)

							logger.warning(F"""Unused weights: {unused_weights}"""		)






def     A(  snake_case_						,       snake_case_						,       snake_case_						,       snake_case_						,       snake_case_		):
							"""simple docstring"""





							lowercase__:    Optional[int]        =      full_name.split("conv_layers."		)[-1]
							lowercase__:    Optional[Any]        =      name.split("."		)
							lowercase__:    Optional[Any]        =      int(items[0]		)
							lowercase__:    List[Any]        =      int(items[1]		)

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																					)
																					lowercase__:    List[Any]        =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""		)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																					)
																					lowercase__:    int        =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""		)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																					    " found."
																					)
																					lowercase__:    List[str]        =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																					)
																					lowercase__:    Tuple        =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
							else:
														unused_weights.append(snake_case_		)






def     A(  snake_case_						,       snake_case_		):
							"""simple docstring"""





							lowercase__:    List[Any]        =      SEWDConfig()
							if is_finetuned:
														lowercase__:    Optional[Any]        =      model.wav_encoder.wav_model.cfg
							else:
														lowercase__:    Dict        =      model.cfg

							lowercase__:    str        =      fs_config.conv_bias
							lowercase__:    int        =      eval(fs_config.conv_feature_layers		)
							lowercase__:    int        =      [x[0] for x in conv_layers]
							lowercase__:    Tuple        =      [x[1] for x in conv_layers]
							lowercase__:    Optional[Any]        =      [x[2] for x in conv_layers]
							lowercase__:    Tuple        =      """gelu"""
							lowercase__:    Dict        =      """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
							lowercase__:    Union[str, Any]        =      0.0
							lowercase__:    List[Any]        =      fs_config.activation_fn.name
							lowercase__:    Optional[int]        =      fs_config.encoder_embed_dim
							lowercase__:    int        =      0.02
							lowercase__:    Tuple        =      fs_config.encoder_ffn_embed_dim
							lowercase__:    Union[str, Any]        =      1E-5
							lowercase__:    Dict        =      fs_config.encoder_layerdrop
							lowercase__:    Any        =      fs_config.encoder_attention_heads
							lowercase__:    int        =      fs_config.conv_pos_groups
							lowercase__:    Union[str, Any]        =      fs_config.conv_pos
							lowercase__:    str        =      len(snake_case_		)
							lowercase__:    List[str]        =      fs_config.encoder_layers
							lowercase__:    List[Any]        =      fs_config.squeeze_factor
							# DeBERTa-specific parameters:
							lowercase__:    Any        =      fs_config.max_position_embeddings
							lowercase__:    List[str]        =      fs_config.position_buckets
							lowercase__:    Optional[Any]        =      fs_config.share_att_key
							lowercase__:    str        =      fs_config.relative_attention
							lowercase__:    Optional[Any]        =      fs_config.position_biased_input
							lowercase__:    Optional[int]        =      tuple(fs_config.pos_att_type.split("|"		)		)
							lowercase__:    Optional[int]        =      fs_config.norm_rel_ebd

							# take care of any params that are overridden by the Wav2VecCtc model
							if is_finetuned:
														lowercase__:    List[str]        =      model.cfg
														lowercase__:    List[Any]        =      fs_config.final_dropout
														lowercase__:    Any        =      fs_config.layerdrop
							lowercase__:    Dict        =      fs_config.activation_dropout
							lowercase__:    Any        =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
							lowercase__:    int        =      fs_config.attention_dropout
							lowercase__:    Union[str, Any]        =      fs_config.dropout_input
							lowercase__:    Dict        =      fs_config.dropout
							lowercase__:    Dict        =      fs_config.mask_channel_length
							lowercase__:    Any        =      fs_config.mask_channel_prob
							lowercase__:    Optional[Any]        =      fs_config.mask_length
							lowercase__:    Optional[int]        =      fs_config.mask_prob

							lowercase__:    List[Any]        =      """Wav2Vec2FeatureExtractor"""
							lowercase__:    Tuple        =      """Wav2Vec2CTCTokenizer"""

							return config






@torch.no_grad()
def     A(  snake_case_						,       snake_case_						,       snake_case_=None						,       snake_case_=None						,       snake_case_=True		):
							"""simple docstring"""





							if is_finetuned:
														lowercase__:    Dict        =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]						,       arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
							else:
														lowercase__:    Any        =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

							if config_path is not None:
														lowercase__:    Dict        =      SEWDConfig.from_pretrained(snake_case_		)
							else:
														lowercase__:    List[str]        =      convert_config(model[0]						,       snake_case_		)
							lowercase__:    List[Any]        =      model[0].eval()

							lowercase__:    Dict        =      True if config.feat_extract_norm == """layer""" else False
							lowercase__:    int        =      WavaVecaFeatureExtractor(
							    feature_size=1						,       sampling_rate=16000						,       padding_value=0						,       do_normalize=snake_case_						,       return_attention_mask=snake_case_						,       )

							if is_finetuned:
														if dict_path:
																					lowercase__:    Union[str, Any]        =      Dictionary.load(snake_case_		)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					lowercase__:    Dict        =      target_dict.pad_index
																					lowercase__:    Any        =      target_dict.bos_index
																					lowercase__:    Optional[int]        =      target_dict.pad_index
																					lowercase__:    Tuple        =      target_dict.bos_index
																					lowercase__:    List[Any]        =      target_dict.eos_index
																					lowercase__:    Union[str, Any]        =      len(target_dict.symbols		)
																					lowercase__:    Optional[Any]        =      os.path.join(snake_case_						,       "vocab.json"		)
																					if not os.path.isdir(snake_case_		):
																												logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(snake_case_		)		)
																												return
																					os.makedirs(snake_case_						,       exist_ok=snake_case_		)
																					with open(snake_case_						,       "w"						,       encoding="utf-8"		) as vocab_handle:
																												json.dump(target_dict.indices						,       snake_case_		)
																					lowercase__:    Any        =      WavaVecaCTCTokenizer(
																					    snake_case_						,       unk_token=target_dict.unk_word						,       pad_token=target_dict.pad_word						,       bos_token=target_dict.bos_word						,       eos_token=target_dict.eos_word						,       word_delimiter_token="|"						,       do_lower_case=snake_case_						,       )
																					lowercase__:    Optional[Any]        =      WavaVecaProcessor(feature_extractor=snake_case_						,       tokenizer=snake_case_		)
																					processor.save_pretrained(snake_case_		)

														lowercase__:    str        =      SEWDForCTC(snake_case_		)
							else:
														lowercase__:    Optional[int]        =      SEWDModel(snake_case_		)
														feature_extractor.save_pretrained(snake_case_		)

							recursively_load_weights(snake_case_						,       snake_case_						,       snake_case_		)

							hf_model.save_pretrained(snake_case_		)


if __name__ == "__main__":
		UpperCamelCase			=      argparse.ArgumentParser()
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
		parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
		parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
		parser.add_argument(
		    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
		)
		UpperCamelCase			=      parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)














"""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 		_a       :





   '''simple docstring'''






   def __init__(   self			,				UpperCAmelCase_			,				UpperCAmelCase_=13			,				UpperCAmelCase_=7			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=99			,				UpperCAmelCase_=32			,				UpperCAmelCase_=5			,				UpperCAmelCase_=4			,				UpperCAmelCase_=37			,				UpperCAmelCase_="gelu"			,				UpperCAmelCase_=0.1			,				UpperCAmelCase_=0.1			,				UpperCAmelCase_=512			,				UpperCAmelCase_=16			,				UpperCAmelCase_=2			,				UpperCAmelCase_=0.02			,				UpperCAmelCase_=3			,				UpperCAmelCase_=4			,				UpperCAmelCase_=None			,				UpperCAmelCase_=1_000			,				)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    int        =      parent
          lowercase__:    str        =      batch_size
          lowercase__:    Tuple        =      seq_length
          lowercase__:    Any        =      is_training
          lowercase__:    Union[str, Any]        =      use_input_mask
          lowercase__:    str        =      use_token_type_ids
          lowercase__:    Optional[int]        =      use_labels
          lowercase__:    List[str]        =      vocab_size
          lowercase__:    Optional[Any]        =      hidden_size
          lowercase__:    Optional[Any]        =      num_hidden_layers
          lowercase__:    Optional[int]        =      num_attention_heads
          lowercase__:    List[str]        =      intermediate_size
          lowercase__:    Any        =      hidden_act
          lowercase__:    Any        =      hidden_dropout_prob
          lowercase__:    List[str]        =      attention_probs_dropout_prob
          lowercase__:    Tuple        =      max_position_embeddings
          lowercase__:    Any        =      type_vocab_size
          lowercase__:    List[str]        =      type_sequence_label_size
          lowercase__:    Dict        =      initializer_range
          lowercase__:    Dict        =      num_labels
          lowercase__:    List[str]        =      num_choices
          lowercase__:    int        =      scope
          lowercase__:    List[Any]        =      range_bbox





   def __lowercase    (   self)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    int        =      ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size)

          lowercase__:    Tuple        =      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__:    List[str]        =      bbox[i, j, 3]
                               lowercase__:    List[str]        =      bbox[i, j, 1]
                               lowercase__:    Union[str, Any]        =      t
                        if bbox[i, j, 2] < bbox[i, j, 0]:
                               lowercase__:    Optional[int]        =      bbox[i, j, 2]
                               lowercase__:    Tuple        =      bbox[i, j, 0]
                               lowercase__:    Optional[Any]        =      t

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

          lowercase__:    str        =      None
          if self.use_token_type_ids:
                 lowercase__:    List[Any]        =      ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size)

          lowercase__:    Tuple        =      None
          lowercase__:    Dict        =      None
          lowercase__:    Any        =      None
          if self.use_labels:
                 lowercase__:    List[Any]        =      ids_tensor([self.batch_size]			,				self.type_sequence_label_size)
                 lowercase__:    Any        =      ids_tensor([self.batch_size, self.seq_length]			,				self.num_labels)
                 lowercase__:    List[str]        =      ids_tensor([self.batch_size]			,				self.num_choices)

          lowercase__:    int        =      self.get_config()

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





   def __lowercase    (   self)	-> Optional[int]:




          '''simple docstring'''




          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			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[Any]:




          '''simple docstring'''




          lowercase__:    Any        =      LayoutLMModel(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    Tuple        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)
          lowercase__:    Dict        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)
          lowercase__:    Any        =      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			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[Any]:




          '''simple docstring'''




          lowercase__:    int        =      LayoutLMForMaskedLM(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    str        =      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			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    Union[str, Any]        =      self.num_labels
          lowercase__:    Optional[Any]        =      LayoutLMForSequenceClassification(UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    Optional[Any]        =      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			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[str]:




          '''simple docstring'''




          lowercase__:    List[str]        =      self.num_labels
          lowercase__:    List[str]        =      LayoutLMForTokenClassification(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    List[Any]        =      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			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> str:




          '''simple docstring'''




          lowercase__:    Dict        =      LayoutLMForQuestionAnswering(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    List[Any]        =      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)	-> Tuple:




          '''simple docstring'''




          lowercase__:    int        =      self.prepare_config_and_inputs()
          (
              (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     
          ):    int        =      config_and_inputs
          lowercase__:    Optional[int]        =      {
              "input_ids": input_ids,
              "bbox": bbox,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict







@require_torch
class 		_a       (						lowercase_    ,					lowercase_    ,					unittest.TestCase   ):





   '''simple docstring'''






   UpperCamelCase__      					=    (
       (
           LayoutLMModel,
           LayoutLMForMaskedLM,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   UpperCamelCase__      					=    (
       {
           """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__      					=    True





   def __lowercase    (   self)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    List[str]        =      LayoutLMModelTester(self)
          lowercase__:    List[str]        =      ConfigTester(self			,				config_class=UpperCAmelCase_			,				hidden_size=37)





   def __lowercase    (   self)	-> Optional[Any]:




          '''simple docstring'''




          self.config_tester.run_common_tests()





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




          '''simple docstring'''




          lowercase__:    int        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCAmelCase_)





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




          '''simple docstring'''




          lowercase__:    List[Any]        =      self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowercase__:    Union[str, Any]        =      type
                 self.model_tester.create_and_check_model(*UpperCAmelCase_)





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




          '''simple docstring'''




          lowercase__:    Any        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_)





   def __lowercase    (   self)	-> Dict:




          '''simple docstring'''




          lowercase__:    List[Any]        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_)





   def __lowercase    (   self)	-> int:




          '''simple docstring'''




          lowercase__:    Dict        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_)





   def __lowercase    (   self)	-> str:




          '''simple docstring'''




          lowercase__:    Optional[Any]        =      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)	-> List[Any]:




          '''simple docstring'''




          pass







def     A(  ):
       """simple docstring"""





       lowercase__:    Any        =      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=snake_case_		)  # noqa: E231
       lowercase__:    Dict        =      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=snake_case_		)  # noqa: E231
       lowercase__:    Optional[Any]        =      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=snake_case_		)  # noqa: E231
       lowercase__:    Dict        =      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=snake_case_		)  # noqa: E231
       # these are sequence labels (i.e. at the token level)
       lowercase__:    int        =      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=snake_case_		)  # noqa: E231
       # fmt: on

       return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 		_a       (						unittest.TestCase   ):





   '''simple docstring'''






   @slow
   def __lowercase    (   self)	-> Any:




          '''simple docstring'''




          lowercase__:    str        =      LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    Dict        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    str        =      model(input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)

          # test the sequence output on [0, :3, :3]
          lowercase__:    Optional[int]        =      torch.tensor(
              [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]			,				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__:    List[Any]        =      torch.tensor([-0.65_80, -0.02_14, 0.85_52]			,				device=UpperCAmelCase_)

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





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




          '''simple docstring'''




          lowercase__:    int        =      LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,				num_labels=2).to(
              UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    List[Any]        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    str        =      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__:    Dict        =      outputs.loss
          lowercase__:    int        =      torch.Size([])
          self.assertEqual(loss.shape			,				UpperCAmelCase_)

          # test the shape of the logits
          lowercase__:    str        =      outputs.logits
          lowercase__:    Any        =      torch.Size((2, 2))
          self.assertEqual(logits.shape			,				UpperCAmelCase_)





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




          '''simple docstring'''




          lowercase__:    Tuple        =      LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,				num_labels=13).to(
              UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    List[Any]        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    Any        =      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__:    int        =      outputs.logits
          lowercase__:    int        =      torch.Size((2, 25, 13))
          self.assertEqual(logits.shape			,				UpperCAmelCase_)





   @slow
   def __lowercase    (   self)	-> Tuple:




          '''simple docstring'''




          lowercase__:    Tuple        =      LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    Tuple        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    str        =      model(input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)

          # test the shape of the logits
          lowercase__:    str        =      torch.Size((2, 25))
          self.assertEqual(outputs.start_logits.shape			,				UpperCAmelCase_)
          self.assertEqual(outputs.end_logits.shape			,				UpperCAmelCase_)






