

'''simple docstring'''

import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
 raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
 raise Exception("requires fairseq < v2")

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

lowercase__         =      "Hello, World!"
lowercase__         =      "en_XX"




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     List[Any]						 =			Path('data_bin'     )
 UpperCAmelCase	:     List[str]						 =			FairseqXmodModel.from_pretrained(
     model_name_or_path=str(Path(UpperCAmelCase_     ).parent     )  ,	checkpoint_file=Path(UpperCAmelCase_     ).name  ,	_name='xmod_base'  ,	arch='xmod_base'  ,	task='multilingual_masked_lm'  ,	data_name_or_path=str(UpperCAmelCase_     )  ,	bpe='sentencepiece'  ,	sentencepiece_model=str(Path(UpperCAmelCase_     ).parent / 'sentencepiece.bpe.model'     )  ,	src_dict=str(data_dir / 'dict.txt'     )  ,	)
 xmod.eval()  # disable dropout
 print(UpperCAmelCase_     )

 UpperCAmelCase	:     List[Any]						 =			xmod.model.encoder.sentence_encoder
 UpperCAmelCase	:     Union[str, Any]						 =			XmodConfig(
     vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings  ,	hidden_size=xmod.cfg.model.encoder_embed_dim  ,	num_hidden_layers=xmod.cfg.model.encoder_layers  ,	num_attention_heads=xmod.cfg.model.encoder_attention_heads  ,	intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim  ,	max_position_embeddings=5_14  ,	type_vocab_size=1  ,	layer_norm_eps=1E-5  ,	pre_norm=xmod.cfg.model.encoder_normalize_before  ,	adapter_reduction_factor=getattr(xmod.cfg.model  ,	'bottleneck'  ,	2     )  ,	adapter_layer_norm=xmod.cfg.model.adapter_layer_norm  ,	adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm  ,	ln_before_adapter=xmod.cfg.model.ln_before_adapter  ,	languages=xmod.cfg.model.languages  ,	)
 if classification_head:
  UpperCAmelCase	:     Dict						 =			xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

 print('Our X-MOD config:'  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     Optional[Any]						 =			XmodForSequenceClassification(UpperCAmelCase_     ) if classification_head else XmodForMaskedLM(UpperCAmelCase_     )
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 UpperCAmelCase	:     Dict						 =			xmod_sent_encoder.embed_tokens.weight
 UpperCAmelCase	:     List[Any]						 =			xmod_sent_encoder.embed_positions.weight
 UpperCAmelCase	:     Tuple						 =			torch.zeros_like(
     model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c xmod doesn't use them.

 UpperCAmelCase	:     Union[str, Any]						 =			xmod_sent_encoder.layernorm_embedding.weight
 UpperCAmelCase	:     int						 =			xmod_sent_encoder.layernorm_embedding.bias

 for i in range(config.num_hidden_layers     ):
  # Encoder: start of layer
  UpperCAmelCase	:     int						 =			model.roberta.encoder.layer[i]
  UpperCAmelCase	:     int						 =			xmod_sent_encoder.layers[i]

  # self attention
  UpperCAmelCase	:     int						 =			layer.attention.self
  if not (
      xmod_layer.self_attn.k_proj.weight.data.shape
      == xmod_layer.self_attn.q_proj.weight.data.shape
      == xmod_layer.self_attn.v_proj.weight.data.shape
      == torch.Size((config.hidden_size, config.hidden_size)     )
  ):
   raise AssertionError('Dimensions of self-attention weights do not match.'     )

  UpperCAmelCase	:     List[Any]						 =			xmod_layer.self_attn.q_proj.weight
  UpperCAmelCase	:     Dict						 =			xmod_layer.self_attn.q_proj.bias
  UpperCAmelCase	:     Optional[Any]						 =			xmod_layer.self_attn.k_proj.weight
  UpperCAmelCase	:     Optional[int]						 =			xmod_layer.self_attn.k_proj.bias
  UpperCAmelCase	:     List[Any]						 =			xmod_layer.self_attn.v_proj.weight
  UpperCAmelCase	:     Optional[int]						 =			xmod_layer.self_attn.v_proj.bias

  # self-attention output
  UpperCAmelCase	:     str						 =			layer.attention.output
  if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
   raise AssertionError('Dimensions of self-attention output weights do not match.'     )
  UpperCAmelCase	:     Tuple						 =			xmod_layer.self_attn.out_proj.weight
  UpperCAmelCase	:     Optional[Any]						 =			xmod_layer.self_attn.out_proj.bias
  UpperCAmelCase	:     int						 =			xmod_layer.self_attn_layer_norm.weight
  UpperCAmelCase	:     Union[str, Any]						 =			xmod_layer.self_attn_layer_norm.bias

  # intermediate
  UpperCAmelCase	:     int						 =			layer.intermediate
  if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
   raise AssertionError('Dimensions of intermediate weights do not match.'     )
  UpperCAmelCase	:     Union[str, Any]						 =			xmod_layer.fca.weight
  UpperCAmelCase	:     str						 =			xmod_layer.fca.bias

  # output
  UpperCAmelCase	:     Optional[int]						 =			layer.output
  if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
   raise AssertionError('Dimensions of feed-forward weights do not match.'     )
  UpperCAmelCase	:     Optional[Any]						 =			xmod_layer.fca.weight
  UpperCAmelCase	:     List[str]						 =			xmod_layer.fca.bias
  UpperCAmelCase	:     int						 =			xmod_layer.final_layer_norm.weight
  UpperCAmelCase	:     List[str]						 =			xmod_layer.final_layer_norm.bias
  if bert_output.adapter_layer_norm is not None:
   UpperCAmelCase	:     Optional[Any]						 =			xmod_layer.adapter_layer_norm.weight
   UpperCAmelCase	:     str						 =			xmod_layer.adapter_layer_norm.bias

  if sorted(bert_output.adapter_modules.keys()     ) != sorted(xmod_layer.adapter_modules.keys()     ):
   raise AssertionError('Lists of language adapters do not match.'     )
  for lang_code, adapter in xmod_layer.adapter_modules.items():
   UpperCAmelCase	:     str						 =			bert_output.adapter_modules[lang_code]
   UpperCAmelCase	:     Tuple						 =			xmod_layer.adapter_modules[lang_code]
   UpperCAmelCase	:     Union[str, Any]						 =			from_adapter.fca.weight
   UpperCAmelCase	:     str						 =			from_adapter.fca.bias
   UpperCAmelCase	:     Optional[int]						 =			from_adapter.fca.weight
   UpperCAmelCase	:     Tuple						 =			from_adapter.fca.bias

  # end of layer

 if xmod_sent_encoder.layer_norm is not None:
  UpperCAmelCase	:     List[str]						 =			xmod_sent_encoder.layer_norm.weight
  UpperCAmelCase	:     int						 =			xmod_sent_encoder.layer_norm.bias

 if classification_head:
  UpperCAmelCase	:     Tuple						 =			xmod.model.classification_heads['mnli'].dense.weight
  UpperCAmelCase	:     Dict						 =			xmod.model.classification_heads['mnli'].dense.bias
  UpperCAmelCase	:     Any						 =			xmod.model.classification_heads['mnli'].out_proj.weight
  UpperCAmelCase	:     str						 =			xmod.model.classification_heads['mnli'].out_proj.bias
 else:
  # LM Head
  UpperCAmelCase	:     Tuple						 =			xmod.model.encoder.lm_head.dense.weight
  UpperCAmelCase	:     int						 =			xmod.model.encoder.lm_head.dense.bias
  UpperCAmelCase	:     List[str]						 =			xmod.model.encoder.lm_head.layer_norm.weight
  UpperCAmelCase	:     Optional[Any]						 =			xmod.model.encoder.lm_head.layer_norm.bias
  UpperCAmelCase	:     Any						 =			xmod.model.encoder.lm_head.weight
  UpperCAmelCase	:     Dict						 =			xmod.model.encoder.lm_head.bias

 # Let's check that we get the same results.
 UpperCAmelCase	:     Dict						 =			xmod.encode(UpperCAmelCase_     ).unsqueeze(0     )  # batch of size 1
 model.roberta.set_default_language(UpperCAmelCase_     )

 UpperCAmelCase	:     Optional[int]						 =			model(UpperCAmelCase_     )[0]
 if classification_head:
  UpperCAmelCase	:     Tuple						 =			xmod.model.classification_heads['mnli'](xmod.extract_features(UpperCAmelCase_     )     )
 else:
  UpperCAmelCase	:     Tuple						 =			xmod.model(UpperCAmelCase_  ,	lang_id=[SAMPLE_LANGUAGE]     )[0]
 print(our_output.shape  ,	their_output.shape     )
 UpperCAmelCase	:     int						 =			torch.max(torch.abs(our_output - their_output     )     ).item()
 print(F"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
 UpperCAmelCase	:     List[str]						 =			torch.allclose(UpperCAmelCase_  ,	UpperCAmelCase_  ,	atol=1E-3     )
 print('Do both models output the same tensors?'  ,	'🔥' if success else '💩'     )
 if not success:
  raise Exception('Something went wRoNg'     )

 Path(UpperCAmelCase_     ).mkdir(parents=UpperCAmelCase_  ,	exist_ok=UpperCAmelCase_     )
 print(F"""Saving model to {pytorch_dump_folder_path}"""     )
 model.save_pretrained(UpperCAmelCase_     )


if __name__ == "__main__":
 lowercase__         =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--xmod_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--classification_head", action="store_true", help="Whether to convert a final classification head."
 )
 lowercase__         =      parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )







'''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_			:






     '''simple docstring'''



     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					str					,      lowercase_			:					Union[str, Any]=2					,      lowercase_			:					Optional[Any]=3					,      lowercase_			:					List[Any]=4					,      lowercase_			:					Union[str, Any]=2					,      lowercase_			:					List[Any]=7					,      lowercase_			:					Any=True					,      lowercase_			:					Tuple=True					,      lowercase_			:					List[str]=True					,      lowercase_			:					Union[str, Any]=True					,      lowercase_			:					str=99					,      lowercase_			:					str=36					,      lowercase_			:					int=3					,      lowercase_			:					int=4					,      lowercase_			:					Any=37					,      lowercase_			:					str="gelu"					,      lowercase_			:					List[str]=0.1					,      lowercase_			:					Optional[int]=0.1					,      lowercase_			:					int=512					,      lowercase_			:					int=16					,      lowercase_			:					Dict=2					,      lowercase_			:					Dict=0.02					,      lowercase_			:					Optional[int]=6					,      lowercase_			:					Tuple=6					,      lowercase_			:					Any=3					,      lowercase_			:					Dict=4					,      lowercase_			:					Any=None					,      lowercase_			:					Tuple=1_000					,      )						->     Tuple:
      UpperCAmelCase	:     List[Any]						 =			parent
      UpperCAmelCase	:     List[Any]						 =			batch_size
      UpperCAmelCase	:     List[Any]						 =			num_channels
      UpperCAmelCase	:     Optional[int]						 =			image_size
      UpperCAmelCase	:     int						 =			patch_size
      UpperCAmelCase	:     Tuple						 =			text_seq_length
      UpperCAmelCase	:     int						 =			is_training
      UpperCAmelCase	:     Any						 =			use_input_mask
      UpperCAmelCase	:     Optional[int]						 =			use_token_type_ids
      UpperCAmelCase	:     int						 =			use_labels
      UpperCAmelCase	:     Dict						 =			vocab_size
      UpperCAmelCase	:     List[str]						 =			hidden_size
      UpperCAmelCase	:     Any						 =			num_hidden_layers
      UpperCAmelCase	:     str						 =			num_attention_heads
      UpperCAmelCase	:     Tuple						 =			intermediate_size
      UpperCAmelCase	:     Optional[int]						 =			hidden_act
      UpperCAmelCase	:     str						 =			hidden_dropout_prob
      UpperCAmelCase	:     Any						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     Tuple						 =			max_position_embeddings
      UpperCAmelCase	:     List[str]						 =			type_vocab_size
      UpperCAmelCase	:     List[str]						 =			type_sequence_label_size
      UpperCAmelCase	:     int						 =			initializer_range
      UpperCAmelCase	:     Optional[int]						 =			coordinate_size
      UpperCAmelCase	:     Optional[int]						 =			shape_size
      UpperCAmelCase	:     str						 =			num_labels
      UpperCAmelCase	:     str						 =			num_choices
      UpperCAmelCase	:     int						 =			scope
      UpperCAmelCase	:     Tuple						 =			range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      UpperCAmelCase	:     Any						 =			text_seq_length
      UpperCAmelCase	:     int						 =			(image_size // patch_size) ** 2 + 1
      UpperCAmelCase	:     Optional[int]						 =			self.text_seq_length + self.image_seq_length


     def        UpperCAmelCase_	(		self			:					int	)						->     Union[str, Any]:
      UpperCAmelCase	:     Optional[int]						 =			ids_tensor([self.batch_size, self.text_seq_length]					,      self.vocab_size	)

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

      UpperCAmelCase	:     List[str]						 =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      UpperCAmelCase	:     int						 =			None
      if self.use_input_mask:
       UpperCAmelCase	:     Union[str, Any]						 =			random_attention_mask([self.batch_size, self.text_seq_length]	)

      UpperCAmelCase	:     int						 =			None
      if self.use_token_type_ids:
       UpperCAmelCase	:     List[Any]						 =			ids_tensor([self.batch_size, self.text_seq_length]					,      self.type_vocab_size	)

      UpperCAmelCase	:     Dict						 =			None
      UpperCAmelCase	:     Dict						 =			None
      if self.use_labels:
       UpperCAmelCase	:     Dict						 =			ids_tensor([self.batch_size]					,      self.type_sequence_label_size	)
       UpperCAmelCase	:     int						 =			ids_tensor([self.batch_size, self.text_seq_length]					,      self.num_labels	)

      UpperCAmelCase	:     str						 =			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        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					str					,      lowercase_			:					Tuple					,      lowercase_			:					Any					,      lowercase_			:					Optional[int]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					str					,      lowercase_			:					str					,      lowercase_			:					Optional[Any]	)						->     Any:
      UpperCAmelCase	:     Dict						 =			LayoutLMvaModel(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()

      # text + image
      UpperCAmelCase	:     Optional[Any]						 =			model(lowercase_					,      pixel_values=lowercase_	)
      UpperCAmelCase	:     str						 =			model(
          lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_	)
      UpperCAmelCase	:     Any						 =			model(lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_					,      token_type_ids=lowercase_	)
      UpperCAmelCase	:     str						 =			model(lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_	)

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

      # text only
      UpperCAmelCase	:     List[Any]						 =			model(lowercase_	)
      self.parent.assertEqual(
          result.last_hidden_state.shape					,      (self.batch_size, self.text_seq_length, self.hidden_size)	)

      # image only
      UpperCAmelCase	:     Dict						 =			model(pixel_values=lowercase_	)
      self.parent.assertEqual(
          result.last_hidden_state.shape					,      (self.batch_size, self.image_seq_length, self.hidden_size)	)


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					int					,      lowercase_			:					Tuple					,      lowercase_			:					List[Any]					,      lowercase_			:					Tuple					,      lowercase_			:					Tuple					,      lowercase_			:					str					,      lowercase_			:					Any					,      lowercase_			:					Union[str, Any]	)						->     Union[str, Any]:
      UpperCAmelCase	:     List[Any]						 =			self.num_labels
      UpperCAmelCase	:     Union[str, Any]						 =			LayoutLMvaForSequenceClassification(lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     int						 =			model(
          lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      labels=lowercase_					,      )
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)	)


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					int					,      lowercase_			:					str					,      lowercase_			:					Any					,      lowercase_			:					int					,      lowercase_			:					Any					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					int	)						->     Any:
      UpperCAmelCase	:     Optional[int]						 =			self.num_labels
      UpperCAmelCase	:     int						 =			LayoutLMvaForTokenClassification(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Optional[Any]						 =			model(
          lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      labels=lowercase_					,      )
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.text_seq_length, self.num_labels)	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Tuple					,      lowercase_			:					Tuple					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					List[str]					,      lowercase_			:					Tuple					,      lowercase_			:					Any					,      lowercase_			:					Optional[int]	)						->     Optional[int]:
      UpperCAmelCase	:     Union[str, Any]						 =			LayoutLMvaForQuestionAnswering(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     List[str]						 =			model(
          lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      start_positions=lowercase_					,      end_positions=lowercase_					,      )
      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        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      UpperCAmelCase	:     List[str]						 =			self.prepare_config_and_inputs()
      (
          (
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			
      )	:     Optional[int]						 =			config_and_inputs
      UpperCAmelCase	:     Optional[Any]						 =			{
          '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							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  False
     UpperCAmelCase_						:     Dict  		=  False
     UpperCAmelCase_						:     List[Any]  		=  False

     UpperCAmelCase_						:     List[str]  		=  (
         (
             LayoutLMvaModel,
             LayoutLMvaForSequenceClassification,
             LayoutLMvaForTokenClassification,
             LayoutLMvaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase_						:     List[str]  		=  (
         {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
         if is_torch_available()
         else {}
     )


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					int					,      lowercase_			:					List[Any]					,      lowercase_			:					List[Any]					,      lowercase_			:					Optional[int]					,      lowercase_			:					Optional[Any]	)						->     Union[str, Any]:
      # `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        UpperCAmelCase_	(		self			:					str	)						->     Any:
      UpperCAmelCase	:     Union[str, Any]						 =			LayoutLMvaModelTester(self	)
      UpperCAmelCase	:     Optional[Any]						 =			ConfigTester(self					,      config_class=lowercase_					,      hidden_size=37	)


     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					int					,      lowercase_			:					Dict					,      lowercase_			:					Any=False	)						->     Optional[Any]:
      UpperCAmelCase	:     str						 =			copy.deepcopy(lowercase_	)
      if model_class in get_values(lowercase_	):
       UpperCAmelCase	:     str						 =			{
           k: v.unsqueeze(1	).expand(-1					,      self.model_tester.num_choices					,      -1	).contiguous()
           if isinstance(lowercase_					,      torch.Tensor	) and v.ndim > 1
           else v
           for k, v in inputs_dict.items()
       }
      if return_labels:
       if model_class in get_values(lowercase_	):
        UpperCAmelCase	:     Dict						 =			torch.ones(self.model_tester.batch_size					,      dtype=torch.long					,      device=lowercase_	)
       elif model_class in get_values(lowercase_	):
        UpperCAmelCase	:     int						 =			torch.zeros(
            self.model_tester.batch_size					,      dtype=torch.long					,      device=lowercase_	)
        UpperCAmelCase	:     Optional[int]						 =			torch.zeros(
            self.model_tester.batch_size					,      dtype=torch.long					,      device=lowercase_	)
       elif model_class in [
           *get_values(lowercase_	),
       ]:
        UpperCAmelCase	:     List[Any]						 =			torch.zeros(
            self.model_tester.batch_size					,      dtype=torch.long					,      device=lowercase_	)
       elif model_class in [
           *get_values(lowercase_	),
       ]:
        UpperCAmelCase	:     Tuple						 =			torch.zeros(
            (self.model_tester.batch_size, self.model_tester.text_seq_length)					,      dtype=torch.long					,      device=lowercase_					,      )

      return inputs_dict


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     Union[str, Any]:
      self.config_tester.run_common_tests()


     def        UpperCAmelCase_	(		self			:					Any	)						->     Any:
      UpperCAmelCase	:     str						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     int:
      UpperCAmelCase	:     str						 =			self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
       UpperCAmelCase	:     Optional[int]						 =			type
       self.model_tester.create_and_check_model(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     str:
      UpperCAmelCase	:     List[str]						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Dict	)						->     Any:
      UpperCAmelCase	:     Dict						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Any:
      UpperCAmelCase	:     Tuple						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowercase_	)




     @slow
     def        UpperCAmelCase_	(		self			:					Any	)						->     Optional[Any]:
      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase	:     int						 =			LayoutLMvaModel.from_pretrained(lowercase_	)
       self.assertIsNotNone(lowercase_	)




def 				UpperCamelCase(					):
 UpperCAmelCase	:     List[str]						 =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
 return image




@require_torch
class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     @cached_property
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Dict:
      return LayoutLMvaImageProcessor(apply_ocr=lowercase_	) if is_vision_available() else None




     @slow
     def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[str]:
      UpperCAmelCase	:     int						 =			LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'	).to(lowercase_	)

      UpperCAmelCase	:     Dict						 =			self.default_image_processor
      UpperCAmelCase	:     str						 =			prepare_img()
      UpperCAmelCase	:     Optional[Any]						 =			image_processor(images=lowercase_					,      return_tensors='pt'	).pixel_values.to(lowercase_	)

      UpperCAmelCase	:     int						 =			torch.tensor([[1, 2]]	)
      UpperCAmelCase	:     Tuple						 =			torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]	).unsqueeze(0	)

      # forward pass
      UpperCAmelCase	:     Dict						 =			model(
          input_ids=input_ids.to(lowercase_	)					,      bbox=bbox.to(lowercase_	)					,      pixel_values=pixel_values.to(lowercase_	)					,      )

      # verify the logits
      UpperCAmelCase	:     Optional[Any]						 =			torch.Size((1, 199, 768)	)
      self.assertEqual(outputs.last_hidden_state.shape					,      lowercase_	)

      UpperCAmelCase	:     List[str]						 =			torch.tensor(
          [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]	).to(lowercase_	)

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




