
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 					lowerCamelCase		:





     def __init__(self				:   Optional[Any]    ,   _A				:   Union[str, Any]    ,   _A				:   Optional[int]=2    ,   _A				:   Optional[Any]=3    ,   _A				:   Optional[int]=4    ,   _A				:   Optional[Any]=2    ,   _A				:   Optional[Any]=7    ,   _A				:   int=True    ,   _A				:   Tuple=True    ,   _A				:   Any=True    ,   _A				:   Optional[Any]=True    ,   _A				:   int=9_9    ,   _A				:   Any=3_6    ,   _A				:   int=3    ,   _A				:   str=4    ,   _A				:   Optional[Any]=3_7    ,   _A				:   Union[str, Any]="gelu"    ,   _A				:   Any=0.1    ,   _A				:   Optional[int]=0.1    ,   _A				:   Union[str, Any]=5_1_2    ,   _A				:   int=1_6    ,   _A				:   int=2    ,   _A				:   List[str]=0.02    ,   _A				:   Optional[Any]=6    ,   _A				:   Tuple=6    ,   _A				:   Optional[int]=3    ,   _A				:   int=4    ,   _A				:   List[Any]=None    ,   _A				:   int=1_0_0_0    ,   )						->						List[str]:
         snake_case								= parent
         snake_case								= batch_size
         snake_case								= num_channels
         snake_case								= image_size
         snake_case								= patch_size
         snake_case								= text_seq_length
         snake_case								= is_training
         snake_case								= use_input_mask
         snake_case								= use_token_type_ids
         snake_case								= use_labels
         snake_case								= vocab_size
         snake_case								= hidden_size
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= intermediate_size
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= max_position_embeddings
         snake_case								= type_vocab_size
         snake_case								= type_sequence_label_size
         snake_case								= initializer_range
         snake_case								= coordinate_size
         snake_case								= shape_size
         snake_case								= num_labels
         snake_case								= num_choices
         snake_case								= scope
         snake_case								= range_bbox

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





     def 		UpperCAmelCase(self				:   List[str]       )						->						Any:
         snake_case								= ids_tensor([self.batch_size, self.text_seq_length]    ,   self.vocab_size       )

         snake_case								= 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]:
                     snake_case								= bbox[i, j, 3]
                     snake_case								= bbox[i, j, 1]
                     snake_case								= t
                 if bbox[i, j, 2] < bbox[i, j, 0]:
                     snake_case								= bbox[i, j, 2]
                     snake_case								= bbox[i, j, 0]
                     snake_case								= t

         snake_case								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         snake_case								= None
         if self.use_input_mask:
             snake_case								= random_attention_mask([self.batch_size, self.text_seq_length]       )

         snake_case								= None
         if self.use_token_type_ids:
             snake_case								= ids_tensor([self.batch_size, self.text_seq_length]    ,   self.type_vocab_size       )

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

         snake_case								= 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    ,   _A				:   Any    ,   _A				:   List[str]    ,   _A				:   str    ,   _A				:   Dict    ,   _A				:   Dict    ,   _A				:   Dict    ,   _A				:   Tuple    ,   _A				:   Optional[Any]       )						->						Any:
         snake_case								= LayoutLMvaModel(config=_A       )
         model.to(_A       )
         model.eval()

         # text + image
         snake_case								= model(_A    ,   pixel_values=_A       )
         snake_case								= model(
             _A    ,   bbox=_A    ,   pixel_values=_A    ,   attention_mask=_A    ,   token_type_ids=_A       )
         snake_case								= model(_A    ,   bbox=_A    ,   pixel_values=_A    ,   token_type_ids=_A       )
         snake_case								= model(_A    ,   bbox=_A    ,   pixel_values=_A       )

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

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

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





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





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





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   List[Any]    ,   _A				:   Optional[int]    ,   _A				:   Union[str, Any]    ,   _A				:   Optional[int]    ,   _A				:   Union[str, Any]    ,   _A				:   Union[str, Any]    ,   _A				:   Union[str, Any]    ,   _A				:   Dict       )						->						Tuple:
         snake_case								= LayoutLMvaForQuestionAnswering(config=_A       )
         model.to(_A       )
         model.eval()
         snake_case								= model(
             _A    ,   bbox=_A    ,   pixel_values=_A    ,   attention_mask=_A    ,   token_type_ids=_A    ,   start_positions=_A    ,   end_positions=_A    ,   )
         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				:   List[str]       )						->						str:
         snake_case								= self.prepare_config_and_inputs()
         (
             (
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							
         )								= config_and_inputs
         snake_case								= {
             "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 					lowerCamelCase		(  A_							,			A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							str									=		False
     UpperCAmelCase__    :							Any									=		False
     UpperCAmelCase__    :							Tuple									=		False

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





     def 		UpperCAmelCase(self				:   int    ,   _A				:   int    ,   _A				:   str    ,   _A				:   str    ,   _A				:   str    ,   _A				:   str       )						->						Optional[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				:   Union[str, Any]       )						->						int:
         snake_case								= LayoutLMvaModelTester(self       )
         snake_case								= ConfigTester(self    ,   config_class=_A    ,   hidden_size=3_7       )





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

         return inputs_dict





     def 		UpperCAmelCase(self				:   int       )						->						Tuple:
         self.config_tester.run_common_tests()





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						List[str]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A       )





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						List[str]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             snake_case								= type
             self.model_tester.create_and_check_model(*_A       )





     def 		UpperCAmelCase(self				:   Dict       )						->						List[str]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*_A       )





     def 		UpperCAmelCase(self				:   int       )						->						List[Any]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_A       )





     def 		UpperCAmelCase(self				:   List[str]       )						->						Dict:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_A       )





     @slow
     def 		UpperCAmelCase(self				:   str       )						->						List[str]:
         for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case								= LayoutLMvaModel.from_pretrained(_A       )
             self.assertIsNotNone(_A       )



def 						lowercase_      (       )							->							Any:
    """simple docstring"""

    snake_case								= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
    return image







@require_torch
class 					lowerCamelCase		(  unittest.TestCase  ):





     @cached_property
     def 		UpperCAmelCase(self				:   Dict       )						->						Optional[int]:
         return LayoutLMvaImageProcessor(apply_ocr=_A       ) if is_vision_available() else None





     @slow
     def 		UpperCAmelCase(self				:   str       )						->						Optional[int]:
         snake_case								= LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"       ).to(_A       )

         snake_case								= self.default_image_processor
         snake_case								= prepare_img()
         snake_case								= image_processor(images=_A    ,   return_tensors="pt"       ).pixel_values.to(_A       )

         snake_case								= torch.tensor([[1, 2]]       )
         snake_case								= torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]       ).unsqueeze(0       )

         # forward pass
         snake_case								= model(
             input_ids=input_ids.to(_A       )    ,   bbox=bbox.to(_A       )    ,   pixel_values=pixel_values.to(_A       )    ,   )

         # verify the logits
         snake_case								= torch.Size((1, 1_9_9, 7_6_8)       )
         self.assertEqual(outputs.last_hidden_state.shape    ,   _A       )

         snake_case								= torch.tensor(
             [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]       ).to(_A       )

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



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


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



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

    snake_case								= RobertaPreLayerNormConfig.from_pretrained(
        A__			,		architectures=["RobertaPreLayerNormForMaskedLM"]		)

    # convert state_dict
    snake_case								= torch.load(hf_hub_download(repo_id=A__			,		filename="pytorch_model.bin"		)		)
    snake_case								= {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("roberta."		):
            snake_case								= "roberta_prelayernorm." + tensor_key[len("roberta."		) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(".self.LayerNorm.weight"		) or tensor_key.endswith(".self.LayerNorm.bias"		):
            continue

        snake_case								= tensor_value

    snake_case								= RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=A__			,		config=A__			,		state_dict=A__		)
    model.save_pretrained(A__		)

    # convert tokenizer
    snake_case								= AutoTokenizer.from_pretrained(A__		)
    tokenizer.save_pretrained(A__		)


if __name__ == "__main__":
   _A   =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--checkpoint-repo",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   _A   =			parser.parse_args()
   convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

