


'''simple docstring'''

import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
       import torch
       from torch import nn

       from transformers import DonutSwinModel
       from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST







class 			UpperCAmelCase			:

 def __init__(			self			:						int,       a_			:						Union[str, Any],       a_			:						Dict=13,       a_			:						List[str]=32,       a_			:						int=2,       a_			:						List[str]=3,       a_			:						List[Any]=16,       a_			:						List[Any]=[1, 2, 1],       a_			:						Dict=[2, 2, 4],       a_			:						Dict=2,       a_			:						List[str]=2.0,       a_			:						List[Any]=True,       a_			:						Dict=0.0,       a_			:						Tuple=0.0,       a_			:						str=0.1,       a_			:						Any="gelu",       a_			:						Union[str, Any]=False,       a_			:						List[str]=True,       a_			:						Optional[Any]=0.02,       a_			:						Optional[int]=1e-5,       a_			:						Union[str, Any]=True,       a_			:						List[Any]=None,       a_			:						Optional[int]=True,       a_			:						Tuple=10,       a_			:						Dict=8,       ):





    """simple docstring"""



    UpperCamelCase__			    =    parent
    UpperCamelCase__			    =    batch_size
    UpperCamelCase__			    =    image_size
    UpperCamelCase__			    =    patch_size
    UpperCamelCase__			    =    num_channels
    UpperCamelCase__			    =    embed_dim
    UpperCamelCase__			    =    depths
    UpperCamelCase__			    =    num_heads
    UpperCamelCase__			    =    window_size
    UpperCamelCase__			    =    mlp_ratio
    UpperCamelCase__			    =    qkv_bias
    UpperCamelCase__			    =    hidden_dropout_prob
    UpperCamelCase__			    =    attention_probs_dropout_prob
    UpperCamelCase__			    =    drop_path_rate
    UpperCamelCase__			    =    hidden_act
    UpperCamelCase__			    =    use_absolute_embeddings
    UpperCamelCase__			    =    patch_norm
    UpperCamelCase__			    =    layer_norm_eps
    UpperCamelCase__			    =    initializer_range
    UpperCamelCase__			    =    is_training
    UpperCamelCase__			    =    scope
    UpperCamelCase__			    =    use_labels
    UpperCamelCase__			    =    type_sequence_label_size
    UpperCamelCase__			    =    encoder_stride

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



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

    UpperCamelCase__			    =    None
    if self.use_labels:
       UpperCamelCase__			    =    ids_tensor([self.batch_size],       self.type_sequence_label_size       )

    UpperCamelCase__			    =    self.get_config()

    return config, pixel_values, labels

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    return DonutSwinConfig(
        image_size=self.image_size,       patch_size=self.patch_size,       num_channels=self.num_channels,       embed_dim=self.embed_dim,       depths=self.depths,       num_heads=self.num_heads,       window_size=self.window_size,       mlp_ratio=self.mlp_ratio,       qkv_bias=self.qkv_bias,       hidden_dropout_prob=self.hidden_dropout_prob,       attention_probs_dropout_prob=self.attention_probs_dropout_prob,       drop_path_rate=self.drop_path_rate,       hidden_act=self.hidden_act,       use_absolute_embeddings=self.use_absolute_embeddings,       path_norm=self.patch_norm,       layer_norm_eps=self.layer_norm_eps,       initializer_range=self.initializer_range,       encoder_stride=self.encoder_stride,       )

 def 		lowercase_    (			self			:						Tuple,       a_			:						Tuple,       a_			:						Dict,       a_			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    DonutSwinModel(config=a_       )
    model.to(a_       )
    model.eval()
    UpperCamelCase__			    =    model(a_       )

    UpperCamelCase__			    =    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
    UpperCamelCase__			    =    int(config.embed_dim * 2 ** (len(config.depths       ) - 1)       )

    self.parent.assertEqual(result.last_hidden_state.shape,       (self.batch_size, expected_seq_len, expected_dim)       )

 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.prepare_config_and_inputs()
    (
        (
        UpperCamelCase__
    )    ,		(
        UpperCamelCase__
    )    ,		(
        UpperCamelCase__
    )    ,		
    )			    =    config_and_inputs
    UpperCamelCase__			    =    {"pixel_values": pixel_values}
    return config, inputs_dict







@require_torch
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       List[str]            =       (DonutSwinModel,) if is_torch_available() else ()
 _lowerCamelCase			:       Tuple            =       {'feature-extraction': DonutSwinModel} if is_torch_available() else {}
 _lowerCamelCase			:       str            =       True

 _lowerCamelCase			:       Optional[int]            =       False
 _lowerCamelCase			:       Tuple            =       False
 _lowerCamelCase			:       List[Any]            =       False

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    DonutSwinModelTester(self       )
    UpperCamelCase__			    =    ConfigTester(self,       config_class=a_,       embed_dim=37       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    return

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a_       )

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    pass

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    model_class(a_       )
       self.assertIsInstance(model.get_input_embeddings(),       (nn.Module)       )
       UpperCamelCase__			    =    model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(a_,       nn.Linear       )       )

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    model_class(a_       )
       UpperCamelCase__			    =    inspect.signature(model.forward       )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       UpperCamelCase__			    =    [*signature.parameters.keys()]

       UpperCamelCase__			    =    ["pixel_values"]
       self.assertListEqual(arg_names[:1],       a_       )

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase__			    =    True

    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    False
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )
       UpperCamelCase__			    =    outputs.attentions
       UpperCamelCase__			    =    len(self.model_tester.depths       )
       self.assertEqual(len(a_       ),       a_       )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    config.window_size**2
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )
       UpperCamelCase__			    =    outputs.attentions
       self.assertEqual(len(a_       ),       a_       )

       self.assertListEqual(
           list(attentions[0].shape[-3:]       ),       [self.model_tester.num_heads[0], window_size_squared, window_size_squared],       )
       UpperCamelCase__			    =    len(a_       )

       # Check attention is always last and order is fine
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )

       if hasattr(self.model_tester,       "num_hidden_states_types"       ):
          UpperCamelCase__			    =    self.model_tester.num_hidden_states_types
       else:
          # also another +1 for reshaped_hidden_states
          UpperCamelCase__			    =    2
       self.assertEqual(out_len + added_hidden_states,       len(a_       )       )

       UpperCamelCase__			    =    outputs.attentions

       self.assertEqual(len(a_       ),       a_       )

       self.assertListEqual(
           list(self_attentions[0].shape[-3:]       ),       [self.model_tester.num_heads[0], window_size_squared, window_size_squared],       )

 def 		lowercase_    (			self			:						Any,       a_			:						Dict,       a_			:						Union[str, Any],       a_			:						Optional[Any],       a_			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    model_class(a_       )
    model.to(a_       )
    model.eval()

    with torch.no_grad():
       UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )

    UpperCamelCase__			    =    outputs.hidden_states

    UpperCamelCase__			    =    getattr(
        self.model_tester,       "expected_num_hidden_layers",       len(self.model_tester.depths       ) + 1       )
    self.assertEqual(len(a_       ),       a_       )

    # DonutSwin has a different seq_length
    UpperCamelCase__			    =    (
        config.patch_size
        if isinstance(config.patch_size,       collections.abc.Iterable       )
        else (config.patch_size, config.patch_size)
    )

    UpperCamelCase__			    =    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

    self.assertListEqual(
        list(hidden_states[0].shape[-2:]       ),       [num_patches, self.model_tester.embed_dim],       )

    UpperCamelCase__			    =    outputs.reshaped_hidden_states
    self.assertEqual(len(a_       ),       a_       )

    UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    reshaped_hidden_states[0].shape
    UpperCamelCase__			    =    (
        reshaped_hidden_states[0].view(a_,       a_,       height * width       ).permute(0,       2,       1       )
    )
    self.assertListEqual(
        list(reshaped_hidden_states.shape[-2:]       ),       [num_patches, self.model_tester.embed_dim],       )

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

    UpperCamelCase__			    =    (
        self.model_tester.image_size
        if isinstance(self.model_tester.image_size,       collections.abc.Iterable       )
        else (self.model_tester.image_size, self.model_tester.image_size)
    )

    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    True
       self.check_hidden_states_output(a_,       a_,       a_,       a_       )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       UpperCamelCase__			    =    True

       self.check_hidden_states_output(a_,       a_,       a_,       a_       )

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase__			    =    3

    UpperCamelCase__			    =    (
        self.model_tester.image_size
        if isinstance(self.model_tester.image_size,       collections.abc.Iterable       )
        else (self.model_tester.image_size, self.model_tester.image_size)
    )
    UpperCamelCase__			    =    (
        config.patch_size
        if isinstance(config.patch_size,       collections.abc.Iterable       )
        else (config.patch_size, config.patch_size)
    )

    UpperCamelCase__			    =    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
    UpperCamelCase__			    =    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    True
       self.check_hidden_states_output(a_,       a_,       a_,       (padded_height, padded_width)       )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       UpperCamelCase__			    =    True
       self.check_hidden_states_output(a_,       a_,       a_,       (padded_height, padded_width)       )

 @slow
 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCamelCase__			    =    DonutSwinModel.from_pretrained(a_       )
       self.assertIsNotNone(a_       )

 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

    UpperCamelCase__			    =    _config_zero_init(a_       )
    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    model_class(config=a_       )
       for name, param in model.named_parameters():
          if "embeddings" not in name and param.requires_grad:
             self.assertIn(
                 ((param.data.mean() * 1e9).round() / 1e9).item(),       [0.0, 1.0],       msg=f'Parameter {name} of model {model_class} seems not properly initialized',       )

'''simple docstring'''






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		if "model" in orig_key:
				lowerCAmelCase_   :       Union[str, Any]    							=orig_key.replace('''model.'''  ,					''''''  )
		if "norm1" in orig_key:
				lowerCAmelCase_   :       List[Any]    							=orig_key.replace('''norm1'''  ,					'''attention.output.LayerNorm'''  )
		if "norm2" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm2'''  ,					'''output.LayerNorm'''  )
		if "norm" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm'''  ,					'''LayerNorm'''  )
		if "transformer" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.split('''.'''  )[0].split('''_'''  )[-1]
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace(f'transformer_{layer_num}'  ,					f'encoder.layer.{layer_num}'  )
		if "mha.attn" in orig_key:
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace('''mha.attn'''  ,					'''attention.self'''  )
		if "mha" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mha'''  ,					'''attention'''  )
		if "W_q" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''W_q'''  ,					'''self.query'''  )
		if "W_k" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''W_k'''  ,					'''self.key'''  )
		if "W_v" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''W_v'''  ,					'''self.value'''  )
		if "ff.0" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''ff.0'''  ,					'''intermediate.dense'''  )
		if "ff.2" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.replace('''ff.2'''  ,					'''output.dense'''  )
		if "ff" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''ff'''  ,					'''output.dense'''  )
		if "mlm_class" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mlm.mlm_class'''  ,					'''cls.predictions.decoder'''  )
		if "mlm" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''mlm'''  ,					'''cls.predictions.transform'''  )
		if "backbone.backbone.encoders" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''backbone.backbone.encoders'''  ,					'''encoder.layer'''  )
		if "cls" not in orig_key:
				lowerCAmelCase_   :       List[Any]    							='''mra.''' + orig_key

		return orig_key







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_   :       Tuple    							=orig_state_dict.pop(_SCREAMING_SNAKE_CASE  )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowerCAmelCase_   :       Dict    							=val

		lowerCAmelCase_   :       Union[str, Any]    							=orig_state_dict['''cls.predictions.decoder.bias''']
		lowerCAmelCase_   :       int    							=torch.arange(_SCREAMING_SNAKE_CASE  ).expand((1, -1)  ) + 2

		return orig_state_dict







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Tuple    							=torch.load(_SCREAMING_SNAKE_CASE  ,					map_location='''cpu'''  )['''model_state_dict''']
		lowerCAmelCase_   :       Optional[int]    							=MraConfig.from_json_file(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Optional[Any]    							=MraForMaskedLM(_SCREAMING_SNAKE_CASE  )

		lowerCAmelCase_   :       str    							=convert_checkpoint_helper(config.max_position_embeddings  ,					_SCREAMING_SNAKE_CASE  )

		print(model.load_state_dict(_SCREAMING_SNAKE_CASE  )  )
		model.eval()
		model.save_pretrained(_SCREAMING_SNAKE_CASE  )

		print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'  )


if __name__ == "__main__":
							__lowercase	=    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The json file for Mra model config.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							__lowercase	=    parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






