
'''simple docstring'''




import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  str   =		model.config

    A			:  Tuple   =		DonutSwinConfig(
        image_size=original_config.input_size						,							patch_size=4						,							depths=original_config.encoder_layer						,							num_heads=[4, 8, 16, 32]						,							window_size=original_config.window_size						,							embed_dim=128						,							)
    A			:  Any   =		MBartConfig(
        is_decoder=snake_case__						,							is_encoder_decoder=snake_case__						,							add_cross_attention=snake_case__						,							decoder_layers=original_config.decoder_layer						,							max_position_embeddings=original_config.max_position_embeddings						,							vocab_size=len(
            model.decoder.tokenizer		)						,							scale_embedding=snake_case__						,							add_final_layer_norm=snake_case__						,							)

    return encoder_config, decoder_config







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    if "encoder.model" in name:
        A			:  Dict   =		name.replace('''encoder.model'''						,							'''encoder'''		)
    if "decoder.model" in name:
        A			:  Any   =		name.replace('''decoder.model'''						,							'''decoder'''		)
    if "patch_embed.proj" in name:
        A			:  int   =		name.replace('''patch_embed.proj'''						,							'''embeddings.patch_embeddings.projection'''		)
    if "patch_embed.norm" in name:
        A			:  List[Any]   =		name.replace('''patch_embed.norm'''						,							'''embeddings.norm'''		)
    if name.startswith('''encoder'''		):
        if "layers" in name:
            A			:  Union[str, Any]   =		'''encoder.''' + name
        if "attn.proj" in name:
            A			:  Tuple   =		name.replace('''attn.proj'''						,							'''attention.output.dense'''		)
        if "attn" in name and "mask" not in name:
            A			:  int   =		name.replace('''attn'''						,							'''attention.self'''		)
        if "norm1" in name:
            A			:  Optional[Any]   =		name.replace('''norm1'''						,							'''layernorm_before'''		)
        if "norm2" in name:
            A			:  List[Any]   =		name.replace('''norm2'''						,							'''layernorm_after'''		)
        if "mlp.fc1" in name:
            A			:  List[str]   =		name.replace('''mlp.fc1'''						,							'''intermediate.dense'''		)
        if "mlp.fc2" in name:
            A			:  Optional[int]   =		name.replace('''mlp.fc2'''						,							'''output.dense'''		)

        if name == "encoder.norm.weight":
            A			:  List[Any]   =		'''encoder.layernorm.weight'''
        if name == "encoder.norm.bias":
            A			:  Dict   =		'''encoder.layernorm.bias'''

    return name







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    for key in orig_state_dict.copy().keys():
        A			:  Dict   =		orig_state_dict.pop(snake_case__		)

        if "qkv" in key:
            A			:  Any   =		key.split('''.'''		)
            A			:  Dict   =		int(key_split[3]		)
            A			:  int   =		int(key_split[5]		)
            A			:  Union[str, Any]   =		model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                A			:  List[str]   =		val[:dim, :]
                A			:  Optional[int]   =		val[dim : dim * 2, :]
                A			:  str   =		val[-dim:, :]
            else:
                A			:  List[str]   =		val[:dim]
                A			:  Tuple   =		val[dim : dim * 2]
                A			:  List[str]   =		val[-dim:]
        elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
            # HuggingFace implementation doesn't use attn_mask buffer
            # and model doesn't use final LayerNorms for the encoder
            pass
        else:
            A			:  Any   =		val

    return orig_state_dict







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__=None						,							snake_case__=False		):




    '''simple docstring'''



    A			:  Optional[int]   =		DonutModel.from_pretrained(snake_case__		).eval()

    # load HuggingFace model
    A,    A			:  Union[str, Any]   =		get_configs(snake_case__		)
    A			:  int   =		DonutSwinModel(snake_case__		)
    A			:  Optional[Any]   =		MBartForCausalLM(snake_case__		)
    A			:  Any   =		VisionEncoderDecoderModel(encoder=snake_case__						,							decoder=snake_case__		)
    model.eval()

    A			:  Any   =		original_model.state_dict()
    A			:  Union[str, Any]   =		convert_state_dict(snake_case__						,							snake_case__		)
    model.load_state_dict(snake_case__		)

    # verify results on scanned document
    A			:  List[str]   =		load_dataset('''hf-internal-testing/example-documents'''		)
    A			:  Optional[Any]   =		dataset['''test'''][0]['''image'''].convert('''RGB'''		)

    A			:  Union[str, Any]   =		XLMRobertaTokenizerFast.from_pretrained(snake_case__						,							from_slow=snake_case__		)
    A			:  Optional[Any]   =		DonutImageProcessor(
        do_align_long_axis=original_model.config.align_long_axis						,							size=original_model.config.input_size[::-1]		)
    A			:  int   =		DonutProcessor(snake_case__						,							snake_case__		)
    A			:  Dict   =		processor(snake_case__						,							return_tensors='''pt'''		).pixel_values

    if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
        A			:  int   =		'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
        A			:  Optional[Any]   =		'''When is the coffee break?'''
        A			:  Optional[Any]   =		task_prompt.replace('''{user_input}'''						,							snake_case__		)
    elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
        A			:  Any   =		'''<s_rvlcdip>'''
    elif model_name in [
        "naver-clova-ix/donut-base-finetuned-cord-v1",
        "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
    ]:
        A			:  str   =		'''<s_cord>'''
    elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
        A			:  Optional[Any]   =		'''s_cord-v2>'''
    elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
        A			:  List[str]   =		'''<s_zhtrainticket>'''
    elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
        # use a random prompt
        A			:  List[Any]   =		'''hello world'''
    else:
        raise ValueError('''Model name not supported'''		)
    A			:  Union[str, Any]   =		original_model.decoder.tokenizer(snake_case__						,							add_special_tokens=snake_case__						,							return_tensors='''pt'''		)[
        '''input_ids'''
    ]

    A			:  List[str]   =		original_model.encoder.model.patch_embed(snake_case__		)
    A,    A			:  Dict   =		model.encoder.embeddings(snake_case__		)
    assert torch.allclose(snake_case__						,							snake_case__						,							atol=1E-3		)

    # verify encoder hidden states
    A			:  List[str]   =		original_model.encoder(snake_case__		)
    A			:  Optional[Any]   =		model.encoder(snake_case__		).last_hidden_state
    assert torch.allclose(snake_case__						,							snake_case__						,							atol=1E-2		)

    # verify decoder hidden states
    A			:  Optional[int]   =		original_model(snake_case__						,							snake_case__						,							snake_case__		).logits
    A			:  List[Any]   =		model(snake_case__						,							decoder_input_ids=snake_case__		).logits
    assert torch.allclose(snake_case__						,							snake_case__						,							atol=1E-3		)
    print('''Looks ok!'''		)

    if pytorch_dump_folder_path is not None:
        print(F'Saving model and processor to {pytorch_dump_folder_path}'		)
        model.save_pretrained(snake_case__		)
        processor.save_pretrained(snake_case__		)

    if push_to_hub:
        model.push_to_hub('''nielsr/''' + model_name.split('''/'''		)[-1]						,							commit_message='''Update model'''		)
        processor.push_to_hub('''nielsr/''' + model_name.split('''/'''		)[-1]						,							commit_message='''Update model'''		)


if __name__ == "__main__":
      lowercase     :							List[str]       						=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='naver-clova-ix/donut-base-finetuned-docvqa',
          required=False,
          type=str,
          help='Name of the original model you\'d like to convert.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default=None,
          required=False,
          type=str,
          help='Path to the output PyTorch model directory.',
      )
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Whether or not to push the converted model and processor to the 🤗 hub.',
      )

      lowercase     :							Optional[Any]       						=     parser.parse_args()
      convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase     :							List[str]       						=     logging.get_logger(__name__)

lowercase     :							Optional[int]       						=     {
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''switch_transformers'''
     __magic_name__          =						['''past_key_values''']
     __magic_name__          =						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





     def __init__(				self		,     SCREAMING_SNAKE_CASE=32128		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=64		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=64		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=0.01		,     SCREAMING_SNAKE_CASE="float32"		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=128		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=0.001		,     SCREAMING_SNAKE_CASE=0.001		,     SCREAMING_SNAKE_CASE=1.0		,     SCREAMING_SNAKE_CASE="relu"		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=0		,     SCREAMING_SNAKE_CASE=1		,     **SCREAMING_SNAKE_CASE		,     )					->							List[Any]:






         """simple docstring"""






         A			:  Union[str, Any]   =		vocab_size
         A			:  Any   =		d_model
         A			:  Dict   =		d_kv
         A			:  Optional[Any]   =		d_ff

         A			:  List[Any]   =		num_sparse_encoder_layers

         A			:  List[Any]   =		num_layers
         A			:  Optional[int]   =		(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         A			:  List[str]   =		num_sparse_decoder_layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_encoder_layers > 0:
             A			:  int   =		self.num_layers // self.num_sparse_encoder_layers
         else:
             A			:  int   =		self.num_layers  # HACK: this will create 0 sparse layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_decoder_layers > 0:
             A			:  str   =		self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
             A			:  Dict   =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

         A			:  List[Any]   =		num_heads
         A			:  str   =		num_experts
         A			:  Dict   =		expert_capacity
         A			:  Optional[int]   =		router_bias
         A			:  Any   =		router_jitter_noise
         if router_dtype not in ["float32", "float16", "bfloat16"]:
             raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'      )
         A			:  Dict   =		router_dtype

         A			:  int   =		router_ignore_padding_tokens
         A			:  List[Any]   =		relative_attention_num_buckets
         A			:  Dict   =		relative_attention_max_distance

         A			:  Optional[int]   =		dropout_rate
         A			:  Dict   =		layer_norm_epsilon
         A			:  Optional[int]   =		initializer_factor
         A			:  Union[str, Any]   =		feed_forward_proj
         A			:  Any   =		use_cache
         A			:  Tuple   =		add_router_probs

         A			:  List[str]   =		router_z_loss_coef
         A			:  str   =		router_aux_loss_coef

         A			:  Union[str, Any]   =		self.feed_forward_proj.split('''-'''      )
         A			:  Optional[Any]   =		act_info[-1]
         A			:  Union[str, Any]   =		act_info[0] == '''gated'''

         if len(SCREAMING_SNAKE_CASE      ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE      ) > 2:
             raise ValueError(
                 F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                 '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                 '''\'gated-gelu\' or \'relu\''''      )

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
             A			:  str   =		'''gelu_new'''

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE		,     eos_token_id=SCREAMING_SNAKE_CASE		,     is_encoder_decoder=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE		,     )




