
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 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      List[str]			)     ->						Dict:
				__lowercase								=					model.config

				__lowercase								=					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    ,							)
				__lowercase								=					MBartConfig(
				    is_decoder=A_    ,							is_encoder_decoder=A_    ,							add_cross_attention=A_    ,							decoder_layers=original_config.decoder_layer    ,							max_position_embeddings=original_config.max_position_embeddings    ,							vocab_size=len(
				        model.decoder.tokenizer			)    ,							scale_embedding=A_    ,							add_final_layer_norm=A_    ,							)

				return encoder_config, decoder_config




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Tuple			)     ->						Dict:







				if "encoder.model" in name:
								__lowercase								=					name.replace('encoder.model'    ,							'encoder'			)
				if "decoder.model" in name:
								__lowercase								=					name.replace('decoder.model'    ,							'decoder'			)
				if "patch_embed.proj" in name:
								__lowercase								=					name.replace('patch_embed.proj'    ,							'embeddings.patch_embeddings.projection'			)
				if "patch_embed.norm" in name:
								__lowercase								=					name.replace('patch_embed.norm'    ,							'embeddings.norm'			)
				if name.startswith('encoder'			):
								if "layers" in name:
												__lowercase								=					"encoder." + name
								if "attn.proj" in name:
												__lowercase								=					name.replace('attn.proj'    ,							'attention.output.dense'			)
								if "attn" in name and "mask" not in name:
												__lowercase								=					name.replace('attn'    ,							'attention.self'			)
								if "norm1" in name:
												__lowercase								=					name.replace('norm1'    ,							'layernorm_before'			)
								if "norm2" in name:
												__lowercase								=					name.replace('norm2'    ,							'layernorm_after'			)
								if "mlp.fc1" in name:
												__lowercase								=					name.replace('mlp.fc1'    ,							'intermediate.dense'			)
								if "mlp.fc2" in name:
												__lowercase								=					name.replace('mlp.fc2'    ,							'output.dense'			)

								if name == "encoder.norm.weight":
												__lowercase								=					"encoder.layernorm.weight"
								if name == "encoder.norm.bias":
												__lowercase								=					"encoder.layernorm.bias"

				return name




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Union[str, Any]    ,							SCREAMING_SNAKE_CASE       :      Union[str, Any]			)     ->						Tuple:







				for key in orig_state_dict.copy().keys():
								__lowercase								=					orig_state_dict.pop(A_			)

								if "qkv" in key:
												__lowercase								=					key.split('.'			)
												__lowercase								=					int(key_split[3]			)
												__lowercase								=					int(key_split[5]			)
												__lowercase								=					model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

												if "weight" in key:
																__lowercase								=					val[:dim, :]
																__lowercase								=					val[dim : dim * 2, :]
																__lowercase								=					val[-dim:, :]
												else:
																__lowercase								=					val[:dim]
																__lowercase								=					val[dim : dim * 2]
																__lowercase								=					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:
												__lowercase								=					val

				return orig_state_dict




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      int    ,							SCREAMING_SNAKE_CASE       :      Dict=None    ,							SCREAMING_SNAKE_CASE       :      Union[str, Any]=False			)     ->						List[str]:
				__lowercase								=					DonutModel.from_pretrained(A_			).eval()

				# load HuggingFace model
				__lowercase								=					get_configs(A_			)
				__lowercase								=					DonutSwinModel(A_			)
				__lowercase								=					MBartForCausalLM(A_			)
				__lowercase								=					VisionEncoderDecoderModel(encoder=A_    ,							decoder=A_			)
				model.eval()

				__lowercase								=					original_model.state_dict()
				__lowercase								=					convert_state_dict(A_    ,							A_			)
				model.load_state_dict(A_			)

				# verify results on scanned document
				__lowercase								=					load_dataset('hf-internal-testing/example-documents'			)
				__lowercase								=					dataset["test"][0]["image"].convert('RGB'			)

				__lowercase								=					XLMRobertaTokenizerFast.from_pretrained(A_    ,							from_slow=A_			)
				__lowercase								=					DonutImageProcessor(
				    do_align_long_axis=original_model.config.align_long_axis    ,							size=original_model.config.input_size[::-1]			)
				__lowercase								=					DonutProcessor(A_    ,							A_			)
				__lowercase								=					processor(A_    ,							return_tensors='pt'			).pixel_values

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

				__lowercase								=					original_model.encoder.model.patch_embed(A_			)
				__lowercase								=					model.encoder.embeddings(A_			)
				assert torch.allclose(A_    ,							A_    ,							atol=1E-3			)

				# verify encoder hidden states
				__lowercase								=					original_model.encoder(A_			)
				__lowercase								=					model.encoder(A_			).last_hidden_state
				assert torch.allclose(A_    ,							A_    ,							atol=1E-2			)

				# verify decoder hidden states
				__lowercase								=					original_model(A_    ,							A_    ,							A_			).logits
				__lowercase								=					model(A_    ,							decoder_input_ids=A_			).logits
				assert torch.allclose(A_    ,							A_    ,							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(A_			)
								processor.save_pretrained(A_			)

				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__":
							SCREAMING_SNAKE_CASE__							=				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.""",
							)

							SCREAMING_SNAKE_CASE__							=				parser.parse_args()
							convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""







import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def      lowercase			(				A_       )->      List[Any]:
  '''simple docstring'''







  monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"  ,   set()       )


@pytest.fixture
def      lowercase			(				A_       )->      Tuple:
  '''simple docstring'''







  class 						_A  :







      """simple docstring"""




      def __init__(					self				:    str				, __UpperCAmelCase				:    int):
        a						:		List[Any]      							=			metric_id






  class 						_A  :







      """simple docstring"""




      UpperCAmelCase       :							Union[str, Any]	=       [MetricMock(_a  ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]






      def 	__snake_case							(					self				:    List[str]):
        return self._metrics






  monkeypatch.setattr("datasets.inspect.huggingface_hub"  ,   HfhMock()       )







@pytest.mark.parametrize(
    "func, args"  ,   [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]       )
def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      Any:
  '''simple docstring'''







  if "tmp_path" in args:
    a						:		Union[str, Any]      							=			tuple(arg if arg != "tmp_path" else tmp_path for arg in args       )
  with pytest.warns(A_  ,   match="https://huggingface.co/docs/evaluate"       ):
    func(*A_       )






