import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest

@require_torchsde
class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  (DPMSolverSDEScheduler,)
  lowercase_								=  10




  def 						__lowercase(   self  :						Any  ,			**a_  :						List[Any]					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		{
           'num_train_timesteps': 1100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'noise_sampler_seed': 0,
       }

       config.update(**a_					)
       return config




  def 						__lowercase(   self  :						int					)->							str:




       """simple docstring"""
       for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=a_					)




  def 						__lowercase(   self  :						Any					)->							Tuple:




       """simple docstring"""
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]  ,			[0.0002, 0.002, 0.02]					):
            self.check_over_configs(beta_start=a_  ,			beta_end=a_					)




  def 						__lowercase(   self  :						List[Any]					)->							Tuple:




       """simple docstring"""
       for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=a_					)




  def 						__lowercase(   self  :						List[Any]					)->							Union[str, Any]:




       """simple docstring"""
       for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=a_					)




  def 						__lowercase(   self  :						Union[str, Any]					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__					:					str      =		self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		scheduler_class(**a_					)

       scheduler.set_timesteps(self.num_inference_steps					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.dummy_model()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		sample.to(a_					)

       for i, t in enumerate(scheduler.timesteps					):
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		scheduler.scale_model_input(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		model(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		scheduler.step(a_  ,			a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		output.prev_sample

       SCREAMING_SNAKE_CASE__					:					Dict      =		torch.sum(torch.abs(a_					)					)
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.mean(torch.abs(a_					)					)

       if torch_device in ["mps"]:
            assert abs(result_sum.item() - 167.47_8210_4492_1875					) < 1e-2
            assert abs(result_mean.item() - 0.2178_7059_6456_5277					) < 1e-3
       elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 171.59_3521_1181_6406					) < 1e-2
            assert abs(result_mean.item() - 0.2_2342_9068_9229_9652					) < 1e-3
       else:
            assert abs(result_sum.item() - 162.52_3834_2285_1562					) < 1e-2
            assert abs(result_mean.item() - 0.211_6195_7085_1326					) < 1e-3




  def 						__lowercase(   self  :						Optional[int]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_scheduler_config(prediction_type='v_prediction'					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		scheduler_class(**a_					)

       scheduler.set_timesteps(self.num_inference_steps					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.dummy_model()
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		sample.to(a_					)

       for i, t in enumerate(scheduler.timesteps					):
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		scheduler.scale_model_input(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					Any      =		model(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					List[str]      =		scheduler.step(a_  ,			a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		output.prev_sample

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		torch.sum(torch.abs(a_					)					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.mean(torch.abs(a_					)					)

       if torch_device in ["mps"]:
            assert abs(result_sum.item() - 124.77_1492_0043_9453					) < 1e-2
            assert abs(result_mean.item() - 0.1_6226_2890_1481_6284					) < 1e-3
       elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 128.1_6633_6059_5703					) < 1e-2
            assert abs(result_mean.item() - 0.1_6688_3260_0116_7297					) < 1e-3
       else:
            assert abs(result_sum.item() - 119.8_4875_4882_8125					) < 1e-2
            assert abs(result_mean.item() - 0.1560_5306_6253_6621					) < 1e-3




  def 						__lowercase(   self  :						str					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__					:					int      =		scheduler_class(**a_					)

       scheduler.set_timesteps(self.num_inference_steps  ,			device=a_					)

       SCREAMING_SNAKE_CASE__					:					str      =		self.dummy_model()
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.dummy_sample_deter.to(a_					) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		scheduler.scale_model_input(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		model(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					Any      =		scheduler.step(a_  ,			a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		output.prev_sample

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.sum(torch.abs(a_					)					)
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.mean(torch.abs(a_					)					)

       if torch_device in ["mps"]:
            assert abs(result_sum.item() - 167.46_9573_9746_0938					) < 1e-2
            assert abs(result_mean.item() - 0.2_1805_9346_0798_2635					) < 1e-3
       elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 171.59_3536_3769_5312					) < 1e-2
            assert abs(result_mean.item() - 0.2_2342_9083_8241_5771					) < 1e-3
       else:
            assert abs(result_sum.item() - 162.52_3834_2285_1562					) < 1e-2
            assert abs(result_mean.item() - 0.211_6195_7085_1326					) < 1e-3




  def 						__lowercase(   self  :						str					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		scheduler_class(**a_  ,			use_karras_sigmas=a_					)

       scheduler.set_timesteps(self.num_inference_steps  ,			device=a_					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.dummy_model()
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.dummy_sample_deter.to(a_					) * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		sample.to(a_					)

       for t in scheduler.timesteps:
            SCREAMING_SNAKE_CASE__					:					Tuple      =		scheduler.scale_model_input(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					str      =		model(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					List[Any]      =		scheduler.step(a_  ,			a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		output.prev_sample

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.sum(torch.abs(a_					)					)
       SCREAMING_SNAKE_CASE__					:					str      =		torch.mean(torch.abs(a_					)					)

       if torch_device in ["mps"]:
            assert abs(result_sum.item() - 176.66_9741_3574_2188					) < 1e-2
            assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2
       elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 177.63_6535_6445_3125					) < 1e-2
            assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2
       else:
            assert abs(result_sum.item() - 170.3_1352_2338_8672					) < 1e-2
            assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2










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__				(							UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   model.config

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

		return encoder_config, decoder_config






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

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

		return name






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				Optional[int]     ):
		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE__								=   orig_state_dict.pop(UpperCamelCase__     )

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

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

		return orig_state_dict






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				int=None       , UpperCamelCase__:				str=False     ):
		# load original model
		SCREAMING_SNAKE_CASE__								=   DonutModel.from_pretrained(UpperCamelCase__     ).eval()

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

		SCREAMING_SNAKE_CASE__								=   original_model.state_dict()
		SCREAMING_SNAKE_CASE__								=   convert_state_dict(UpperCamelCase__       , UpperCamelCase__     )
		model.load_state_dict(UpperCamelCase__     )

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

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

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

		SCREAMING_SNAKE_CASE__								=   original_model.encoder.model.patch_embed(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   model.encoder.embeddings(UpperCamelCase__     )
		assert torch.allclose(UpperCamelCase__       , UpperCamelCase__       , atol=1e-3     )

		# verify encoder hidden states
		SCREAMING_SNAKE_CASE__								=   original_model.encoder(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   model.encoder(UpperCamelCase__     ).last_hidden_state
		assert torch.allclose(UpperCamelCase__       , UpperCamelCase__       , atol=1e-2     )

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

		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__":
	_lowerCamelCase 					=      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.',
	)

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