





import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 DecisionTransformerModel
	from transformers.models.decision_transformer.modeling_decision_transformer import (
	    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
	)




class 	UpperCamelCase_							:


							def __init__(				self     :int				,					__A     :Any				,					__A     :int=13				,					__A     :Optional[Any]=7				,					__A     :Any=6				,					__A     :List[Any]=17				,					__A     :List[str]=23				,					__A     :List[str]=11				,					__A     :Optional[Any]=True				,					) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   parent
									SCREAMING_SNAKE_CASE__								=   batch_size
									SCREAMING_SNAKE_CASE__								=   seq_length
									SCREAMING_SNAKE_CASE__								=   act_dim
									SCREAMING_SNAKE_CASE__								=   state_dim
									SCREAMING_SNAKE_CASE__								=   hidden_size
									SCREAMING_SNAKE_CASE__								=   max_length
									SCREAMING_SNAKE_CASE__								=   is_training


							def   _snake_case	(				self     :int		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   floats_tensor((self.batch_size, self.seq_length, self.state_dim)		)
									SCREAMING_SNAKE_CASE__								=   floats_tensor((self.batch_size, self.seq_length, self.act_dim)		)
									SCREAMING_SNAKE_CASE__								=   floats_tensor((self.batch_size, self.seq_length, 1)		)
									SCREAMING_SNAKE_CASE__								=   floats_tensor((self.batch_size, self.seq_length, 1)		)
									SCREAMING_SNAKE_CASE__								=   ids_tensor((self.batch_size, self.seq_length)				,					vocab_size=1000		)
									SCREAMING_SNAKE_CASE__								=   random_attention_mask((self.batch_size, self.seq_length)		)

									SCREAMING_SNAKE_CASE__								=   self.get_config()

									return (
									    config,
									    states,
									    actions,
									    rewards,
									    returns_to_go,
									    timesteps,
									    attention_mask,
									)


							def   _snake_case	(				self     :Optional[Any]		) -> Tuple:



									"""simple docstring"""



									return DecisionTransformerConfig(
									    batch_size=self.batch_size				,					seq_length=self.seq_length				,					act_dim=self.act_dim				,					state_dim=self.state_dim				,					hidden_size=self.hidden_size				,					max_length=self.max_length				,					)


							def   _snake_case	(				self     :List[Any]				,					__A     :Union[str, Any]				,					__A     :str				,					__A     :Dict				,					__A     :Union[str, Any]				,					__A     :Tuple				,					__A     :Tuple				,					__A     :Dict				,					) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   DecisionTransformerModel(config=__A		)
									model.to(__A		)
									model.eval()
									SCREAMING_SNAKE_CASE__								=   model(__A				,					__A				,					__A				,					__A				,					__A				,					__A		)

									self.parent.assertEqual(result.state_preds.shape				,					states.shape		)
									self.parent.assertEqual(result.action_preds.shape				,					actions.shape		)
									self.parent.assertEqual(result.return_preds.shape				,					returns_to_go.shape		)
									self.parent.assertEqual(
									    result.last_hidden_state.shape				,					(self.batch_size, self.seq_length * 3, self.hidden_size)		)  # seq length *3 as there are 3 modelities: states, returns and actions


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    
									)								=   config_and_inputs
									SCREAMING_SNAKE_CASE__								=   {
									    """states""": states,
									    """actions""": actions,
									    """rewards""": rewards,
									    """returns_to_go""": returns_to_go,
									    """timesteps""": timesteps,
									    """attention_mask""": attention_mask,
									}
									return config, inputs_dict




@require_torch
class 	UpperCamelCase_							(  UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	unittest.TestCase     ):
							lowerCamelCase_							     =       (DecisionTransformerModel,) if is_torch_available() else ()
							lowerCamelCase_							     =       ()
							lowerCamelCase_							     =       {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

							# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
							lowerCamelCase_							     =       False

							# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False


							def   _snake_case	(				self     :List[Any]		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   DecisionTransformerModelTester(self		)
									SCREAMING_SNAKE_CASE__								=   ConfigTester(self				,					config_class=__A				,					hidden_size=37		)


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



									"""simple docstring"""



									self.config_tester.run_common_tests()


							def   _snake_case	(				self     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__A		)


							@slow
							def   _snake_case	(				self     :str		) -> Union[str, Any]:



									"""simple docstring"""



									for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											SCREAMING_SNAKE_CASE__								=   DecisionTransformerModel.from_pretrained(__A		)
											self.assertIsNotNone(__A		)


							def   _snake_case	(				self     :str		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs_for_common()

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

											SCREAMING_SNAKE_CASE__								=   [
											    """states""",
											    """actions""",
											    """rewards""",
											    """returns_to_go""",
											    """timesteps""",
											    """attention_mask""",
											]

											self.assertListEqual(arg_names[: len(__A		)]				,					__A		)




@require_torch
class 	UpperCamelCase_							(  unittest.TestCase     ):


							@slow
							def   _snake_case	(				self     :Union[str, Any]		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   2  # number of steps of autoregressive prediction we will perform
									SCREAMING_SNAKE_CASE__								=   10  # defined by the RL environment, may be normalized
									SCREAMING_SNAKE_CASE__								=   DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""		)
									SCREAMING_SNAKE_CASE__								=   model.to(__A		)
									SCREAMING_SNAKE_CASE__								=   model.config
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   torch.randn(1				,					1				,					config.state_dim		).to(device=__A				,					dtype=torch.floataa		)  # env.reset()

									SCREAMING_SNAKE_CASE__								=   torch.tensor(
									    [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]				,					device=__A		)

									SCREAMING_SNAKE_CASE__								=   torch.tensor(__A				,					device=__A				,					dtype=torch.floataa		).reshape(1				,					1				,					1		)
									SCREAMING_SNAKE_CASE__								=   state
									SCREAMING_SNAKE_CASE__								=   torch.zeros(1				,					0				,					config.act_dim				,					device=__A				,					dtype=torch.floataa		)
									SCREAMING_SNAKE_CASE__								=   torch.zeros(1				,					0				,					device=__A				,					dtype=torch.floataa		)
									SCREAMING_SNAKE_CASE__								=   torch.tensor(0				,					device=__A				,					dtype=torch.long		).reshape(1				,					1		)

									for step in range(__A		):
											SCREAMING_SNAKE_CASE__								=   torch.cat([actions, torch.zeros(1				,					1				,					config.act_dim				,					device=__A		)]				,					dim=1		)
											SCREAMING_SNAKE_CASE__								=   torch.cat([rewards, torch.zeros(1				,					1				,					device=__A		)]				,					dim=1		)

											SCREAMING_SNAKE_CASE__								=   torch.ones(1				,					states.shape[1]		).to(dtype=torch.long				,					device=states.device		)

											with torch.no_grad():
													SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   model(
													    states=__A				,					actions=__A				,					rewards=__A				,					returns_to_go=__A				,					timesteps=__A				,					attention_mask=__A				,					return_dict=__A				,					)

											self.assertEqual(action_pred.shape				,					actions.shape		)
											self.assertTrue(torch.allclose(action_pred[0, -1]				,					expected_outputs[step]				,					atol=1E-4		)		)
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   (  # env.step(action)
											    torch.randn(1				,					1				,					config.state_dim		).to(device=__A				,					dtype=torch.floataa		),
											    1.0,
											    False,
											    {},
											)

											SCREAMING_SNAKE_CASE__								=   action_pred[0, -1]
											SCREAMING_SNAKE_CASE__								=   torch.cat([states, state]				,					dim=1		)
											SCREAMING_SNAKE_CASE__								=   returns_to_go[0, -1] - reward
											SCREAMING_SNAKE_CASE__								=   torch.cat([returns_to_go, pred_return.reshape(1				,					1				,					1		)]				,					dim=1		)
											SCREAMING_SNAKE_CASE__								=   torch.cat(
											    [timesteps, torch.ones((1, 1)				,					device=__A				,					dtype=torch.long		) * (step + 1)]				,					dim=1		)






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)