






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 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







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


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    seq_length
         _UpperCAmelCase							=    act_dim
         _UpperCAmelCase							=    state_dim
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    max_length
         _UpperCAmelCase							=    is_training


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


         """simple docstring"""
         _UpperCAmelCase							=    floats_tensor((self.batch_size, self.seq_length, self.state_dim)							)
         _UpperCAmelCase							=    floats_tensor((self.batch_size, self.seq_length, self.act_dim)							)
         _UpperCAmelCase							=    floats_tensor((self.batch_size, self.seq_length, 1)							)
         _UpperCAmelCase							=    floats_tensor((self.batch_size, self.seq_length, 1)							)
         _UpperCAmelCase							=    ids_tensor((self.batch_size, self.seq_length)       ,				vocab_size=1000							)
         _UpperCAmelCase							=    random_attention_mask((self.batch_size, self.seq_length)							)

         _UpperCAmelCase							=    self.get_config()

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


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


         """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  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Union[str, Any]       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    DecisionTransformerModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)

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


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         (
             (
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			
         )							=    config_and_inputs
         _UpperCAmelCase							=    {
             """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 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _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  lowerCamelCase       (      self		:			List[Any]							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    DecisionTransformerModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				hidden_size=37							)


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


         """simple docstring"""
         self.config_tester.run_common_tests()


  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  @slow
  def  lowerCamelCase       (      self		:			str							)						->	Dict:


         """simple docstring"""
         for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    DecisionTransformerModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Any							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

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

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

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



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







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


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

         _UpperCAmelCase							=    torch.tensor(
             [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]       ,				device=lowerCamelCase							)

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

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

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

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

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

                _UpperCAmelCase							=    action_pred[0, -1]
                _UpperCAmelCase							=    torch.cat([states, state]       ,				dim=1							)
                _UpperCAmelCase							=    returns_to_go[0, -1] - reward
                _UpperCAmelCase							=    torch.cat([returns_to_go, pred_return.reshape(1       ,				1       ,				1							)]       ,				dim=1							)
                _UpperCAmelCase							=    torch.cat(
                    [timesteps, torch.ones((1, 1)       ,				device=lowerCamelCase       ,				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				(      snake_case							)		->    Optional[int]:
		__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=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  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		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				(      snake_case						,   snake_case							)		->    List[Any]:
		for key in orig_state_dict.copy().keys():
				__lowercase											=     orig_state_dict.pop(snake_case							)

				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				(      snake_case						,   snake_case=None						,   snake_case=False							)		->    Any:
		# load original model
		__lowercase											=     DonutModel.from_pretrained(snake_case							).eval()

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

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

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

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

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

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

		# verify decoder hidden states
		__lowercase											=     original_model(snake_case						,   snake_case						,   snake_case							).logits
		__lowercase											=     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__":
		SCREAMING_SNAKE_CASE_   :      Dict			=   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_   :      Tuple			=   parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




