
import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
				import torch

				from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin




class 				_lowerCamelCase :





					"""simple docstring"""







					def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE="resnet50"						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						)  ->   str:




											"""simple docstring"""







											UpperCamelCase__    :   Tuple					=				parent
											UpperCamelCase__    :   Optional[int]					=				out_indices if out_indices is not None else [4]
											UpperCamelCase__    :   Optional[int]					=				stage_names
											UpperCamelCase__    :   Any					=				out_features
											UpperCamelCase__    :   str					=				backbone
											UpperCamelCase__    :   str					=				batch_size
											UpperCamelCase__    :   str					=				image_size
											UpperCamelCase__    :   List[Any]					=				num_channels
											UpperCamelCase__    :   Any					=				use_pretrained_backbone
											UpperCamelCase__    :   Optional[Any]					=				is_training






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




											"""simple docstring"""







											UpperCamelCase__    :   List[str]					=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
											UpperCamelCase__    :   Optional[Any]					=				self.get_config()

											return config, pixel_values






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




											"""simple docstring"""







											return TimmBackboneConfig(
											    image_size=self.image_size						,						num_channels=self.num_channels						,						out_features=self.out_features						,						out_indices=self.out_indices						,						stage_names=self.stage_names						,						use_pretrained_backbone=self.use_pretrained_backbone						,						backbone=self.backbone						,						)






					def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   int:




											"""simple docstring"""







											UpperCamelCase__    :   Optional[int]					=				TimmBackbone(config=__SCREAMING_SNAKE_CASE  )
											model.to(__SCREAMING_SNAKE_CASE  )
											model.eval()
											with torch.no_grad():
																	UpperCamelCase__    :   Tuple					=				model(__SCREAMING_SNAKE_CASE  )
											self.parent.assertEqual(
											    result.feature_map[-1].shape						,						(self.batch_size, model.channels[-1], 1_4, 1_4)						,						)






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




											"""simple docstring"""







											UpperCamelCase__    :   Optional[Any]					=				self.prepare_config_and_inputs()
											UpperCamelCase__    :   Any					=				config_and_inputs
											UpperCamelCase__    :   Optional[int]					=				{'''pixel_values''': pixel_values}
											return config, inputs_dict




@require_torch
@require_timm
class 				_lowerCamelCase (      UpperCamelCase_	,							UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase ):





					"""simple docstring"""







					SCREAMING_SNAKE_CASE_		   =  (TimmBackbone,) if is_torch_available() else ()
					SCREAMING_SNAKE_CASE_		   =  {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
					SCREAMING_SNAKE_CASE_		   =  False
					SCREAMING_SNAKE_CASE_		   =  False
					SCREAMING_SNAKE_CASE_		   =  False
					SCREAMING_SNAKE_CASE_		   =  False






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




											"""simple docstring"""







											UpperCamelCase__    :   List[str]					=				TimmBackboneModelTester(self  )
											UpperCamelCase__    :   List[str]					=				ConfigTester(self						,						config_class=__SCREAMING_SNAKE_CASE						,						has_text_modality=__SCREAMING_SNAKE_CASE  )






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




											"""simple docstring"""







											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




											"""simple docstring"""







											UpperCamelCase__    :   Union[str, Any]					=				'''resnet18'''
											UpperCamelCase__    :   Tuple					=				'''microsoft/resnet-18'''

											UpperCamelCase__    :   str					=				AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE						,						use_timm_backbone=__SCREAMING_SNAKE_CASE  )
											UpperCamelCase__    :   Union[str, Any]					=				AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE  )

											self.assertEqual(len(timm_model.out_features  )						,						len(transformers_model.out_features  )  )
											self.assertEqual(len(timm_model.stage_names  )						,						len(transformers_model.stage_names  )  )
											self.assertEqual(timm_model.channels						,						transformers_model.channels  )
											# Out indices are set to the last layer by default. For timm models, we don't know
											# the number of layers in advance, so we set it to (-1,), whereas for transformers
											# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
											self.assertEqual(timm_model.out_indices						,						(-1,)  )
											self.assertEqual(transformers_model.out_indices						,						[len(timm_model.stage_names  ) - 1]  )

											UpperCamelCase__    :   Optional[int]					=				AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE						,						use_timm_backbone=__SCREAMING_SNAKE_CASE						,						out_indices=[1, 2, 3]  )
											UpperCamelCase__    :   str					=				AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE						,						out_indices=[1, 2, 3]  )

											self.assertEqual(timm_model.out_indices						,						transformers_model.out_indices  )
											self.assertEqual(len(timm_model.out_features  )						,						len(transformers_model.out_features  )  )
											self.assertEqual(timm_model.channels						,						transformers_model.channels  )






					@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




											"""simple docstring"""







											pass






					@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




											"""simple docstring"""







											pass






					@unittest.skip('''TimmBackbone initialization is managed on the timm side'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




											"""simple docstring"""







											pass






					@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




											"""simple docstring"""







											pass






					@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




											"""simple docstring"""







											pass






					@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




											"""simple docstring"""







											pass






					@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




											"""simple docstring"""







											pass






					@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




											"""simple docstring"""







											pass






					@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




											"""simple docstring"""







											pass






					@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




											"""simple docstring"""







											pass






					@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




											"""simple docstring"""







											pass






					@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




											"""simple docstring"""







											pass






					@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




											"""simple docstring"""







											pass






					@unittest.skip('''Safetensors is not supported by timm.'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




											"""simple docstring"""







											pass






					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




											"""simple docstring"""







											pass






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




											"""simple docstring"""







											UpperCamelCase__    :   Optional[Any]					=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	UpperCamelCase__    :   Optional[int]					=				model_class(__SCREAMING_SNAKE_CASE  )
																	UpperCamelCase__    :   Optional[Any]					=				inspect.signature(model.forward  )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	UpperCamelCase__    :   Union[str, Any]					=				[*signature.parameters.keys()]

																	UpperCamelCase__    :   Tuple					=				['''pixel_values''']
																	self.assertListEqual(arg_names[:1]						,						__SCREAMING_SNAKE_CASE  )






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




											"""simple docstring"""







											UpperCamelCase__    :   str					=				self.model_tester.prepare_config_and_inputs_for_common()
											UpperCamelCase__    :   int					=				True
											UpperCamelCase__    :   List[Any]					=				self.has_attentions

											# no need to test all models as different heads yield the same functionality
											UpperCamelCase__    :   List[str]					=				self.all_model_classes[0]
											UpperCamelCase__    :   Any					=				model_class(__SCREAMING_SNAKE_CASE  )
											model.to(__SCREAMING_SNAKE_CASE  )

											UpperCamelCase__    :   Optional[Any]					=				self._prepare_for_class(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
											UpperCamelCase__    :   Union[str, Any]					=				model(**__SCREAMING_SNAKE_CASE  )
											UpperCamelCase__    :   List[str]					=				outputs[0][-1]

											# Encoder-/Decoder-only models
											UpperCamelCase__    :   Dict					=				outputs.hidden_states[0]
											hidden_states.retain_grad()

											if self.has_attentions:
																	UpperCamelCase__    :   Optional[int]					=				outputs.attentions[0]
																	attentions.retain_grad()

											output.flatten()[0].backward(retain_graph=__SCREAMING_SNAKE_CASE  )

											self.assertIsNotNone(hidden_states.grad  )

											if self.has_attentions:
																	self.assertIsNotNone(attentions.grad  )






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




											"""simple docstring"""







											UpperCamelCase__    :   Optional[Any]					=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	UpperCamelCase__    :   List[str]					=				model_class(__SCREAMING_SNAKE_CASE  )
																	model.to(__SCREAMING_SNAKE_CASE  )
																	model.eval()
																	UpperCamelCase__    :   Tuple					=				model(**__SCREAMING_SNAKE_CASE  )

																	self.assertEqual(len(result.feature_maps  )						,						len(config.out_indices  )  )
																	self.assertEqual(len(model.channels  )						,						len(config.out_indices  )  )

																	# Check output of last stage is taken if out_features=None, out_indices=None
																	UpperCamelCase__    :   Tuple					=				copy.deepcopy(__SCREAMING_SNAKE_CASE  )
																	UpperCamelCase__    :   str					=				None
																	UpperCamelCase__    :   Any					=				model_class(__SCREAMING_SNAKE_CASE  )
																	model.to(__SCREAMING_SNAKE_CASE  )
																	model.eval()
																	UpperCamelCase__    :   Tuple					=				model(**__SCREAMING_SNAKE_CASE  )

																	self.assertEqual(len(result.feature_maps  )						,						1  )
																	self.assertEqual(len(model.channels  )						,						1  )

																	# Check backbone can be initialized with fresh weights
																	UpperCamelCase__    :   str					=				copy.deepcopy(__SCREAMING_SNAKE_CASE  )
																	UpperCamelCase__    :   int					=				False
																	UpperCamelCase__    :   List[Any]					=				model_class(__SCREAMING_SNAKE_CASE  )
																	model.to(__SCREAMING_SNAKE_CASE  )
																	model.eval()
																	UpperCamelCase__    :   List[Any]					=				model(**__SCREAMING_SNAKE_CASE  )




import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


lowerCamelCase      =logging.get_logger(__name__)
logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      if "xprophetnet" in prophetnet_checkpoint_path:
            UpperCamelCase__    :   int					=				XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__		)
            UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				XLMProphetNetForConditionalGeneration.from_pretrained(
                UpperCamelCase__ ,		output_loading_info=UpperCamelCase__		)
      else:
            UpperCamelCase__    :   Tuple					=				ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__		)
            UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				ProphetNetForConditionalGeneration.from_pretrained(
                UpperCamelCase__ ,		output_loading_info=UpperCamelCase__		)

      UpperCamelCase__    :   Optional[Any]					=				['''key_proj''', '''value_proj''', '''query_proj''']

      UpperCamelCase__    :   Tuple					=				{
          '''self_attn''': '''ngram_self_attn''',
          '''cross_attn''': '''encoder_attn''',
          '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
          '''feed_forward_layer_norm''': '''final_layer_norm''',
          '''feed_forward''': '''''',
          '''intermediate''': '''fc1''',
          '''output''': '''fc2''',
          '''key_proj''': '''k_proj''',
          '''query_proj''': '''q_proj''',
          '''value_proj''': '''v_proj''',
          '''word_embeddings''': '''embed_tokens''',
          '''embeddings_layer_norm''': '''emb_layer_norm''',
          '''relative_pos_embeddings''': '''relative_linear''',
          '''ngram_embeddings''': '''ngram_input_embed''',
          '''position_embeddings''': '''embed_positions''',
      }

      for key in loading_info["missing_keys"]:
            UpperCamelCase__    :   Tuple					=				key.split('''.'''		)

            if attributes[0] == "lm_head":
                  UpperCamelCase__    :   Union[str, Any]					=				prophet
                  UpperCamelCase__    :   int					=				prophet_old
            else:
                  UpperCamelCase__    :   int					=				prophet.prophetnet
                  UpperCamelCase__    :   str					=				prophet_old.model

            UpperCamelCase__    :   Tuple					=				False
            for attribute in attributes:
                  if attribute in mapping:
                        UpperCamelCase__    :   List[str]					=				mapping[attribute]
                        if not hasattr(UpperCamelCase__ ,		UpperCamelCase__		) and len(UpperCamelCase__		) > 0:
                              UpperCamelCase__    :   Tuple					=				attribute
                  elif hasattr(UpperCamelCase__ ,		UpperCamelCase__		):
                        UpperCamelCase__    :   Tuple					=				attribute

                  if attribute == "weight":
                        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                        UpperCamelCase__    :   Dict					=				old_model.weight
                        logger.info(f'''{attribute} is initialized.'''		)
                        UpperCamelCase__    :   Optional[Any]					=				True
                        break
                  elif attribute == "bias":
                        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                        UpperCamelCase__    :   Any					=				old_model.bias
                        logger.info(f'''{attribute} is initialized'''		)
                        UpperCamelCase__    :   Any					=				True
                        break
                  elif attribute in special_keys and hasattr(UpperCamelCase__ ,		'''in_proj_weight'''		):
                        UpperCamelCase__    :   Optional[int]					=				old_model.in_proj_weight.shape[0] // 3
                        UpperCamelCase__    :   Optional[Any]					=				getattr(UpperCamelCase__ ,		UpperCamelCase__		)
                        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                        if attribute == "query_proj":
                              UpperCamelCase__    :   Optional[Any]					=				nn.Parameter(old_model.in_proj_weight[:embed_dim, :]		)
                              UpperCamelCase__    :   str					=				nn.Parameter(old_model.in_proj_bias[:embed_dim]		)

                        elif attribute == "key_proj":
                              UpperCamelCase__    :   Union[str, Any]					=				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
                              UpperCamelCase__    :   Optional[int]					=				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
                        elif attribute == "value_proj":
                              UpperCamelCase__    :   List[str]					=				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
                              UpperCamelCase__    :   Tuple					=				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
                        UpperCamelCase__    :   Tuple					=				True
                        break
                  elif attribute == "position_embeddings":
                        assert (
                            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                        ), "Hidden size has to match"
                        assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
                        UpperCamelCase__    :   Optional[Any]					=				nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]		)
                        UpperCamelCase__    :   List[Any]					=				True
                        break

                  if attribute.isdigit():
                        UpperCamelCase__    :   int					=				model[int(UpperCamelCase__		)]
                        UpperCamelCase__    :   int					=				old_model[int(UpperCamelCase__		)]
                  else:
                        UpperCamelCase__    :   str					=				getattr(UpperCamelCase__ ,		UpperCamelCase__		)

                        if old_attribute == "":
                              UpperCamelCase__    :   List[str]					=				old_model
                        else:
                              if not hasattr(UpperCamelCase__ ,		UpperCamelCase__		):
                                    raise ValueError(f'''{old_model} does not have {old_attribute}'''		)
                              UpperCamelCase__    :   List[Any]					=				getattr(UpperCamelCase__ ,		UpperCamelCase__		)

            if not is_key_init:
                  raise ValueError(f'''{key} was not correctly initialized!'''		)

      print(f'''Saving model to {pytorch_dump_folder_path}'''		)
      prophet.save_pretrained(UpperCamelCase__		)


if __name__ == "__main__":
    lowerCamelCase      =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    lowerCamelCase      =parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


