
import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCamelCase      ="src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase      =direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCamelCase      =transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCamelCase      ={
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
    }
)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
						UpperCamelCase__    :   Union[str, Any]					=				False
						for attribute in attributes:
												for modeling_source in source_strings:
																		# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
																		if (
																		    f'''config.{attribute}''' in modeling_source
																		    or f'''getattr(config, "{attribute}"''' in modeling_source
																		    or f'''getattr(self.config, "{attribute}"''' in modeling_source
																		):
																								UpperCamelCase__    :   List[Any]					=				True
																		# Deal with multi-line cases
																		elif (
																		    re.search(
																		        Rf'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"''' ,		UpperCamelCase__ ,		)
																		    is not None
																		):
																								UpperCamelCase__    :   Optional[Any]					=				True
																		# `SequenceSummary` is called with `SequenceSummary(config)`
																		elif attribute in [
																		    "summary_type",
																		    "summary_use_proj",
																		    "summary_activation",
																		    "summary_last_dropout",
																		    "summary_proj_to_labels",
																		    "summary_first_dropout",
																		]:
																								if "SequenceSummary" in modeling_source:
																														UpperCamelCase__    :   str					=				True
																		if attribute_used:
																								break
												if attribute_used:
																		break

    # common and important attributes, even if they do not always appear in the modeling files
						UpperCamelCase__    :   List[Any]					=				[
						    '''bos_index''',
						    '''eos_index''',
						    '''pad_index''',
						    '''unk_index''',
						    '''mask_index''',
						    '''image_size''',
						    '''use_cache''',
						    '''out_features''',
						    '''out_indices''',
						]
						UpperCamelCase__    :   Optional[int]					=				['''encoder_no_repeat_ngram_size''']

						# Special cases to be allowed
						UpperCamelCase__    :   Union[str, Any]					=				True
						if not attribute_used:
												UpperCamelCase__    :   Optional[Any]					=				False
												for attribute in attributes:
																		# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
																		if attribute in ["is_encoder_decoder"] and default_value is True:
																								UpperCamelCase__    :   Optional[int]					=				True
																		elif attribute in ["tie_word_embeddings"] and default_value is False:
																								UpperCamelCase__    :   List[Any]					=				True

																		# Allow cases without checking the default value in the configuration class
																		elif attribute in attributes_to_allow + attributes_used_in_generation:
																								UpperCamelCase__    :   int					=				True
																		elif attribute.endswith('''_token_id'''		):
																								UpperCamelCase__    :   Optional[Any]					=				True

																		# configuration class specific cases
																		if not case_allowed:
																								UpperCamelCase__    :   Tuple					=				SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ ,		[]		)
																								UpperCamelCase__    :   Any					=				allowed_cases is True or attribute in allowed_cases

						return attribute_used or case_allowed


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
						UpperCamelCase__    :   List[str]					=				dict(inspect.signature(config_class.__init__		).parameters		)
						UpperCamelCase__    :   Optional[Any]					=				[x for x in list(signature.keys()		) if x not in ['''self''', '''kwargs''']]
						UpperCamelCase__    :   int					=				[signature[param].default for param in parameter_names]

						# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
						# as one variant is used, the test should pass
						UpperCamelCase__    :   Tuple					=				{}
						if len(config_class.attribute_map		) > 0:
												UpperCamelCase__    :   Any					=				{v: k for k, v in config_class.attribute_map.items()}

						# Get the path to modeling source files
						UpperCamelCase__    :   Union[str, Any]					=				inspect.getsourcefile(UpperCamelCase__		)
						UpperCamelCase__    :   Optional[Any]					=				os.path.dirname(UpperCamelCase__		)
						# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
						UpperCamelCase__    :   Union[str, Any]					=				[os.path.join(UpperCamelCase__ ,		UpperCamelCase__		) for fn in os.listdir(UpperCamelCase__		) if fn.startswith('''modeling_'''		)]

						# Get the source code strings
						UpperCamelCase__    :   Union[str, Any]					=				[]
						for path in modeling_paths:
												if os.path.isfile(UpperCamelCase__		):
																		with open(UpperCamelCase__		) as fp:
																								modeling_sources.append(fp.read()		)

						UpperCamelCase__    :   Any					=				[]
						for config_param, default_value in zip(UpperCamelCase__ ,		UpperCamelCase__		):
												# `attributes` here is all the variant names for `config_param`
												UpperCamelCase__    :   str					=				[config_param]
												# some configuration classes have non-empty `attribute_map`, and both names could be used in the
												# corresponding modeling files. As long as one of them appears, it is fine.
												if config_param in reversed_attribute_map:
																		attributes.append(reversed_attribute_map[config_param]		)

												if not check_attribute_being_used(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
																		unused_attributes.append(attributes[0]		)

						return sorted(UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			):
						UpperCamelCase__    :   Union[str, Any]					=				{}
						for _config_class in list(CONFIG_MAPPING.values()		):
												# Skip deprecated models
												if "models.deprecated" in _config_class.__module__:
																		continue
												# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
												UpperCamelCase__    :   Dict					=				[
												    cls
												    for name, cls in inspect.getmembers(
												        inspect.getmodule(_config_class		) ,		lambda UpperCamelCase__		: inspect.isclass(UpperCamelCase__		)
												        and issubclass(UpperCamelCase__ ,		UpperCamelCase__		)
												        and inspect.getmodule(UpperCamelCase__		) == inspect.getmodule(_config_class		) ,		)
												]
												for config_class in config_classes_in_module:
																		UpperCamelCase__    :   str					=				check_config_attributes_being_used(UpperCamelCase__		)
																		if len(UpperCamelCase__		) > 0:
																								UpperCamelCase__    :   int					=				unused_attributes

						if len(UpperCamelCase__		) > 0:
												UpperCamelCase__    :   Optional[Any]					=				'''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
												for name, attributes in configs_with_unused_attributes.items():
																		error += f'''{name}: {attributes}\n'''

												raise ValueError(UpperCamelCase__		)


if __name__ == "__main__":
				check_config_attributes()




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)


