


"""simple docstring"""







import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


a__       :							Optional[Any]        =  [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					for pegasus_name, hf_name in PATTERNS:
										__SCREAMING_SNAKE_CASE		     =       k.replace(lowerCAmelCase_						,		lowerCAmelCase_     )
					return k




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       DEFAULTS.copy()
					cfg_kwargs.update(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       PegasusConfig(**lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       PegasusForConditionalGeneration(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       torch_model.model.state_dict()
					__SCREAMING_SNAKE_CASE		     =       {}
					for k, v in tf_weights.items():
										__SCREAMING_SNAKE_CASE		     =       rename_state_dict_key(lowerCAmelCase_     )
										if new_k not in sd:
															raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""     )

										if "dense" in k or "proj" in new_k:
															__SCREAMING_SNAKE_CASE		     =       v.T
										__SCREAMING_SNAKE_CASE		     =       torch.tensor(lowerCAmelCase_						,		dtype=sd[new_k].dtype     )
										assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
					# make sure embedding.padding_idx is respected
					__SCREAMING_SNAKE_CASE		     =       torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]     )
					__SCREAMING_SNAKE_CASE		     =       mapping["shared.weight"]
					__SCREAMING_SNAKE_CASE		     =       mapping["shared.weight"]
					__SCREAMING_SNAKE_CASE		     =       {k: torch.zeros_like(lowerCAmelCase_     ) for k, v in sd.items() if k.endswith("bias"     ) and k not in mapping}
					mapping.update(**lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       torch_model.model.load_state_dict(lowerCAmelCase_						,		strict=lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       [
					    k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
					]
					assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
					assert extra == [], f"""no matches found for the following tf keys {extra}"""
					return torch_model




def      UpperCAmelCase__ (lowerCAmelCase_="./ckpt/aeslc/model.ckpt-32000"     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       tf.train.list_variables(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       {}
					__SCREAMING_SNAKE_CASE		     =       ["Adafactor", "global_step"]
					for name, shape in tqdm(lowerCAmelCase_						,		desc="converting tf checkpoint to dict"     ):
										__SCREAMING_SNAKE_CASE		     =       any(pat in name for pat in ignore_name     )
										if skip_key:
															continue
										__SCREAMING_SNAKE_CASE		     =       tf.train.load_variable(lowerCAmelCase_						,		lowerCAmelCase_     )
										__SCREAMING_SNAKE_CASE		     =       array
					return tf_weights




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       Path(lowerCAmelCase_     ).parent.name
					__SCREAMING_SNAKE_CASE		     =       task_specific_params[f"""summarization_{dataset}"""]["max_position_embeddings"]
					__SCREAMING_SNAKE_CASE		     =       PegasusTokenizer.from_pretrained("sshleifer/pegasus"						,		model_max_length=lowerCAmelCase_     )
					assert tok.model_max_length == desired_max_model_length
					tok.save_pretrained(lowerCAmelCase_     )

					# convert model
					__SCREAMING_SNAKE_CASE		     =       get_tf_weights_as_numpy(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       task_specific_params[f"""summarization_{dataset}"""]
					if dataset == "large":
										__SCREAMING_SNAKE_CASE		     =       task_specific_params
					__SCREAMING_SNAKE_CASE		     =       convert_pegasus(lowerCAmelCase_						,		lowerCAmelCase_     )
					torch_model.save_pretrained(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       torch_model.state_dict()
					sd.pop("model.decoder.embed_positions.weight"     )
					sd.pop("model.encoder.embed_positions.weight"     )
					torch.save(lowerCAmelCase_						,		Path(lowerCAmelCase_     ) / "pytorch_model.bin"     )


if __name__ == "__main__":
		a__       :							Optional[int]        =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
		parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
		a__       :							int        =  parser.parse_args()
		if args.save_dir is None:
				a__       :							str        =  Path(args.tf_ckpt_path).parent.name
				a__       :							Union[str, Any]        =  os.path.join('''pegasus''', dataset)
		convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)




"""simple docstring"""







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


a__       :							Dict        =  logging.get_logger(__name__)

a__       :							str        =  {
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Optional[Any]					      =    "dpt"




	def __init__(				self    :       Tuple    ,    UpperCAmelCase__    :       int=7_6_8    ,    UpperCAmelCase__    :       List[str]=1_2    ,    UpperCAmelCase__    :       Any=1_2    ,    UpperCAmelCase__    :       int=3_0_7_2    ,    UpperCAmelCase__    :       List[str]="gelu"    ,    UpperCAmelCase__    :       Union[str, Any]=0.0    ,    UpperCAmelCase__    :       Any=0.0    ,    UpperCAmelCase__    :       List[Any]=0.02    ,    UpperCAmelCase__    :       List[Any]=1E-12    ,    UpperCAmelCase__    :       List[str]=3_8_4    ,    UpperCAmelCase__    :       Tuple=1_6    ,    UpperCAmelCase__    :       Optional[Any]=3    ,    UpperCAmelCase__    :       str=False    ,    UpperCAmelCase__    :       Any=True    ,    UpperCAmelCase__    :       Optional[Any]=[2, 5, 8, 1_1]    ,    UpperCAmelCase__    :       Union[str, Any]="project"    ,    UpperCAmelCase__    :       Dict=[4, 2, 1, 0.5]    ,    UpperCAmelCase__    :       Optional[Any]=[9_6, 1_9_2, 3_8_4, 7_6_8]    ,    UpperCAmelCase__    :       List[str]=2_5_6    ,    UpperCAmelCase__    :       Optional[Any]=-1    ,    UpperCAmelCase__    :       Union[str, Any]=False    ,    UpperCAmelCase__    :       Any=True    ,    UpperCAmelCase__    :       Dict=0.4    ,    UpperCAmelCase__    :       Union[str, Any]=2_5_5    ,    UpperCAmelCase__    :       List[Any]=0.1    ,    UpperCAmelCase__    :       Optional[int]=[1, 1_0_2_4, 2_4, 2_4]    ,    UpperCAmelCase__    :       List[Any]=[0, 1]    ,    UpperCAmelCase__    :       List[str]=None    ,    **UpperCAmelCase__    :       List[Any]    ,    ) ->			Dict:
						super().__init__(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       hidden_size
						__SCREAMING_SNAKE_CASE		     =       is_hybrid

						if self.is_hybrid:
											if backbone_config is None:
																logger.info("Initializing the config with a `BiT` backbone."     )
																__SCREAMING_SNAKE_CASE		     =       {
																    "global_padding": "same",
																    "layer_type": "bottleneck",
																    "depths": [3, 4, 9],
																    "out_features": ["stage1", "stage2", "stage3"],
																    "embedding_dynamic_padding": True,
																}
																__SCREAMING_SNAKE_CASE		     =       BitConfig(**UpperCAmelCase__     )
											elif isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ):
																logger.info("Initializing the config with a `BiT` backbone."     )
																__SCREAMING_SNAKE_CASE		     =       BitConfig(**UpperCAmelCase__     )
											elif isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ):
																__SCREAMING_SNAKE_CASE		     =       backbone_config
											else:
																raise ValueError(
																    F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""     )

											__SCREAMING_SNAKE_CASE		     =       backbone_featmap_shape
											__SCREAMING_SNAKE_CASE		     =       neck_ignore_stages

											if readout_type != "project":
																raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."     )
						else:
											__SCREAMING_SNAKE_CASE		     =       None
											__SCREAMING_SNAKE_CASE		     =       None
											__SCREAMING_SNAKE_CASE		     =       []

						__SCREAMING_SNAKE_CASE		     =       num_hidden_layers
						__SCREAMING_SNAKE_CASE		     =       num_attention_heads
						__SCREAMING_SNAKE_CASE		     =       intermediate_size
						__SCREAMING_SNAKE_CASE		     =       hidden_act
						__SCREAMING_SNAKE_CASE		     =       hidden_dropout_prob
						__SCREAMING_SNAKE_CASE		     =       attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE		     =       initializer_range
						__SCREAMING_SNAKE_CASE		     =       layer_norm_eps
						__SCREAMING_SNAKE_CASE		     =       image_size
						__SCREAMING_SNAKE_CASE		     =       patch_size
						__SCREAMING_SNAKE_CASE		     =       num_channels
						__SCREAMING_SNAKE_CASE		     =       qkv_bias
						__SCREAMING_SNAKE_CASE		     =       backbone_out_indices
						if readout_type not in ["ignore", "add", "project"]:
											raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"     )
						__SCREAMING_SNAKE_CASE		     =       readout_type
						__SCREAMING_SNAKE_CASE		     =       reassemble_factors
						__SCREAMING_SNAKE_CASE		     =       neck_hidden_sizes
						__SCREAMING_SNAKE_CASE		     =       fusion_hidden_size
						__SCREAMING_SNAKE_CASE		     =       head_in_index
						__SCREAMING_SNAKE_CASE		     =       use_batch_norm_in_fusion_residual
						# auxiliary head attributes (semantic segmentation)
						__SCREAMING_SNAKE_CASE		     =       use_auxiliary_head
						__SCREAMING_SNAKE_CASE		     =       auxiliary_loss_weight
						__SCREAMING_SNAKE_CASE		     =       semantic_loss_ignore_index
						__SCREAMING_SNAKE_CASE		     =       semantic_classifier_dropout




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       copy.deepcopy(self.__dict__     )

						if output["backbone_config"] is not None:
											__SCREAMING_SNAKE_CASE		     =       self.backbone_config.to_dict()

						__SCREAMING_SNAKE_CASE		     =       self.__class__.model_type
						return output
