




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								=       [
    # 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      lowerCamelCase_						(			UpperCamelCase__							: List[Any] )	->			Union[str, Any]:





				"""simple docstring"""


				for pegasus_name, hf_name in PATTERNS:
								__lowerCamelCase									=       k.replace(UpperCamelCase__					,	UpperCamelCase__ )
				return k







def      lowerCamelCase_						(			UpperCamelCase__							: dict					,	UpperCamelCase__							: dict )	->			PegasusForConditionalGeneration:





				"""simple docstring"""


				__lowerCamelCase									=       DEFAULTS.copy()
				cfg_kwargs.update(UpperCamelCase__ )
				__lowerCamelCase									=       PegasusConfig(**UpperCamelCase__ )
				__lowerCamelCase									=       PegasusForConditionalGeneration(UpperCamelCase__ )
				__lowerCamelCase									=       torch_model.model.state_dict()
				__lowerCamelCase									=       {}
				for k, v in tf_weights.items():
								__lowerCamelCase									=       rename_state_dict_key(UpperCamelCase__ )
								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:
												__lowerCamelCase									=       v.T
								__lowerCamelCase									=       torch.tensor(UpperCamelCase__					,	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
				__lowerCamelCase									=       torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
				__lowerCamelCase									=       mapping['shared.weight']
				__lowerCamelCase									=       mapping['shared.weight']
				__lowerCamelCase									=       {k: torch.zeros_like(UpperCamelCase__ ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
				mapping.update(**UpperCamelCase__ )
				__lowerCamelCase			,		__lowerCamelCase									=       torch_model.model.load_state_dict(UpperCamelCase__					,	strict=UpperCamelCase__ )
				__lowerCamelCase									=       [
				    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      lowerCamelCase_						(			UpperCamelCase__							: Dict="./ckpt/aeslc/model.ckpt-32000" )	->			Dict:





				"""simple docstring"""


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







def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: str )	->			Any:





				"""simple docstring"""


				__lowerCamelCase									=       Path(UpperCamelCase__ ).parent.name
				__lowerCamelCase									=       task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
				__lowerCamelCase									=       PegasusTokenizer.from_pretrained('sshleifer/pegasus'					,	model_max_length=UpperCamelCase__ )
				assert tok.model_max_length == desired_max_model_length
				tok.save_pretrained(UpperCamelCase__ )

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


if __name__ == "__main__":
					__A								=       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								=       parser.parse_args()
					if args.save_dir is None:
										__A								=       Path(args.tf_ckpt_path).parent.name
										__A								=       os.path.join("pegasus", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__A								=       logging.get_logger(__name__)  # pylint: disable=invalid-name







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			Tuple:





				"""simple docstring"""


				if not path:
								return "pipe"

				for ext in PipelineDataFormat.SUPPORTED_FORMATS:
								if path.endswith(UpperCamelCase__ ):
												return ext

				raise Exception(
				    F"""Unable to determine file format from file extension {path}. """
				    F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: Dict )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       pipeline(
				    task=args.task					,	model=args.model if args.model else None					,	config=args.config					,	tokenizer=args.tokenizer					,	device=args.device					,	)
				__lowerCamelCase									=       try_infer_format_from_ext(args.input ) if args.format == 'infer' else args.format
				__lowerCamelCase									=       PipelineDataFormat.from_str(
				    format=UpperCamelCase__					,	output_path=args.output					,	input_path=args.input					,	column=args.column if args.column else nlp.default_input_names					,	overwrite=args.overwrite					,	)
				return RunCommand(UpperCamelCase__					,	UpperCamelCase__ )







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       nlp
							__lowerCamelCase									=       reader


			@staticmethod
			def  lowercase_       ( lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       parser.add_parser('run'     ,		help='Run a pipeline through the CLI'				)
							run_parser.add_argument('--task'     ,		choices=get_supported_tasks()     ,		help='Task to run'				)
							run_parser.add_argument('--input'     ,		type=lowerCamelCase__     ,		help='Path to the file to use for inference'				)
							run_parser.add_argument('--output'     ,		type=lowerCamelCase__     ,		help='Path to the file that will be used post to write results.'				)
							run_parser.add_argument('--model'     ,		type=lowerCamelCase__     ,		help='Name or path to the model to instantiate.'				)
							run_parser.add_argument('--config'     ,		type=lowerCamelCase__     ,		help='Name or path to the model\'s config to instantiate.'				)
							run_parser.add_argument(
							    '--tokenizer'     ,		type=lowerCamelCase__     ,		help='Name of the tokenizer to use. (default: same as the model name)'				)
							run_parser.add_argument(
							    '--column'     ,		type=lowerCamelCase__     ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'     ,		)
							run_parser.add_argument(
							    '--format'     ,		type=lowerCamelCase__     ,		default='infer'     ,		choices=PipelineDataFormat.SUPPORTED_FORMATS     ,		help='Input format to read from'     ,		)
							run_parser.add_argument(
							    '--device'     ,		type=lowerCamelCase__     ,		default=-1     ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'     ,		)
							run_parser.add_argument('--overwrite'     ,		action='store_true'     ,		help='Allow overwriting the output file.'				)
							run_parser.set_defaults(func=lowerCamelCase__				)






			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self._nlp, []

							for entry in self._reader:
											__lowerCamelCase									=       nlp(**lowerCamelCase__				) if self._reader.is_multi_columns else nlp(lowerCamelCase__				)
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
															outputs.append(lowerCamelCase__				)
											else:
															outputs += output

        # Saving data
							if self._nlp.binary_output:
											__lowerCamelCase									=       self._reader.save_binary(lowerCamelCase__				)
											logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""				)
							else:
											self._reader.save(lowerCamelCase__				)


