
"""simple docstring"""





import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
				import tensorflow as tf

				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
				from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

_a								=						logging.get_logger(__name__)






class        _UpperCAmelCase(       enum.Enum      ):
		lowercase__         = 0
		lowercase__         = 1






@add_end_docstrings(lowerCamelCase      )
class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'generated'



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






							'''simple docstring'''
							super().__init__(*__a							,						**__a)

							self.check_model_type(
							    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							    if self.framework == '''tf'''
							    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING)



		def 		UpperCAmelCase				(   self							,						__a=None							,						__a=None							,						__a=None							,						__a=None							,						__a=None							,						__a=None							,						**__a							,						)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    {}
							if truncation is not None:
												_UpperCamelCase									=    truncation

							_UpperCamelCase									=    generate_kwargs

							_UpperCamelCase									=    {}
							if return_tensors is not None and return_type is None:
												_UpperCamelCase									=    ReturnType.TENSORS if return_tensors else ReturnType.TEXT
							if return_type is not None:
												_UpperCamelCase									=    return_type

							if clean_up_tokenization_spaces is not None:
												_UpperCamelCase									=    clean_up_tokenization_spaces

							if stop_sequence is not None:
												_UpperCamelCase									=    self.tokenizer.encode(__a							,						add_special_tokens=__a)
												if len(__a) > 1:
																	warnings.warn(
																	    '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
																	    ''' the stop sequence will be used as the stop sequence string in the interim.''')
												_UpperCamelCase									=    stop_sequence_ids[0]

							return preprocess_params, forward_params, postprocess_params



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Any:






							'''simple docstring'''
							return True



		def 		UpperCAmelCase				(   self							,						*__a							,						__a)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.model.config.prefix if self.model.config.prefix is not None else ''''''
							if isinstance(args[0]							,						__a):
												if self.tokenizer.pad_token_id is None:
																	raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input''')
												_UpperCamelCase									=    ([prefix + arg for arg in args[0]],)
												_UpperCamelCase									=    True

							elif isinstance(args[0]							,						__a):
												_UpperCamelCase									=    (prefix + args[0],)
												_UpperCamelCase									=    False
							else:
												raise ValueError(
												    F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`''')
							_UpperCamelCase									=    self.tokenizer(*__a							,						padding=__a							,						truncation=__a							,						return_tensors=self.framework)
							# This is produced by tokenizers but is an invalid generate kwargs
							if "token_type_ids" in inputs:
												del inputs["token_type_ids"]
							return inputs



		def __call__(   self							,						*__a							,						**__a)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    super().__call__(*__a							,						**__a)
							if (
							    isinstance(args[0]							,						__a)
							    and all(isinstance(__a							,						__a) for el in args[0])
							    and all(len(__a) == 1 for res in result)
							):
												return [res[0] for res in result]
							return result



		def 		UpperCAmelCase				(   self							,						__a							,						__a=TruncationStrategy.DO_NOT_TRUNCATE							,						**__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self._parse_and_tokenize(__a							,						truncation=__a							,						**__a)
							return inputs



		def 		UpperCAmelCase				(   self							,						__a							,						**__a)			->			Optional[int]:






							'''simple docstring'''
							if self.framework == "pt":
												_UpperCamelCase					,    _UpperCamelCase									=    model_inputs['''input_ids'''].shape
							elif self.framework == "tf":
												_UpperCamelCase					,    _UpperCamelCase									=    tf.shape(model_inputs['''input_ids''']).numpy()

							_UpperCamelCase									=    generate_kwargs.get('''min_length'''							,						self.model.config.min_length)
							_UpperCamelCase									=    generate_kwargs.get('''max_length'''							,						self.model.config.max_length)
							self.check_inputs(__a							,						generate_kwargs['''min_length''']							,						generate_kwargs['''max_length'''])
							_UpperCamelCase									=    self.model.generate(**__a							,						**__a)
							_UpperCamelCase									=    output_ids.shape[0]
							if self.framework == "pt":
												_UpperCamelCase									=    output_ids.reshape(__a							,						out_b // in_b							,						*output_ids.shape[1:])
							elif self.framework == "tf":
												_UpperCamelCase									=    tf.reshape(__a							,						(in_b, out_b // in_b, *output_ids.shape[1:]))
							return {"output_ids": output_ids}




		def 		UpperCAmelCase				(   self							,						__a							,						__a=ReturnType.TEXT							,						__a=False)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    []
							for output_ids in model_outputs["output_ids"][0]:
												if return_type == ReturnType.TENSORS:
																	_UpperCamelCase									=    {F'''{self.return_name}_token_ids''': output_ids}
												elif return_type == ReturnType.TEXT:
																	_UpperCamelCase									=    {
																	    F'''{self.return_name}_text''': self.tokenizer.decode(
																	        __a							,						skip_special_tokens=__a							,						clean_up_tokenization_spaces=__a							,						)
																	}
												records.append(__a)
							return records






@add_end_docstrings(lowerCamelCase      )
class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'summary'



		def __call__(   self							,						*__a							,						**__a)			->			Dict:






							'''simple docstring'''
							return super().__call__(*__a							,						**__a)




		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			bool:






							'''simple docstring'''
							if max_length < min_length:
												logger.warning(F'''Your min_length={min_length} must be inferior than your max_length={max_length}.''')

							if input_length < max_length:
												logger.warning(
												    F'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
												    '''a summarization task, where outputs shorter than the input are typically wanted, you might '''
												    F'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})''')






@add_end_docstrings(lowerCamelCase      )
class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'translation'



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							if input_length > 0.9 * max_length:
												logger.warning(
												    F'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
												    '''increasing your max_length manually, e.g. translator(\'...\', max_length=400)''')
							return True



		def 		UpperCAmelCase				(   self							,						*__a							,						__a=TruncationStrategy.DO_NOT_TRUNCATE							,						__a=None							,						__a=None)			->			Dict:






							'''simple docstring'''
							if getattr(self.tokenizer							,						'''_build_translation_inputs'''							,						__a):
												return self.tokenizer._build_translation_inputs(
												    *__a							,						return_tensors=self.framework							,						truncation=__a							,						src_lang=__a							,						tgt_lang=__a)
							else:
												return super()._parse_and_tokenize(*__a							,						truncation=__a)



		def 		UpperCAmelCase				(   self							,						__a=None							,						__a=None							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    super()._sanitize_parameters(**__a)
							if src_lang is not None:
												_UpperCamelCase									=    src_lang
							if tgt_lang is not None:
												_UpperCamelCase									=    tgt_lang
							if src_lang is None and tgt_lang is None:
												# Backward compatibility, direct arguments use is preferred.
												_UpperCamelCase									=    kwargs.get('''task'''							,						self.task)
												_UpperCamelCase									=    task.split('''_''')
												if task and len(__a) == 4:
																	# translation, XX, to YY
																	_UpperCamelCase									=    items[1]
																	_UpperCamelCase									=    items[3]
							return preprocess_params, forward_params, postprocess_params




		def __call__(   self							,						*__a							,						**__a)			->			Dict:






							'''simple docstring'''
							return super().__call__(*__a							,						**__a)










'''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


lowerCAmelCase   : 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 	_A  (       A							)  ->				List[str]:
	for pegasus_name, hf_name in PATTERNS:
		lowercase						:		int   					=		k.replace(A							,A							)
	return k




def 	_A  (       A							,A							)  ->				PegasusForConditionalGeneration:
	lowercase						:		int   					=		DEFAULTS.copy()
	cfg_kwargs.update(A							)
	lowercase						:		Any   					=		PegasusConfig(**A							)
	lowercase						:		Optional[Any]   					=		PegasusForConditionalGeneration(A							)
	lowercase						:		Dict   					=		torch_model.model.state_dict()
	lowercase						:		List[str]   					=		{}
	for k, v in tf_weights.items():
		lowercase						:		Optional[Any]   					=		rename_state_dict_key(A							)
		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:
			lowercase						:		Tuple   					=		v.T
		lowercase						:		str   					=		torch.tensor(A							,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
	lowercase						:		Union[str, Any]   					=		torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]							)
	lowercase						:		Optional[int]   					=		mapping["shared.weight"]
	lowercase						:		int   					=		mapping["shared.weight"]
	lowercase						:		Optional[Any]   					=		{k: torch.zeros_like(A							) for k, v in sd.items() if k.endswith("bias"							) and k not in mapping}
	mapping.update(**A							)
	lowercase					,		lowercase						:		Union[str, Any]   					=		torch_model.model.load_state_dict(A							,strict=A							)
	lowercase						:		List[str]   					=		[
	    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 	_A  (       A="./ckpt/aeslc/model.ckpt-32000"							)  ->				Dict:
	lowercase						:		Optional[int]   					=		tf.train.list_variables(A							)
	lowercase						:		List[str]   					=		{}
	lowercase						:		List[str]   					=		["Adafactor", "global_step"]
	for name, shape in tqdm(A							,desc="converting tf checkpoint to dict"							):
		lowercase						:		Any   					=		any(pat in name for pat in ignore_name							)
		if skip_key:
			continue
		lowercase						:		Optional[Any]   					=		tf.train.load_variable(A							,A							)
		lowercase						:		Dict   					=		array
	return tf_weights




def 	_A  (       A							,A							)  ->				Union[str, Any]:
	# save tokenizer first
	lowercase						:		int   					=		Path(A							).parent.name
	lowercase						:		str   					=		task_specific_params[F'''summarization_{dataset}''']["max_position_embeddings"]
	lowercase						:		int   					=		PegasusTokenizer.from_pretrained("sshleifer/pegasus"							,model_max_length=A							)
	assert tok.model_max_length == desired_max_model_length
	tok.save_pretrained(A							)

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


if __name__ == "__main__":
					lowerCAmelCase   : 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.""")
					lowerCAmelCase   : Dict             =				parser.parse_args()
					if args.save_dir is None:
										lowerCAmelCase   : Dict             =				Path(args.tf_ckpt_path).parent.name
										lowerCAmelCase   : Optional[int]             =				os.path.join("""pegasus""", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


