




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


SCREAMING_SNAKE_CASE:     Optional[int]									= [
    # 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							(					lowerCAmelCase )->	str:
		for pegasus_name, hf_name in PATTERNS:
				SCREAMING_SNAKE_CASE_					      =							k.replace(lowerCAmelCase    ,						lowerCAmelCase )
		return k


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	PegasusForConditionalGeneration:
		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 					_a							(					lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" )->	Dict:
		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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Optional[Any]:
		# save tokenizer first
		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__":
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= 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.''')
						SCREAMING_SNAKE_CASE:     int									= parser.parse_args()
						if args.save_dir is None:
												SCREAMING_SNAKE_CASE:     List[Any]									= Path(args.tf_ckpt_path).parent.name
												SCREAMING_SNAKE_CASE:     Tuple									= os.path.join('''pegasus''', dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

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

if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						from ..tf_utils import stable_softmax

SCREAMING_SNAKE_CASE:     Optional[int]									= logging.get_logger(__name__)



@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   Any					,							**snake_case__	:   str  ):

								"""simple docstring"""

								super().__init__(**snake_case__  )

								requires_backends(self					,							'vision'  )
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == 'tf'
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING  )



						def __call__(			self	:   str					,							snake_case__	:   Union[str, List[str], "Image", List["Image"]]					,							**snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								return super().__call__(snake_case__					,							**snake_case__  )



						def 	__a					(			self	:   str					,							**snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{}
								if "candidate_labels" in kwargs:
										SCREAMING_SNAKE_CASE_					      =							kwargs['candidate_labels']
								if "hypothesis_template" in kwargs:
										SCREAMING_SNAKE_CASE_					      =							kwargs['hypothesis_template']

								return preprocess_params, {}, {}



						def 	__a					(			self	:   List[str]					,							snake_case__	:   Optional[int]					,							snake_case__	:   Optional[Any]=None					,							snake_case__	:   List[str]="This is a photo of {}."  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							load_image(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.image_processor(images=[image]					,							return_tensors=self.framework  )
								SCREAMING_SNAKE_CASE_					      =							candidate_labels
								SCREAMING_SNAKE_CASE_					      =							[hypothesis_template.format(snake_case__  ) for x in candidate_labels]
								SCREAMING_SNAKE_CASE_					      =							self.tokenizer(snake_case__					,							return_tensors=self.framework					,							padding=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							[text_inputs]
								return inputs



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							model_inputs.pop('candidate_labels'  )
								SCREAMING_SNAKE_CASE_					      =							model_inputs.pop('text_inputs'  )
								if isinstance(text_inputs[0]					,							snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							text_inputs[0]
								else:
										# Batching case.
										SCREAMING_SNAKE_CASE_					      =							text_inputs[0][0]

								SCREAMING_SNAKE_CASE_					      =							self.model(**snake_case__					,							**snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							{
								    'candidate_labels': candidate_labels,
								    'logits': outputs.logits_per_image,
								}
								return model_outputs






						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							model_outputs.pop('candidate_labels'  )
								SCREAMING_SNAKE_CASE_					      =							model_outputs['logits'][0]
								if self.framework == "pt":
										SCREAMING_SNAKE_CASE_					      =							logits.softmax(dim=-1  ).squeeze(-1  )
										SCREAMING_SNAKE_CASE_					      =							probs.tolist()
										if not isinstance(snake_case__					,							snake_case__  ):
												SCREAMING_SNAKE_CASE_					      =							[scores]
								elif self.framework == "tf":
										SCREAMING_SNAKE_CASE_					      =							stable_softmax(snake_case__					,							axis=-1  )
										SCREAMING_SNAKE_CASE_					      =							probs.numpy().tolist()
								else:
										raise ValueError(f'''Unsupported framework: {self.framework}'''  )

								SCREAMING_SNAKE_CASE_					      =							[
								    {'score': score, 'label': candidate_label}
								    for score, candidate_label in sorted(zip(snake_case__					,							snake_case__  )					,							key=lambda snake_case__  : -x[0]  )
								]
								return result