





"""simple docstring"""




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


a		:				Any  			=    [
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
a		:				Union[str, Any]  			=    [
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

a		:				Tuple  			=    (
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

a		:				List[str]  			=    (
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

a		:				Optional[int]  			=    [
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple					, _lowercase		:					Tuple      )				->Any:






	'''simple docstring'''






	for tf_name, hf_name in patterns:
		a   :	Tuple			=       k.replace(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      )
	return k






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					Dict      )				->BigBirdPegasusForConditionalGeneration:






	'''simple docstring'''






	a   :	Optional[int]			=       BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE      )
	a   :	Union[str, Any]			=       BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE      )
	a   :	int			=       torch_model.state_dict()
	a   :	str			=       {}

	# separating decoder weights
	a   :	Optional[int]			=       {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"      )}
	a   :	Tuple			=       {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder"      )}

	for k, v in tqdm(decoder_weights.items()					, "tf -> hf conversion"      ):
		a   :	Optional[Any]			=       [k.endswith(_SCREAMING_SNAKE_CASE      ) for ending in KEYS_TO_IGNORE]
		if any(_SCREAMING_SNAKE_CASE      ):
			continue
		a   :	Tuple			=       DECODER_PATTERNS
		a   :	Optional[int]			=       rename_state_dict_key(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      )
		if new_k not in state_dict:
			raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""      )
		if any(True if i in k else False for i in ["dense", "query", "key", "value"]      ):
			a   :	Optional[int]			=       v.T
		a   :	List[str]			=       torch.from_numpy(_SCREAMING_SNAKE_CASE      )
		assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

	for k, v in tqdm(remaining_weights.items()					, "tf -> hf conversion"      ):
		a   :	Union[str, Any]			=       [k.endswith(_SCREAMING_SNAKE_CASE      ) for ending in KEYS_TO_IGNORE]
		if any(_SCREAMING_SNAKE_CASE      ):
			continue
		a   :	int			=       REMAINING_PATTERNS
		a   :	Tuple			=       rename_state_dict_key(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      )
		if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
			raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""      )
		if any(True if i in k else False for i in ["dense", "query", "key", "value"]      ):
			a   :	Tuple			=       v.T
		a   :	List[Any]			=       torch.from_numpy(_SCREAMING_SNAKE_CASE      )
		if k != "pegasus/embeddings/position_embeddings":
			assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

	a   :	Any			=       mapping["model.embed_positions.weight"]
	a   :	Union[str, Any]			=       mapping.pop("model.embed_positions.weight"      )
	a, a   :	Optional[int]			=       torch_model.load_state_dict(_SCREAMING_SNAKE_CASE					, strict=_SCREAMING_SNAKE_CASE      )
	a   :	List[str]			=       [
	    k
	    for k in missing
	    if k
	    not in [
	        "final_logits_bias",
	        "model.encoder.embed_tokens.weight",
	        "model.decoder.embed_tokens.weight",
	        "lm_head.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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]      )				->Dict:






	'''simple docstring'''






	a   :	Any			=       tf.train.list_variables(_SCREAMING_SNAKE_CASE      )
	a   :	List[Any]			=       {}
	a   :	Optional[Any]			=       ["global_step"]
	for name, shape in tqdm(_SCREAMING_SNAKE_CASE					, desc="converting tf checkpoint to dict"      ):
		a   :	Any			=       any(pat in name for pat in ignore_name      )
		if skip_key:
			continue
		a   :	str			=       tf.train.load_variable(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      )
		a   :	Dict			=       array
	return tf_weights






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]					, _lowercase		:					Union[str, Any]					, _lowercase		:					Tuple      )				->List[str]:






	'''simple docstring'''






	a   :	List[str]			=       get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE      )
	a   :	Optional[Any]			=       convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      )
	torch_model.save_pretrained(_SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
		a		:				List[str]  			=    argparse.ArgumentParser()
		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		:				Union[str, Any]  			=    parser.parse_args()
		a		:				Dict  			=    {}
		convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)









"""simple docstring"""




import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					List[str]					, _lowercase		:					Optional[Any]      )				->str:






	'''simple docstring'''






	a   :	Union[str, Any]			=       1.5
	a   :	List[str]			=       int(factor * num_class_images      )
	a   :	Optional[Any]			=       ClipClient(
	    url="https://knn.laion.ai/knn-service"					, indice_name="laion_400m"					, num_images=_lowercase					, aesthetic_weight=0.1      )

	os.makedirs(F"""{class_data_dir}/images"""					, exist_ok=_lowercase      )
	if len(list(Path(F"""{class_data_dir}/images"""      ).iterdir()      )      ) >= num_class_images:
		return

	while True:
		a   :	List[Any]			=       client.query(text=_lowercase      )
		if len(_lowercase      ) >= factor * num_class_images or num_images > 1E4:
			break
		else:
			a   :	Optional[int]			=       int(factor * num_images      )
			a   :	str			=       ClipClient(
			    url="https://knn.laion.ai/knn-service"					, indice_name="laion_400m"					, num_images=_lowercase					, aesthetic_weight=0.1					, )

	a   :	Optional[int]			=       0
	a   :	str			=       0
	a   :	Any			=       tqdm(desc="downloading real regularization images"					, total=_lowercase      )

	with open(F"""{class_data_dir}/caption.txt"""					, "w"      ) as fa, open(F"""{class_data_dir}/urls.txt"""					, "w"      ) as fa, open(
	    F"""{class_data_dir}/images.txt"""					, "w"      ) as fa:
		while total < num_class_images:
			a   :	Optional[Any]			=       class_images[count]
			count += 1
			try:
				a   :	str			=       requests.get(images["url"]      )
				if img.status_code == 200:
					a   :	int			=       Image.open(BytesIO(img.content      )      )
					with open(F"""{class_data_dir}/images/{total}.jpg"""					, "wb"      ) as f:
						f.write(img.content      )
					fa.write(images["caption"] + "\n"      )
					fa.write(images["url"] + "\n"      )
					fa.write(F"""{class_data_dir}/images/{total}.jpg""" + "\n"      )
					total += 1
					pbar.update(1      )
				else:
					continue
			except Exception:
				continue
	return






def    _SCREAMING_SNAKE_CASE	(			)				->Dict:






	'''simple docstring'''






	a   :	Optional[int]			=       argparse.ArgumentParser(""					, add_help=_lowercase      )
	parser.add_argument("--class_prompt"					, help="text prompt to retrieve images"					, required=_lowercase					, type=_lowercase      )
	parser.add_argument("--class_data_dir"					, help="path to save images"					, required=_lowercase					, type=_lowercase      )
	parser.add_argument("--num_class_images"					, help="number of images to download"					, default=200					, type=_lowercase      )
	return parser.parse_args()


if __name__ == "__main__":
		a		:				List[Any]  			=    parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)


