
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


_lowercase     							=						[
    # tf -> hf
    ("/", "."),
    ("layer_", "layers."),
    ("kernel", "weight"),
    ("beta", "bias"),
    ("gamma", "weight"),
    ("pegasus", "model"),
]
_lowercase     							=						[
    (".output.dense", ".fc2"),
    ("intermediate.LayerNorm", "final_layer_norm"),
    ("intermediate.dense", "fc1"),
]

_lowercase     							=						(
    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
)

_lowercase     							=						(
    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
)

_lowercase     							=						[
    "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      _A				(UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      List[Any] )  ->str:



			'''simple docstring'''







			for tf_name, hf_name in patterns:
						lowerCamelCase__ :      Union[str, Any]								=		k.replace(_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE )
			return k






def      _A				(UpperCamelCase		:      dict      ,			UpperCamelCase		:      dict )  ->List[str]:



			'''simple docstring'''







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

			# separating decoder weights
			lowerCamelCase__ :      List[Any]								=		{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder""" )}
			lowerCamelCase__ :      List[str]								=		{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""" ):
						lowerCamelCase__ :      Union[str, Any]								=		[k.endswith(_SCREAMING_SNAKE_CASE ) for ending in KEYS_TO_IGNORE]
						if any(_SCREAMING_SNAKE_CASE ):
									continue
						lowerCamelCase__ :      Union[str, Any]								=		DECODER_PATTERNS
						lowerCamelCase__ :      Optional[Any]								=		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"""] ):
									lowerCamelCase__ :      List[Any]								=		v.T
						lowerCamelCase__ :      Tuple								=		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""" ):
						lowerCamelCase__ :      Tuple								=		[k.endswith(_SCREAMING_SNAKE_CASE ) for ending in KEYS_TO_IGNORE]
						if any(_SCREAMING_SNAKE_CASE ):
									continue
						lowerCamelCase__ :      List[Any]								=		REMAINING_PATTERNS
						lowerCamelCase__ :      Dict								=		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"""] ):
									lowerCamelCase__ :      str								=		v.T
						lowerCamelCase__ :      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}"

			lowerCamelCase__ :      Tuple								=		mapping["""model.embed_positions.weight"""]
			lowerCamelCase__ :      List[Any]								=		mapping.pop("""model.embed_positions.weight""" )
			lowerCamelCase__							,lowerCamelCase__ :      List[Any]								=		torch_model.load_state_dict(_SCREAMING_SNAKE_CASE      ,			strict=_SCREAMING_SNAKE_CASE )
			lowerCamelCase__ :      Any								=		[
			    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      _A				(UpperCamelCase		:      Any )  ->List[str]:



			'''simple docstring'''







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






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str      ,			UpperCamelCase		:      dict )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE )
			lowerCamelCase__ :      Dict								=		convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE )
			torch_model.save_pretrained(_SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				_lowercase     							=						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.''')
				_lowercase     							=						parser.parse_args()
				_lowercase     							=						{}
				convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


_lowercase     							=						logging.get_logger(__name__)
class 				__A					( A_      ):
			UpperCamelCase					:Union[str, Any]     =			['''pixel_values''']





			def __init__(self							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = 1 / 255							,    __magic_name__ = True							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = None							,    **__magic_name__							,    ):
						super().__init__(**__magic_name__				)
						lowerCamelCase__ :      str								=		size if size is not None else {"""height""": 256, """width""": 256}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Any								=		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      str								=		do_resize
						lowerCamelCase__ :      Any								=		size
						lowerCamelCase__ :      Tuple								=		resample
						lowerCamelCase__ :      Union[str, Any]								=		do_center_crop
						lowerCamelCase__ :      List[str]								=		crop_size
						lowerCamelCase__ :      int								=		do_rescale
						lowerCamelCase__ :      Any								=		rescale_factor
						lowerCamelCase__ :      Dict								=		do_normalize
						lowerCamelCase__ :      Dict								=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCamelCase__ :      int								=		image_std if image_std is not None else IMAGENET_STANDARD_STD





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						if "height" not in size or "width" not in size:
									raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
						return resize(
						    __magic_name__							,    size=(size["""height"""], size["""width"""])							,    resample=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		get_size_dict(__magic_name__				)
						if "height" not in size or "width" not in size:
									raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
						return center_crop(__magic_name__							,    size=(size["""height"""], size["""width"""])							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return rescale(__magic_name__							,    scale=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return normalize(__magic_name__							,    mean=__magic_name__							,    std=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__=None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = ChannelDimension.FIRST							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		do_resize if do_resize is not None else self.do_resize
						lowerCamelCase__ :      str								=		resample if resample is not None else self.resample
						lowerCamelCase__ :      str								=		do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCamelCase__ :      Optional[int]								=		do_rescale if do_rescale is not None else self.do_rescale
						lowerCamelCase__ :      str								=		rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCamelCase__ :      Union[str, Any]								=		do_normalize if do_normalize is not None else self.do_normalize
						lowerCamelCase__ :      Tuple								=		image_mean if image_mean is not None else self.image_mean
						lowerCamelCase__ :      List[Any]								=		image_std if image_std is not None else self.image_std

						lowerCamelCase__ :      str								=		size if size is not None else self.size
						lowerCamelCase__ :      List[str]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		crop_size if crop_size is not None else self.crop_size
						lowerCamelCase__ :      Optional[Any]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      Optional[int]								=		make_list_of_images(__magic_name__				)

						if not valid_images(__magic_name__				):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""				)

						if do_resize and size is None or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""				)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""				)

						# All transformations expect numpy arrays.
						lowerCamelCase__ :      Optional[int]								=		[to_numpy_array(__magic_name__				) for image in images]

						if do_resize:
									lowerCamelCase__ :      int								=		[self.resize(image=__magic_name__							,    size=__magic_name__							,    resample=__magic_name__				) for image in images]

						if do_center_crop:
									lowerCamelCase__ :      List[Any]								=		[self.center_crop(image=__magic_name__							,    size=__magic_name__				) for image in images]

						if do_rescale:
									lowerCamelCase__ :      Dict								=		[self.rescale(image=__magic_name__							,    scale=__magic_name__				) for image in images]

						if do_normalize:
									lowerCamelCase__ :      Optional[Any]								=		[self.normalize(image=__magic_name__							,    mean=__magic_name__							,    std=__magic_name__				) for image in images]

						lowerCamelCase__ :      List[str]								=		[to_channel_dimension_format(__magic_name__							,    __magic_name__				) for image in images]

						lowerCamelCase__ :      Dict								=		{"""pixel_values""": images}
						return BatchFeature(data=__magic_name__							,    tensor_type=__magic_name__				)



