



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_snake_case		=  (
    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
)

_snake_case		=  (
    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
)

_snake_case		=  [
    "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				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							for tf_name, hf_name in patterns:
														_lowerCAmelCase     :							Optional[Any]    =    k.replace(_lowerCamelCase				, _lowerCamelCase  )
							return k






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    BigBirdPegasusConfig(**_lowerCamelCase  )
							_lowerCAmelCase     :							Union[str, Any]    =    BigBirdPegasusForConditionalGeneration(_lowerCamelCase  )
							_lowerCAmelCase     :							List[str]    =    torch_model.state_dict()
							_lowerCAmelCase     :							Dict    =    {}

							# separating decoder weights
							_lowerCAmelCase     :							List[str]    =    {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"  )}
							_lowerCAmelCase     :							List[Any]    =    {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     :							Tuple    =    [k.endswith(_lowerCamelCase  ) for ending in KEYS_TO_IGNORE]
														if any(_lowerCamelCase  ):
																					continue
														_lowerCAmelCase     :							Any    =    DECODER_PATTERNS
														_lowerCAmelCase     :							Tuple    =    rename_state_dict_key(_lowerCamelCase				, _lowerCamelCase  )
														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     :							Optional[Any]    =    v.T
														_lowerCAmelCase     :							Tuple    =    torch.from_numpy(_lowerCamelCase  )
														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(_lowerCamelCase  ) for ending in KEYS_TO_IGNORE]
														if any(_lowerCamelCase  ):
																					continue
														_lowerCAmelCase     :							int    =    REMAINING_PATTERNS
														_lowerCAmelCase     :							Optional[int]    =    rename_state_dict_key(_lowerCamelCase				, _lowerCamelCase  )
														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     :							List[Any]    =    v.T
														_lowerCAmelCase     :							Any    =    torch.from_numpy(_lowerCamelCase  )
														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     :							str    =    mapping["model.embed_positions.weight"]
							_lowerCAmelCase     :							List[Any]    =    mapping.pop("model.embed_positions.weight"  )
							_lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    torch_model.load_state_dict(_lowerCamelCase				, strict=_lowerCamelCase  )
							_lowerCAmelCase     :							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 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    get_tf_weights_as_numpy(_lowerCamelCase  )
							_lowerCAmelCase     :							str    =    convert_bigbird_pegasus(_lowerCamelCase				, _lowerCamelCase  )
							torch_model.save_pretrained(_lowerCamelCase  )


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





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
						import torch

if is_vision_available():
						import PIL


_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							def constraint_to_multiple_of(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=0				, _lowerCamelCase=None  ):
														_lowerCAmelCase     :							Tuple    =    round(val / multiple  ) * multiple

														if max_val is not None and x > max_val:
																					_lowerCAmelCase     :							Optional[int]    =    math.floor(val / multiple  ) * multiple

														if x < min_val:
																					_lowerCAmelCase     :							List[str]    =    math.ceil(val / multiple  ) * multiple

														return x

							_lowerCAmelCase     :							Union[str, Any]    =    (output_size, output_size) if isinstance(_lowerCamelCase				, _lowerCamelCase  ) else output_size

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    get_image_size(_lowerCamelCase  )
							_lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    output_size

							# determine new height and width
							_lowerCAmelCase     :							List[Any]    =    output_height / input_height
							_lowerCAmelCase     :							Any    =    output_width / input_width

							if keep_aspect_ratio:
														# scale as little as possible
														if abs(1 - scale_width  ) < abs(1 - scale_height  ):
																					# fit width
																					_lowerCAmelCase     :							Union[str, Any]    =    scale_width
														else:
																					# fit height
																					_lowerCAmelCase     :							Union[str, Any]    =    scale_height

							_lowerCAmelCase     :							List[str]    =    constraint_to_multiple_of(scale_height * input_height				, multiple=_lowerCamelCase  )
							_lowerCAmelCase     :							Dict    =    constraint_to_multiple_of(scale_width * input_width				, multiple=_lowerCamelCase  )

							return (new_height, new_width)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		['pixel_values']







							def __init__(   self,			__a = True,			__a = None,			__a = PILImageResampling.BILINEAR,			__a = False,			__a = 1,			__a = True,			__a = 1 / 255,			__a = True,			__a = None,			__a = None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)
														_lowerCAmelCase     :							Any    =    size if size is not None else {"height": 384, "width": 384}
														_lowerCAmelCase     :							Optional[int]    =    get_size_dict(__a)
														_lowerCAmelCase     :							Optional[Any]    =    do_resize
														_lowerCAmelCase     :							Dict    =    size
														_lowerCAmelCase     :							Any    =    keep_aspect_ratio
														_lowerCAmelCase     :							str    =    ensure_multiple_of
														_lowerCAmelCase     :							str    =    resample
														_lowerCAmelCase     :							Dict    =    do_rescale
														_lowerCAmelCase     :							Optional[int]    =    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,			__a,			__a,			__a = False,			__a = 1,			__a = PILImageResampling.BICUBIC,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    get_size_dict(__a)
														if "height" not in size or "width" not in size:
																					raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}")
														_lowerCAmelCase     :							List[Any]    =    get_resize_output_image_size(
														    __a,			output_size=(size["height"], size["width"]),			keep_aspect_ratio=__a,			multiple=__a,			)
														return resize(__a,			size=__a,			resample=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														return rescale(__a,			scale=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														return normalize(__a,			mean=__a,			std=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = ChannelDimension.FIRST,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    do_resize if do_resize is not None else self.do_resize
														_lowerCAmelCase     :							List[Any]    =    size if size is not None else self.size
														_lowerCAmelCase     :							str    =    get_size_dict(__a)
														_lowerCAmelCase     :							Dict    =    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
														_lowerCAmelCase     :							Any    =    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
														_lowerCAmelCase     :							int    =    resample if resample is not None else self.resample
														_lowerCAmelCase     :							Union[str, Any]    =    do_rescale if do_rescale is not None else self.do_rescale
														_lowerCAmelCase     :							Tuple    =    rescale_factor if rescale_factor is not None else self.rescale_factor
														_lowerCAmelCase     :							List[str]    =    do_normalize if do_normalize is not None else self.do_normalize
														_lowerCAmelCase     :							Dict    =    image_mean if image_mean is not None else self.image_mean
														_lowerCAmelCase     :							List[str]    =    image_std if image_std is not None else self.image_std

														_lowerCAmelCase     :							Optional[Any]    =    make_list_of_images(__a)

														if not valid_images(__a):
																					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_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     :							List[Any]    =    [to_numpy_array(__a) for image in images]

														if do_resize:
																					_lowerCAmelCase     :							Any    =    [self.resize(image=__a,			size=__a,			resample=__a) for image in images]

														if do_rescale:
																					_lowerCAmelCase     :							List[str]    =    [self.rescale(image=__a,			scale=__a) for image in images]

														if do_normalize:
																					_lowerCAmelCase     :							Dict    =    [self.normalize(image=__a,			mean=__a,			std=__a) for image in images]

														_lowerCAmelCase     :							List[str]    =    [to_channel_dimension_format(__a,			__a) for image in images]

														_lowerCAmelCase     :							Optional[Any]    =    {"pixel_values": images}
														return BatchFeature(data=__a,			tensor_type=__a)







							def    snake_case__      (   self,			__a,			__a = None):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(__a) != len(__a):
																												raise ValueError(
																												    "Make sure that you pass in as many target sizes as the batch dimension of the logits")

																					if is_torch_tensor(__a):
																												_lowerCAmelCase     :							List[Any]    =    target_sizes.numpy()

																					_lowerCAmelCase     :							Dict    =    []

																					for idx in range(len(__a)):
																												_lowerCAmelCase     :							int    =    torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0),			size=target_sizes[idx],			mode="bilinear",			align_corners=__a)
																												_lowerCAmelCase     :							int    =    resized_logits[0].argmax(dim=0)
																												semantic_segmentation.append(__a)
														else:
																					_lowerCAmelCase     :							Dict    =    logits.argmax(dim=1)
																					_lowerCAmelCase     :							str    =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

														return semantic_segmentation
