import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_lowerCAmelCase       =		(
    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
)

_lowerCAmelCase       =		(
    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
)

_lowerCAmelCase       =		[
    "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 					_snake_case	(  __snake_case  ,     __snake_case						):
					for tf_name, hf_name in patterns:
										_UpperCamelCase			     =     k.replace(__snake_case  ,     __snake_case						)
					return k
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     BigBirdPegasusConfig(**__snake_case						)
					_UpperCamelCase			     =     BigBirdPegasusForConditionalGeneration(__snake_case						)
					_UpperCamelCase			     =     torch_model.state_dict()
					_UpperCamelCase			     =     {}

					# separating decoder weights
					_UpperCamelCase			     =     {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''						)}
					_UpperCamelCase			     =     {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'''						):
										_UpperCamelCase			     =     [k.endswith(__snake_case						) for ending in KEYS_TO_IGNORE]
										if any(__snake_case						):
															continue
										_UpperCamelCase			     =     DECODER_PATTERNS
										_UpperCamelCase			     =     rename_state_dict_key(__snake_case  ,     __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''']						):
															_UpperCamelCase			     =     v.T
										_UpperCamelCase			     =     torch.from_numpy(__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'''						):
										_UpperCamelCase			     =     [k.endswith(__snake_case						) for ending in KEYS_TO_IGNORE]
										if any(__snake_case						):
															continue
										_UpperCamelCase			     =     REMAINING_PATTERNS
										_UpperCamelCase			     =     rename_state_dict_key(__snake_case  ,     __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''']						):
															_UpperCamelCase			     =     v.T
										_UpperCamelCase			     =     torch.from_numpy(__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}"""

					_UpperCamelCase			     =     mapping['''model.embed_positions.weight''']
					_UpperCamelCase			     =     mapping.pop('''model.embed_positions.weight'''						)
					_UpperCamelCase     ,    _UpperCamelCase			     =     torch_model.load_state_dict(__snake_case  ,     strict=__snake_case						)
					_UpperCamelCase			     =     [
					    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 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     tf.train.list_variables(__snake_case						)
					_UpperCamelCase			     =     {}
					_UpperCamelCase			     =     ['''global_step''']
					for name, shape in tqdm(__snake_case  ,     desc='''converting tf checkpoint to dict'''						):
										_UpperCamelCase			     =     any(pat in name for pat in ignore_name						)
										if skip_key:
															continue
										_UpperCamelCase			     =     tf.train.load_variable(__snake_case  ,     __snake_case						)
										_UpperCamelCase			     =     array
					return tf_weights
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     get_tf_weights_as_numpy(__snake_case						)
					_UpperCamelCase			     =     convert_bigbird_pegasus(__snake_case  ,     __snake_case						)
					torch_model.save_pretrained(__snake_case						)


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

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return math.sqrt(sum(pow(a - b  ,     2						) for a, b in zip(__snake_case  ,     __snake_case						)						)						)
def 					_snake_case	(  __snake_case  ,     __snake_case						):

					if dataset.ndim != value_array.ndim:
										_UpperCamelCase			     =     (
										    '''Wrong input data\'s dimensions... '''
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(__snake_case						)

					try:
										if dataset.shape[1] != value_array.shape[1]:
															_UpperCamelCase			     =     (
															    '''Wrong input data\'s shape... '''
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(__snake_case						)
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError('''Wrong shape'''						)

					if dataset.dtype != value_array.dtype:
										_UpperCamelCase			     =     (
										    '''Input data have different datatype... '''
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(__snake_case						)

					_UpperCamelCase			     =     []

					for value in value_array:
										_UpperCamelCase			     =     euclidean(__snake_case  ,     dataset[0]						)
										_UpperCamelCase			     =     dataset[0].tolist()

										for dataset_value in dataset[1:]:
															_UpperCamelCase			     =     euclidean(__snake_case  ,     __snake_case						)

															if dist > temp_dist:
																				_UpperCamelCase			     =     temp_dist
																				_UpperCamelCase			     =     dataset_value.tolist()

										answer.append([vector, dist]						)

					return answer
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return np.dot(__snake_case  ,     __snake_case						) / (norm(__snake_case						) * norm(__snake_case						))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
