

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

A											=     (
    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											=     (
    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											=     [
    '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(lowercase__			, lowercase__	):





	'''simple docstring'''
	for tf_name, hf_name in patterns:
		snake_case_      		= k.replace(lowercase__			, lowercase__	)
	return k





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= BigBirdPegasusConfig(**lowercase__	)
	snake_case_      		= BigBirdPegasusForConditionalGeneration(lowercase__	)
	snake_case_      		= torch_model.state_dict()
	snake_case_      		= {}

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

	snake_case_      		= mapping['model.embed_positions.weight']
	snake_case_      		= mapping.pop('model.embed_positions.weight'	)
	snake_case_		,       snake_case_      		= torch_model.load_state_dict(lowercase__			, strict=lowercase__	)
	snake_case_      		= [
	    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(lowercase__	):





	'''simple docstring'''
	snake_case_      		= tf.train.list_variables(lowercase__	)
	snake_case_      		= {}
	snake_case_      		= ['global_step']
	for name, shape in tqdm(lowercase__			, desc='converting tf checkpoint to dict'	):
		snake_case_      		= any(pat in name for pat in ignore_name	)
		if skip_key:
			continue
		snake_case_      		= tf.train.load_variable(lowercase__			, lowercase__	)
		snake_case_      		= array
	return tf_weights





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= get_tf_weights_as_numpy(lowercase__	)
	snake_case_      		= convert_bigbird_pegasus(lowercase__			, lowercase__	)
	torch_model.save_pretrained(lowercase__	)


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



def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	if not isinstance(lowercase__			, lowercase__	):
		raise ValueError('iterations must be defined as integers'	)
	if not isinstance(lowercase__			, lowercase__	) or not number >= 1:
		raise ValueError(
		    'starting number must be\n                         and integer and be more than 0'	)
	if not iterations >= 1:
		raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'	)

	snake_case_      		= ''
	while number <= iterations:
		if number % 3 == 0:
			out += "Fizz"
		if number % 5 == 0:
			out += "Buzz"
		if 0 not in (number % 3, number % 5):
			out += str(lowercase__	)

		# print(out)
		number += 1
		out += " "
	return out


if __name__ == "__main__":
		import doctest

		doctest.testmod()
