
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    a      ( a		,						a	)     ->Optional[Any]:




			'''simple docstring'''


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


def    a      ( a		,						a	)     ->BigBirdPegasusForConditionalGeneration:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= BigBirdPegasusConfig(**a	)
			SCREAMING_SNAKE_CASE     						= BigBirdPegasusForConditionalGeneration(a	)
			SCREAMING_SNAKE_CASE     						= torch_model.state_dict()
			SCREAMING_SNAKE_CASE     						= {}

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

			SCREAMING_SNAKE_CASE     						= mapping['''model.embed_positions.weight''']
			SCREAMING_SNAKE_CASE     						= mapping.pop('''model.embed_positions.weight'''	)
			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= torch_model.load_state_dict(a		,						strict=a	)
			SCREAMING_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      ( a	)     ->Dict:




			'''simple docstring'''


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


def    a      ( a		,						a		,						a	)     ->Any:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= get_tf_weights_as_numpy(a	)
			SCREAMING_SNAKE_CASE     						= convert_bigbird_pegasus(a		,						a	)
			torch_model.save_pretrained(a	)


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)

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch





@require_torch
@require_optimum
@slow
class     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained(lowercase			)
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= tokenizer('''This is me'''   ,     return_tensors='''pt'''			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				SCREAMING_SNAKE_CASE     						= model.generate(**lowercase			)

				SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

							self.assertFalse(
							    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

							SCREAMING_SNAKE_CASE     						= model_reloaded.generate(**lowercase			)
							self.assertTrue(torch.allclose(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
							with self.assertRaises(lowercase			):
										model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()
							model.save_pretrained(lowercase			)