
"""simple docstring"""





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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Any:



							for tf_name, hf_name in patterns:
														_SCREAMING_SNAKE_CASE		:						Any			  =    k.replace(_SCREAMING_SNAKE_CASE     ,	_SCREAMING_SNAKE_CASE  )
							return k



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[str]:
							_SCREAMING_SNAKE_CASE		:						str			  =    BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch_model.state_dict()
							_SCREAMING_SNAKE_CASE		:						int			  =    {}

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

							_SCREAMING_SNAKE_CASE		:						str			  =    mapping["""model.embed_positions.weight"""]
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    mapping.pop("""model.embed_positions.weight"""  )
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch_model.load_state_dict(_SCREAMING_SNAKE_CASE     ,	strict=_SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						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      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    tf.train.list_variables(_SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    {}
							_SCREAMING_SNAKE_CASE		:						int			  =    ["""global_step"""]
							for name, shape in tqdm(_SCREAMING_SNAKE_CASE     ,	desc="""converting tf checkpoint to dict"""  ):
														_SCREAMING_SNAKE_CASE		:						Any			  =    any(pat in name for pat in ignore_name  )
														if skip_key:
																					continue
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    tf.train.load_variable(_SCREAMING_SNAKE_CASE     ,	_SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE		:						Any			  =    array
							return tf_weights



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[str]:
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE     ,	_SCREAMING_SNAKE_CASE  )
							torch_model.save_pretrained(_SCREAMING_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)



"""simple docstring"""





import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
			import torch

			from transformers import AutoModelForCausalLM




@require_torch
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    tokenizer.decode(greedy_ids[0])

											with CaptureStdout() as cs:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    TextStreamer(_A)
																		model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A      ,    streamer=_A)
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											_SCREAMING_SNAKE_CASE		:						str			  =    cs.out[:-1]

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Any			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    -1

											_SCREAMING_SNAKE_CASE		:						str			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    tokenizer.decode(greedy_ids[0])

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    TextIteratorStreamer(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    {"""input_ids""": input_ids, """max_new_tokens""": 1_0, """do_sample""": False, """streamer""": streamer}
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    Thread(target=model.generate      ,    kwargs=_A)
											thread.start()
											_SCREAMING_SNAKE_CASE		:						Any			  =    """"""
											for new_text in streamer:
																		streamer_text += new_text

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Dict			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1

											_SCREAMING_SNAKE_CASE		:						Any			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    greedy_ids[:, input_ids.shape[1] :]
											_SCREAMING_SNAKE_CASE		:						Dict			  =    tokenizer.decode(new_greedy_ids[0])

											with CaptureStdout() as cs:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    TextStreamer(_A      ,    skip_prompt=_A)
																		model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A      ,    streamer=_A)
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    cs.out[:-1]

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    AutoTokenizer.from_pretrained("""distilgpt2""")
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoModelForCausalLM.from_pretrained("""distilgpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    -1

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.ones((1, 5)      ,    device=_A).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TextStreamer(_A      ,    skip_special_tokens=_A)
																		model.generate(_A      ,    max_new_tokens=1      ,    do_sample=_A      ,    streamer=_A)

											# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
											# re-tokenized, must only contain one token
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    cs.out[:-1]  # Remove the final "\n"
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    tokenizer(_A      ,    return_tensors="""pt""")
											self.assertEqual(streamer_text_tokenized.input_ids.shape      ,    (1, 1))





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    -1

											_SCREAMING_SNAKE_CASE		:						Dict			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    TextIteratorStreamer(_A      ,    timeout=0.001)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {"""input_ids""": input_ids, """max_new_tokens""": 1_0, """do_sample""": False, """streamer""": streamer}
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    Thread(target=model.generate      ,    kwargs=_A)
											thread.start()

											# The streamer will timeout after 0.001 seconds, so an exception will be raised
											with self.assertRaises(_A):
																		_SCREAMING_SNAKE_CASE		:						str			  =    """"""
																		for new_text in streamer:
																									streamer_text += new_text


