import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_a  :  Union[str, Any]=				(
    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  :  Union[str, Any]=				(
    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  :  List[Any]=				[
    "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 						__UpperCAmelCase       (					UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       str						)							->    Dict:

	'''simple docstring'''





	for tf_name, hf_name in patterns:
		__snake_case    :							List[Any]   	=				k.replace(UpperCAmelCase_    ,							UpperCAmelCase_						)
	return k
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       dict    ,							UpperCAmelCase_					:       dict						)							->    BigBirdPegasusForConditionalGeneration:

	'''simple docstring'''





	__snake_case    :							Tuple   	=				BigBirdPegasusConfig(**UpperCAmelCase_						)
	__snake_case    :							Dict   	=				BigBirdPegasusForConditionalGeneration(UpperCAmelCase_						)
	__snake_case    :							int   	=				torch_model.state_dict()
	__snake_case    :							str   	=				{}

	# separating decoder weights
	__snake_case    :							str   	=				{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'						)}
	__snake_case    :							str   	=				{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    :							Tuple   	=				[k.endswith(UpperCAmelCase_						) for ending in KEYS_TO_IGNORE]
		if any(UpperCAmelCase_						):
			continue
		__snake_case    :							str   	=				DECODER_PATTERNS
		__snake_case    :							Optional[int]   	=				rename_state_dict_key(UpperCAmelCase_    ,							UpperCAmelCase_						)
		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    :							Optional[Any]   	=				v.T
		__snake_case    :							Dict   	=				torch.from_numpy(UpperCAmelCase_						)
		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    :							List[str]   	=				[k.endswith(UpperCAmelCase_						) for ending in KEYS_TO_IGNORE]
		if any(UpperCAmelCase_						):
			continue
		__snake_case    :							List[str]   	=				REMAINING_PATTERNS
		__snake_case    :							List[str]   	=				rename_state_dict_key(UpperCAmelCase_    ,							UpperCAmelCase_						)
		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    :							Union[str, Any]   	=				v.T
		__snake_case    :							Dict   	=				torch.from_numpy(UpperCAmelCase_						)
		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    :							Optional[int]   	=				mapping['model.embed_positions.weight']
	__snake_case    :							Tuple   	=				mapping.pop('model.embed_positions.weight'						)
	__snake_case    :							Optional[int]   	=				torch_model.load_state_dict(UpperCAmelCase_    ,							strict=UpperCAmelCase_						)
	__snake_case    :							Optional[int]   	=				[
	    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 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Optional[Any]   	=				tf.train.list_variables(UpperCAmelCase_						)
	__snake_case    :							Optional[int]   	=				{}
	__snake_case    :							int   	=				['global_step']
	for name, shape in tqdm(UpperCAmelCase_    ,							desc='converting tf checkpoint to dict'						):
		__snake_case    :							str   	=				any(pat in name for pat in ignore_name						)
		if skip_key:
			continue
		__snake_case    :							List[str]   	=				tf.train.load_variable(UpperCAmelCase_    ,							UpperCAmelCase_						)
		__snake_case    :							Union[str, Any]   	=				array
	return tf_weights
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str    ,							UpperCAmelCase_					:       str    ,							UpperCAmelCase_					:       dict						)							->    int:

	'''simple docstring'''





	__snake_case    :							List[Any]   	=				get_tf_weights_as_numpy(UpperCAmelCase_						)
	__snake_case    :							List[Any]   	=				convert_bigbird_pegasus(UpperCAmelCase_    ,							UpperCAmelCase_						)
	torch_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
	_a  :  int=				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  :  Dict=				parser.parse_args()
	_a  :  List[Any]=				{}
	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 	UpperCamelCase    (					unittest.TestCase		):



				def       _lowercase  (self       :   List[Any])					->			Dict:
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							Optional[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Any   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							str   	=				tokenizer.decode(greedy_ids[0])

					with CaptureStdout() as cs:
						__snake_case    :							List[str]   	=				TextStreamer(_A)
						model.generate(_A			,						max_new_tokens=10			,						do_sample=_A			,						streamer=_A)
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					__snake_case    :							Optional[Any]   	=				cs.out[:-1]

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   int)					->			Optional[Any]:
					__snake_case    :							Any   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							List[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Optional[int]   	=				-1

					__snake_case    :							str   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							int   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							int   	=				tokenizer.decode(greedy_ids[0])

					__snake_case    :							int   	=				TextIteratorStreamer(_A)
					__snake_case    :							List[Any]   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							Any   	=				Thread(target=model.generate			,						kwargs=_A)
					thread.start()
					__snake_case    :							Dict   	=				''
					for new_text in streamer:
						streamer_text += new_text

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   Any)					->			List[str]:
					__snake_case    :							List[str]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Any   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Optional[int]   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							Union[str, Any]   	=				greedy_ids[:, input_ids.shape[1] :]
					__snake_case    :							Optional[Any]   	=				tokenizer.decode(new_greedy_ids[0])

					with CaptureStdout() as cs:
						__snake_case    :							str   	=				TextStreamer(_A			,						skip_prompt=_A)
						model.generate(_A			,						max_new_tokens=10			,						do_sample=_A			,						streamer=_A)
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					__snake_case    :							List[str]   	=				cs.out[:-1]

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   List[str])					->			Dict:
					# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
					# with actual models -- the dummy models' tokenizers are not aligned with their models, and
					# `skip_special_tokens=True` has no effect on them
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('distilgpt2')
					__snake_case    :							Dict   	=				AutoModelForCausalLM.from_pretrained('distilgpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[str]   	=				torch.ones((1, 5)			,						device=_A).long() * model.config.bos_token_id
					with CaptureStdout() as cs:
						__snake_case    :							Tuple   	=				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
					__snake_case    :							Tuple   	=				cs.out[:-1]  # Remove the final "\n"
					__snake_case    :							int   	=				tokenizer(_A			,						return_tensors='pt')
					self.assertEqual(streamer_text_tokenized.input_ids.shape			,						(1, 1))






				def       _lowercase  (self       :   Optional[int])					->			List[str]:
					__snake_case    :							Optional[int]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[int]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							int   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							List[Any]   	=				TextIteratorStreamer(_A			,						timeout=0.001)
					__snake_case    :							str   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							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):
						__snake_case    :							Any   	=				''
						for new_text in streamer:
							streamer_text += new_text





