"""simple docstring"""






import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCAmelCase__  :  int						     =	(
    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__  :  Dict						     =	(
    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__  :  Union[str, 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   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				for tf_name, hf_name in patterns:
								UpperCAmelCase__             =							k.replace(lowerCamelCase					,			lowerCamelCase       )
				return k



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							BigBirdPegasusConfig(**lowerCamelCase       )
				UpperCAmelCase__             =							BigBirdPegasusForConditionalGeneration(lowerCamelCase       )
				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(lowerCamelCase       ) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase       ):
												continue
								UpperCAmelCase__             =							DECODER_PATTERNS
								UpperCAmelCase__             =							rename_state_dict_key(lowerCamelCase					,			lowerCamelCase       )
								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(lowerCamelCase       )
								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(lowerCamelCase       ) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase       ):
												continue
								UpperCAmelCase__             =							REMAINING_PATTERNS
								UpperCAmelCase__             =							rename_state_dict_key(lowerCamelCase					,			lowerCamelCase       )
								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(lowerCamelCase       )
								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(lowerCamelCase					,			strict=lowerCamelCase       )
				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   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							tf.train.list_variables(lowerCamelCase       )
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							['global_step']
				for name, shape in tqdm(lowerCamelCase					,			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(lowerCamelCase					,			lowerCamelCase       )
								UpperCAmelCase__             =							array
				return tf_weights



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							get_tf_weights_as_numpy(lowerCamelCase       )
				UpperCAmelCase__             =							convert_bigbird_pegasus(lowerCamelCase					,			lowerCamelCase       )
				torch_model.save_pretrained(lowerCamelCase       )


if __name__ == "__main__":
						lowerCAmelCase__  :  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.')
						lowerCAmelCase__  :  str						     =	parser.parse_args()
						lowerCAmelCase__  :  int						     =	{}
						convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)




"""simple docstring"""






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__  :  Optional[Any]						     =	logging.getLogger()



def   a_						(					):
				UpperCAmelCase__             =							argparse.ArgumentParser()
				parser.add_argument('-f'       )
				UpperCAmelCase__             =							parser.parse_args()
				return args.f







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							logging.StreamHandler(sys.stdout			)
									logger.addHandler(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							get_gpu_count()

									if n_gpu > 1:
													pass
													# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
													# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
													# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
													# cmd = [sys.executable] + distributed_args + args
													# execute_subprocess_async(cmd, env=self.get_env())
													# XXX: test the results - need to save them first into .json file
									else:
													args.insert(0    ,'run_glue_deebert.py'			)
													with patch.object(lowerCamelCase__    ,'argv'    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							run_glue_deebert.main()
																	for value in result.values():
																					self.assertGreaterEqual(lowerCamelCase__    ,0.6_6_6			)







					@slow
					@require_torch_non_multi_gpu
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)



