






from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								return [
								    {"col_1": 3, "col_2": "a"},
								    {"col_1": 2, "col_2": "b"},
								    {"col_1": 1, "col_2": "c"},
								    {"col_1": 0, "col_2": "d"},
								]




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
								return Dataset.from_dict(lowerCamelCase    )




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     self._create_example_records()
								lowercase__  					=     Dataset.from_list(lowerCamelCase    )
								self.assertListEqual(dset.column_names,    ['''col_1''', '''col_2''']    )
								for i, r in enumerate(lowerCamelCase    ):
												self.assertDictEqual(lowerCamelCase,    example_records[i]    )




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     self._create_example_records()
								lowercase__  					=     Dataset.from_list(lowerCamelCase    )
								lowercase__  					=     Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}    )
								self.assertEqual(dset.info,    dset_from_dict.info    )




				def 							lowercase__  (	self		:       int    ):  # checks what happens with missing columns

								'''simple docstring'''
								lowercase__  					=     [{'''col_1''': 1}, {'''col_2''': '''x'''}]
								lowercase__  					=     Dataset.from_list(lowerCamelCase    )
								self.assertDictEqual(dset[0],    {'''col_1''': 1}    )
								self.assertDictEqual(dset[1],    {'''col_1''': None}    )  # NB: first record is used for columns




				def 							lowercase__  (	self		:       int    ):  # checks if the type can be inferred from the second record

								'''simple docstring'''
								lowercase__  					=     [{'''col_1''': []}, {'''col_1''': [1, 2]}]
								lowercase__  					=     Dataset.from_list(lowerCamelCase    )
								self.assertEqual(dset.info.features['''col_1'''],    Sequence(Value('''int64'''    )    )    )




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     Dataset.from_list([]    )
								self.assertEqual(len(lowerCamelCase    ),    0    )
								self.assertListEqual(dset.column_names,    []    )












import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

A__      :     List[str]       =					(
    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__      :     List[str]       =					(
    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__      :     Dict       =					[
    '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_							):






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






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






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

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

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






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






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






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


if __name__ == "__main__":
							A__      :     Optional[Any]       =					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__      :     Tuple       =					parser.parse_args()
							A__      :     int       =					{}
							convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)




