



"""simple docstring"""


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__A   :							List[str]										=   get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class 			_a (					lowerCAmelCase			,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	XGLMTokenizer
			UpperCamelCase__			      =	XGLMTokenizerFast
			UpperCamelCase__			      =	True
			UpperCamelCase__			      =	True



			def  lowercase__						(		self     :    Union[str, Any]   )->Tuple:
							super().setUp()

							# We have a SentencePiece fixture for testing
							_UpperCAmelCase					=    XGLMTokenizer(__UpperCamelCase				,   keep_accents=__UpperCamelCase   )
							tokenizer.save_pretrained(self.tmpdirname   )



			def  lowercase__						(		self     :    Any   )->Tuple:
							_UpperCAmelCase					=    '''<pad>'''
							_UpperCAmelCase					=    1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase   )				,   __UpperCamelCase   )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase   )				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Dict   )->Optional[Any]:
							_UpperCAmelCase					=    list(self.get_tokenizer().get_vocab().keys()   )

							self.assertEqual(vocab_keys[0]				,   '''<s>'''   )
							self.assertEqual(vocab_keys[1]				,   '''<pad>'''   )
							self.assertEqual(len(__UpperCamelCase   )				,   1_0_0_8   )



			def  lowercase__						(		self     :    Tuple   )->Optional[Any]:
							self.assertEqual(self.get_tokenizer().vocab_size				,   1_0_0_8   )



			def  lowercase__						(		self     :    Dict   )->Dict:
							_UpperCAmelCase					=    XGLMTokenizer(__UpperCamelCase				,   keep_accents=__UpperCamelCase   )

							_UpperCAmelCase					=    tokenizer.tokenize('''This is a test'''   )
							self.assertListEqual(__UpperCamelCase				,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(__UpperCamelCase   )				,   [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]				,   )

							_UpperCAmelCase					=    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
							self.assertListEqual(
							    __UpperCamelCase				,   [
							        SPIECE_UNDERLINE + '''I''',
							        SPIECE_UNDERLINE + '''was''',
							        SPIECE_UNDERLINE + '''b''',
							        '''or''',
							        '''n''',
							        SPIECE_UNDERLINE + '''in''',
							        SPIECE_UNDERLINE + '''''',
							        '''9''',
							        '''2''',
							        '''0''',
							        '''0''',
							        '''0''',
							        ''',''',
							        SPIECE_UNDERLINE + '''and''',
							        SPIECE_UNDERLINE + '''this''',
							        SPIECE_UNDERLINE + '''is''',
							        SPIECE_UNDERLINE + '''f''',
							        '''al''',
							        '''s''',
							        '''é''',
							        '''.''',
							    ]				,   )
							_UpperCAmelCase					=    tokenizer.convert_tokens_to_ids(__UpperCamelCase   )
							self.assertListEqual(
							    __UpperCamelCase				,   [
							        value + tokenizer.fairseq_offset
							        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
							    ]				,   )

							_UpperCAmelCase					=    tokenizer.convert_ids_to_tokens(__UpperCamelCase   )
							self.assertListEqual(
							    __UpperCamelCase				,   [
							        SPIECE_UNDERLINE + '''I''',
							        SPIECE_UNDERLINE + '''was''',
							        SPIECE_UNDERLINE + '''b''',
							        '''or''',
							        '''n''',
							        SPIECE_UNDERLINE + '''in''',
							        SPIECE_UNDERLINE + '''''',
							        '''<unk>''',
							        '''2''',
							        '''0''',
							        '''0''',
							        '''0''',
							        ''',''',
							        SPIECE_UNDERLINE + '''and''',
							        SPIECE_UNDERLINE + '''this''',
							        SPIECE_UNDERLINE + '''is''',
							        SPIECE_UNDERLINE + '''f''',
							        '''al''',
							        '''s''',
							        '''<unk>''',
							        '''.''',
							    ]				,   )



			@cached_property
			def  lowercase__						(		self     :    str   )->Any:
							return XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''   )



			def  lowercase__						(		self     :    List[str]   )->int:
							with tempfile.NamedTemporaryFile() as f:
											shutil.copyfile(__UpperCamelCase				,   f.name   )
											_UpperCAmelCase					=    XGLMTokenizer(f.name				,   keep_accents=__UpperCamelCase   )
											_UpperCAmelCase					=    pickle.dumps(__UpperCamelCase   )
							pickle.loads(__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[Any]   )->Any:
							if not self.test_rust_tokenizer:
											return

							_UpperCAmelCase					=    self.get_tokenizer()
							_UpperCAmelCase					=    self.get_rust_tokenizer()

							_UpperCAmelCase					=    '''I was born in 92000, and this is falsé.'''

							_UpperCAmelCase					=    tokenizer.tokenize(__UpperCamelCase   )
							_UpperCAmelCase					=    rust_tokenizer.tokenize(__UpperCamelCase   )
							self.assertListEqual(__UpperCamelCase				,   __UpperCamelCase   )

							_UpperCAmelCase					=    tokenizer.encode(__UpperCamelCase				,   add_special_tokens=__UpperCamelCase   )
							_UpperCAmelCase					=    rust_tokenizer.encode(__UpperCamelCase				,   add_special_tokens=__UpperCamelCase   )
							self.assertListEqual(__UpperCamelCase				,   __UpperCamelCase   )

							_UpperCAmelCase					=    self.get_rust_tokenizer()
							_UpperCAmelCase					=    tokenizer.encode(__UpperCamelCase   )
							_UpperCAmelCase					=    rust_tokenizer.encode(__UpperCamelCase   )
							self.assertListEqual(__UpperCamelCase				,   __UpperCamelCase   )



			@slow
			def  lowercase__						(		self     :    Optional[int]   )->Union[str, Any]:
							_UpperCAmelCase					=    '''Hello World!'''
							_UpperCAmelCase					=    [2, 3_1_2_2_7, 4_4_4_7, 3_5]

							self.assertListEqual(__UpperCamelCase				,   self.big_tokenizer.encode(__UpperCamelCase   )   )



			@slow
			def  lowercase__						(		self     :    List[Any]   )->Any:
							_UpperCAmelCase					=    (
							    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
							    ''' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'''
							)
							# fmt: off
							_UpperCAmelCase					=    [2, 1_0_1_8, 6_7, 1_1, 1_9_8_8, 2_6_1_7, 5_6_3_1, 2_7_8, 1_1, 3_4_0_7, 4_8, 7_1_6_3_0, 2_8_0_8_5, 4, 3_2_3_4, 1_5_7, 1_3, 6, 5, 6, 4, 3_5_2_6, 7_6_8, 1_5, 6_5_9, 5_7, 2_9_8, 3_9_8_3, 8_6_4, 1_2_9, 2_1, 6, 5, 1_3_6_7_5, 3_7_7, 6_5_2, 7_5_8_0, 1_0_3_4_1, 1_5_5, 2_8_1_7, 4_2_2, 1_6_6_6, 7, 1_6_7_4, 5_3, 1_1_3, 2_0_2_2_7_7, 1_7_8_9_2, 3_3, 6_0, 8_7, 4, 3_2_3_4, 1_5_7, 6_1, 2_6_6_7, 5_2_3_7_6, 1_9, 8_8, 2_3, 7_3_5]
							# fmt: on

							self.assertListEqual(__UpperCamelCase				,   self.big_tokenizer.encode(__UpperCamelCase   )   )



			@slow
			def  lowercase__						(		self     :    Dict   )->Optional[int]:
							# fmt: off
							_UpperCAmelCase					=    {
							    '''input_ids''': [[2, 1_0_8_8_2_5, 1_1_6_3, 1_5, 8_8_0_1_0, 4_7_3, 1_5_8_9_8, 1_5_7, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 2_3_8_0_2_1, 1_1_6_3, 5_3, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 5_3_2_8_3, 1_8_2_3_9_6, 8, 1_8_5_6_6, 1_6, 3_6_7_3_3, 4_1_0_1, 8, 2_3_0, 2_4_4_0_1_7, 1_2_2_5_5_3, 7, 1_5, 1_3_2_5_9_7, 4, 2_9_3, 1_2_5_1_1, 7_6_1_0, 4, 3_4_1_4, 1_3_2_5_9_7, 9, 4, 3_2_3_6_1, 3_6_2, 4, 7_3_4, 2_8_5_1_2, 3_2_5_6_9, 1_8, 4, 3_2_3_6_1, 2_6_0_9_6, 1_4_9_8_2, 7_3, 1_8_7_1_5, 2_1_4_3_3, 2_3_5_2_6_1, 1_5, 4_9_2, 1_2_4_2_7, 1_6, 5_3, 1_8_7_1_5, 2_1_4_3_3, 6_5_4_5_4, 1_5, 2_3_6_5_9, 5_6_3, 1_6, 2_7_8, 5_9_7, 2_8_4_3, 5_9_5, 7_9_3_1, 1_8_2_3_9_6, 6_4_1_8_6, 2_2, 8_8_6, 5_9_5, 1_3_2_9_8_1, 5_3, 2_5_5_4_0, 3_4_4_9, 4_3_9_8_2, 3_9_9_0_1, 5_9_5_1, 8_7_8, 3_3_0, 4, 2_7_6_9_4, 8_0_2_6_9, 3_1_2, 5_3, 6_5_1_7, 1_1_7_8_0, 6_1_1, 2_0_4_0_8, 5], [2, 6, 1_3_2_5_9_7, 6_7, 4_2_8_9_7, 3_3, 5_9_2, 8, 1_6_3_7_2_9, 2_5_5_4_0, 3_6_1, 1_3_6_9_9_7, 1_0_9_5_1_4, 1_7_3_2_3_0, 7, 5_0_1, 6_0, 1_0_2_9_1_3, 1_9_6, 5_6_3_1, 2_3_5, 6_3_2_4_3, 4_7_3, 6, 2_3_1_7_5_7, 7_4, 5_2_7_7, 7_9_0_5, 5_3, 3_0_9_5, 3_7_3_1_7, 2_2, 4_5_4, 1_8_3_8_7_4, 5], [2, 2_6_8, 3_1_2_9_8, 4_6_5_3_0, 6, 1_3_2_9_3_5, 4_3_8_3_1, 7, 5_9_7, 3_2, 2_4, 3_6_8_8, 9_8_6_5, 5]],
							    '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
							}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=__UpperCamelCase				,   model_name='''facebook/xglm-564M'''				,   padding=__UpperCamelCase				,   )





"""simple docstring"""


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A   :							Tuple										=   (
    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   :							Optional[int]										=   (
    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   :							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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						dict					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE				)
				_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(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    DECODER_PATTERNS
								_UpperCAmelCase					=    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''']				):
												_UpperCAmelCase					=    v.T
								_UpperCAmelCase					=    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'''				):
								_UpperCAmelCase					=    [k.endswith(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    REMAINING_PATTERNS
								_UpperCAmelCase					=    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''']				):
												_UpperCAmelCase					=    v.T
								_UpperCAmelCase					=    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}'

				_UpperCAmelCase					=    mapping['''model.embed_positions.weight''']
				_UpperCAmelCase					=    mapping.pop('''model.embed_positions.weight'''				)
				_UpperCAmelCase		,						_UpperCAmelCase					=    torch_model.load_state_dict(_SCREAMING_SNAKE_CASE					,     strict=_SCREAMING_SNAKE_CASE				)
				_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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				_UpperCAmelCase					=    tf.train.list_variables(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    {}
				_UpperCAmelCase					=    ['''global_step''']
				for name, shape in tqdm(_SCREAMING_SNAKE_CASE					,     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(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    array
				return tf_weights







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				torch_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							Tuple										=   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   :							Tuple										=   {}
					convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)



