


import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a			:			Optional[int]												=					'''▁'''

a			:			List[Any]												=					get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class 						lowerCamelCase_							(					lowercase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					BertGenerationTokenizer
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					True






					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								super().setUp()

								__lowercase			       = BertGenerationTokenizer(UpperCAmelCase__			,							keep_accents=UpperCAmelCase__    )
								tokenizer.save_pretrained(self.tmpdirname    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = '''<s>'''
								__lowercase			       = 1

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






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = list(self.get_tokenizer().get_vocab().keys()    )

								self.assertEqual(vocab_keys[0]			,							'''<unk>'''    )
								self.assertEqual(vocab_keys[1]			,							'''<s>'''    )
								self.assertEqual(vocab_keys[-1]			,							'''<pad>'''    )
								self.assertEqual(len(UpperCAmelCase__    )			,							1_0_0_2    )






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								self.assertEqual(self.get_tokenizer().vocab_size			,							1_0_0_0    )






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = BertGenerationTokenizer(UpperCAmelCase__			,							keep_accents=UpperCAmelCase__    )

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

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCAmelCase__    )			,							[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]			,							)

								__lowercase			       = 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''',
								        '''.''',
								    ]			,							)
								__lowercase			       = tokenizer.convert_tokens_to_ids(UpperCAmelCase__    )
								self.assertListEqual(
								    UpperCAmelCase__			,							[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]			,							)

								__lowercase			       = 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 A   (					self    ) ->      Dict:







								'''simple docstring'''

								return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )






					@slow
					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								__lowercase			       = '''Hello World!'''
								__lowercase			       = [1_8_5_3_6, 2_2_6_0, 1_0_1]

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






					@slow
					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = (
								    '''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'''
								)
								__lowercase			       = [
								    8_7_1,
								    4_1_9,
								    3_5_8,
								    9_4_6,
								    9_9_1,
								    2_5_2_1,
								    4_5_2,
								    3_5_8,
								    1_3_5_7,
								    3_8_7,
								    7_7_5_1,
								    3_5_3_6,
								    1_1_2,
								    9_8_5,
								    4_5_6,
								    1_2_6,
								    8_6_5,
								    9_3_8,
								    5_4_0_0,
								    5_7_3_4,
								    4_5_8,
								    1_3_6_8,
								    4_6_7,
								    7_8_6,
								    2_4_6_2,
								    5_2_4_6,
								    1_1_5_9,
								    6_3_3,
								    8_6_5,
								    4_5_1_9,
								    4_5_7,
								    5_8_2,
								    8_5_2,
								    2_5_5_7,
								    4_2_7,
								    9_1_6,
								    5_0_8,
								    4_0_5,
								    3_4_3_2_4,
								    4_9_7,
								    3_9_1,
								    4_0_8,
								    1_1_3_4_2,
								    1_2_4_4,
								    3_8_5,
								    1_0_0,
								    9_3_8,
								    9_8_5,
								    4_5_6,
								    5_7_4,
								    3_6_2,
								    1_2_5_9_7,
								    3_2_0_0,
								    3_1_2_9,
								    1_1_7_2,
								]

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






					@require_torch
					@slow
					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								import torch

								from transformers import BertGenerationConfig, BertGenerationEncoder

								# Build sequence
								__lowercase			       = list(self.big_tokenizer.get_vocab().keys()    )[:1_0]
								__lowercase			       = ''' '''.join(UpperCAmelCase__    )
								__lowercase			       = self.big_tokenizer.encode_plus(UpperCAmelCase__			,							return_tensors='''pt'''			,							return_token_type_ids=UpperCAmelCase__    )
								__lowercase			       = self.big_tokenizer.batch_encode_plus(
								    [sequence + ''' ''' + sequence]			,							return_tensors='''pt'''			,							return_token_type_ids=UpperCAmelCase__    )

								__lowercase			       = BertGenerationConfig()
								__lowercase			       = BertGenerationEncoder(UpperCAmelCase__    )

								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

								with torch.no_grad():
											model(**UpperCAmelCase__    )
											model(**UpperCAmelCase__    )






					@slow
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = {'''input_ids''': [[3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4], [4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=UpperCAmelCase__			,							model_name='''google/bert_for_seq_generation_L-24_bbc_encoder'''			,							revision='''c817d1fd1be2ffa69431227a1fe320544943d4db'''			,							)





'''simple docstring'''





import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase_    						=       logging.get_logger(__name__)






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]						)				->  Dict:
						lowercase :							List[str]    		=R'''\w+[.]\d+'''
						lowercase :							List[str]    		=re.findall(__magic_name__   , __magic_name__						)
						for pat in pats:
												lowercase :							Optional[int]    		=key.replace(__magic_name__   , '''_'''.join(pat.split('''.'''						)						)						)
						return key






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Dict						)				->  str:
						lowercase :							Dict    		=pt_tuple_key[:-1] + ('''scale''',)
						if (
						    any('''norm''' in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												lowercase :							str    		=pt_tuple_key[:-1] + ('''scale''',)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												lowercase :							str    		=pt_tuple_key[:-1] + ('''scale''',)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												lowercase :							Dict    		=pt_tuple_key[:-1] + ('''embedding''',)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						lowercase :							Tuple    		=pt_tuple_key[:-1] + ('''kernel''',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												lowercase :							Tuple    		=pt_tensor.transpose(2   , 3   , 1   , 0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						lowercase :							str    		=pt_tuple_key[:-1] + ('''kernel''',)
						if pt_tuple_key[-1] == "weight":
												lowercase :							Optional[Any]    		=pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						lowercase :							Dict    		=pt_tuple_key[:-1] + ('''weight''',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						lowercase :							Union[str, Any]    		=pt_tuple_key[:-1] + ('''bias''',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor







def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Union[str, Any]=42						)				->  List[str]:
						# Step 1: Convert pytorch tensor to numpy
						lowercase :							Optional[Any]    		={k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						lowercase :							str    		=flax_model.init_weights(PRNGKey(__magic_name__						)						)

						lowercase :							Dict    		=flatten_dict(__magic_name__						)
						lowercase :							Dict    		={}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												lowercase :							Dict    		=rename_key(__magic_name__						)
												lowercase :							Optional[int]    		=tuple(renamed_pt_key.split('''.'''						)						)

												# Correctly rename weight parameters
												lowercase		,	lowercase :							Any    		=rename_key_and_reshape_tensor(__magic_name__   , __magic_name__   , __magic_name__						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																								    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)

        # also add unexpected weight so that warning is thrown
												lowercase :							Tuple    		=jnp.asarray(__magic_name__						)

						return unflatten_dict(__magic_name__						)





