


'''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


__SCREAMING_SNAKE_CASE					:				int									=		get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class 					__lowerCAmelCase   (   lowercase				,			unittest.TestCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Union[str, Any]   						=XGLMTokenizer
						_UpperCAmelCase		:       List[Any]   						=XGLMTokenizerFast
						_UpperCAmelCase		:       Tuple   						=True
						_UpperCAmelCase		:       Union[str, Any]   						=True






						def 				_UpperCAmelCase       (  self							:      int	):
													super().setUp()

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






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		"<pad>"
													A_			    =		1

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






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		list(self.get_tokenizer().get_vocab().keys()	)

													self.assertEqual(vocab_keys[0] ,  "<s>"	)
													self.assertEqual(vocab_keys[1] ,  "<pad>"	)
													self.assertEqual(len(lowerCAmelCase	) ,  10_08	)






						def 				_UpperCAmelCase       (  self							:      Dict	):
													self.assertEqual(self.get_tokenizer().vocab_size ,  10_08	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		XGLMTokenizer(lowerCAmelCase ,  keep_accents=lowerCAmelCase	)

													A_			    =		tokenizer.tokenize("This is a test"	)
													self.assertListEqual(lowerCAmelCase ,  ["▁This", "▁is", "▁a", "▁t", "est"]	)

													self.assertListEqual(
													    tokenizer.convert_tokens_to_ids(lowerCAmelCase	) ,  [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,  )

													A_			    =		tokenizer.tokenize("I was born in 92000, and this is falsé."	)
													self.assertListEqual(
													    lowerCAmelCase ,  [
													        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",
													        ".",
													    ] ,  )
													A_			    =		tokenizer.convert_tokens_to_ids(lowerCAmelCase	)
													self.assertListEqual(
													    lowerCAmelCase ,  [
													        value + tokenizer.fairseq_offset
													        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
													    ] ,  )

													A_			    =		tokenizer.convert_ids_to_tokens(lowerCAmelCase	)
													self.assertListEqual(
													    lowerCAmelCase ,  [
													        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 				_UpperCAmelCase       (  self							:      Optional[int]	):
													return XGLMTokenizer.from_pretrained("facebook/xglm-564M"	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													with tempfile.NamedTemporaryFile() as f:
																				shutil.copyfile(lowerCAmelCase ,  f.name	)
																				A_			    =		XGLMTokenizer(f.name ,  keep_accents=lowerCAmelCase	)
																				A_			    =		pickle.dumps(lowerCAmelCase	)
													pickle.loads(lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													if not self.test_rust_tokenizer:
																				return

													A_			    =		self.get_tokenizer()
													A_			    =		self.get_rust_tokenizer()

													A_			    =		"I was born in 92000, and this is falsé."

													A_			    =		tokenizer.tokenize(lowerCAmelCase	)
													A_			    =		rust_tokenizer.tokenize(lowerCAmelCase	)
													self.assertListEqual(lowerCAmelCase ,  lowerCAmelCase	)

													A_			    =		tokenizer.encode(lowerCAmelCase ,  add_special_tokens=lowerCAmelCase	)
													A_			    =		rust_tokenizer.encode(lowerCAmelCase ,  add_special_tokens=lowerCAmelCase	)
													self.assertListEqual(lowerCAmelCase ,  lowerCAmelCase	)

													A_			    =		self.get_rust_tokenizer()
													A_			    =		tokenizer.encode(lowerCAmelCase	)
													A_			    =		rust_tokenizer.encode(lowerCAmelCase	)
													self.assertListEqual(lowerCAmelCase ,  lowerCAmelCase	)






						@slow
						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		"Hello World!"
													A_			    =		[2, 3_12_27, 44_47, 35]

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






						@slow
						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		(
													    "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
													A_			    =		[2, 10_18, 67, 11, 19_88, 26_17, 56_31, 2_78, 11, 34_07, 48, 7_16_30, 2_80_85, 4, 32_34, 1_57, 13, 6, 5, 6, 4, 35_26, 7_68, 15, 6_59, 57, 2_98, 39_83, 8_64, 1_29, 21, 6, 5, 1_36_75, 3_77, 6_52, 75_80, 1_03_41, 1_55, 28_17, 4_22, 16_66, 7, 16_74, 53, 1_13, 20_22_77, 1_78_92, 33, 60, 87, 4, 32_34, 1_57, 61, 26_67, 5_23_76, 19, 88, 23, 7_35]
													# fmt: on

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






						@slow
						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													# fmt: off
													A_			    =		{
													    "input_ids": [[2, 10_88_25, 11_63, 15, 8_80_10, 4_73, 1_58_98, 1_57, 1_36_72, 18_57, 3_12, 8, 23_80_21, 11_63, 53, 1_36_72, 18_57, 3_12, 8, 5_32_83, 18_23_96, 8, 1_85_66, 16, 3_67_33, 41_01, 8, 2_30, 24_40_17, 12_25_53, 7, 15, 13_25_97, 4, 2_93, 1_25_11, 76_10, 4, 34_14, 13_25_97, 9, 4, 3_23_61, 3_62, 4, 7_34, 2_85_12, 3_25_69, 18, 4, 3_23_61, 2_60_96, 1_49_82, 73, 1_87_15, 2_14_33, 23_52_61, 15, 4_92, 1_24_27, 16, 53, 1_87_15, 2_14_33, 6_54_54, 15, 2_36_59, 5_63, 16, 2_78, 5_97, 28_43, 5_95, 79_31, 18_23_96, 6_41_86, 22, 8_86, 5_95, 13_29_81, 53, 2_55_40, 34_49, 4_39_82, 3_99_01, 59_51, 8_78, 3_30, 4, 2_76_94, 8_02_69, 3_12, 53, 65_17, 1_17_80, 6_11, 2_04_08, 5], [2, 6, 13_25_97, 67, 4_28_97, 33, 5_92, 8, 16_37_29, 2_55_40, 3_61, 13_69_97, 10_95_14, 17_32_30, 7, 5_01, 60, 10_29_13, 1_96, 56_31, 2_35, 6_32_43, 4_73, 6, 23_17_57, 74, 52_77, 79_05, 53, 30_95, 3_73_17, 22, 4_54, 18_38_74, 5], [2, 2_68, 3_12_98, 4_65_30, 6, 13_29_35, 4_38_31, 7, 5_97, 32, 24, 36_88, 98_65, 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=lowerCAmelCase ,  model_name="facebook/xglm-564M" ,  padding=lowerCAmelCase ,  )









'''simple docstring'''
import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE					:				List[Any]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Tuple									=		[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]







def       a_				(	UpperCamelCase_ ):
							if k == "embeddings.weight":
														return "shared.weight"

							for parlai_name, hf_name in PATTERNS:
														A_			    =		k.replace(UpperCamelCase_						,  UpperCamelCase_ )

							if k.startswith("encoder" ):
														A_			    =		k.replace(".attn"						,  ".self_attn" )
														A_			    =		k.replace("norm1"						,  "self_attn_layer_norm" )
														A_			    =		k.replace("norm2"						,  "final_layer_norm" )
							elif k.startswith("decoder" ):
														A_			    =		k.replace("norm1"						,  "self_attn_layer_norm" )
														A_			    =		k.replace("norm2"						,  "encoder_attn_layer_norm" )
														A_			    =		k.replace("norm3"						,  "final_layer_norm" )
							return k







def       a_				(	UpperCamelCase_ ):
							A_			    =		[
							    "model.encoder.layernorm_embedding.weight",
							    "model.encoder.layernorm_embedding.bias",
							    "model.decoder.layernorm_embedding.weight",
							    "model.decoder.layernorm_embedding.bias",
							]
							for k in keys:
														A_			    =		sd.pop(UpperCamelCase_ )
														A_			    =		k.replace("layernorm_embedding"						,  "layer_norm" )
														assert new_k not in sd
														A_			    =		v


__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		['''START''']







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		torch.load(UpperCamelCase_						,  map_location="cpu" )
							A_			    =		model["model"]
							A_			    =		BlenderbotConfig.from_json_file(UpperCamelCase_ )
							A_			    =		BlenderbotForConditionalGeneration(UpperCamelCase_ )
							A_			    =		m.model.state_dict().keys()
							A_			    =		[]
							A_			    =		{}
							for k, v in sd.items():
														if k in IGNORE_KEYS:
																					continue

														A_			    =		rename_state_dict_key(UpperCamelCase_ )
														if new_k not in valid_keys:
																					failures.append([k, new_k] )
														else:
																					A_			    =		v
							if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
														rename_layernorm_keys(UpperCamelCase_ )
							m.model.load_state_dict(UpperCamelCase_						,  strict=UpperCamelCase_ )
							m.half()
							m.save_pretrained(UpperCamelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
							parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
							parser.add_argument(
							    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
							)
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)





