

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 	lowercase    (				_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  BarthezTokenizer
		a__					       =  BarthezTokenizerFast
		a__					       =  True
		a__					       =  True


		def 		A__					(				self):
			super().setUp()

			_UpperCamelCase   :		Tuple			=						BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez')
			tokenizer.save_pretrained(self.tmpdirname)
			tokenizer.save_pretrained(self.tmpdirname	,       legacy_format=__snake_case)
			_UpperCamelCase   :		Optional[Any]			=						tokenizer


		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						'<pad>'
			_UpperCamelCase   :		Any			=						1

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


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						list(self.get_tokenizer().get_vocab().keys())

			self.assertEqual(vocab_keys[0]	,       '<s>')
			self.assertEqual(vocab_keys[1]	,       '<pad>')
			self.assertEqual(vocab_keys[-1]	,       '<mask>')
			self.assertEqual(len(__snake_case)	,       10_11_22)


		def 		A__					(				self):
			self.assertEqual(self.get_tokenizer().vocab_size	,       10_11_22)


		@require_torch
		def 		A__					(				self):
			_UpperCamelCase   :		str			=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
			_UpperCamelCase   :		Tuple			=						[0, 57, 30_18, 7_03_07, 91, 2]

			_UpperCamelCase   :		str			=						self.tokenizer(
			    __snake_case	,       max_length=len(__snake_case)	,       padding=__snake_case	,       truncation=__snake_case	,       return_tensors='pt')
			self.assertIsInstance(__snake_case	,       __snake_case)

			self.assertEqual((2, 6)	,       batch.input_ids.shape)
			self.assertEqual((2, 6)	,       batch.attention_mask.shape)
			_UpperCamelCase   :		Optional[int]			=						batch.input_ids.tolist()[0]
			self.assertListEqual(__snake_case	,       __snake_case)


		def 		A__					(				self):
			if not self.test_rust_tokenizer:
				return

			_UpperCamelCase   :		Union[str, Any]			=						self.get_tokenizer()
			_UpperCamelCase   :		Dict			=						self.get_rust_tokenizer()

			_UpperCamelCase   :		Optional[int]			=						'I was born in 92000, and this is falsé.'

			_UpperCamelCase   :		int			=						tokenizer.tokenize(__snake_case)
			_UpperCamelCase   :		str			=						rust_tokenizer.tokenize(__snake_case)
			self.assertListEqual(__snake_case	,       __snake_case)

			_UpperCamelCase   :		Optional[int]			=						tokenizer.encode(__snake_case	,       add_special_tokens=__snake_case)
			_UpperCamelCase   :		str			=						rust_tokenizer.encode(__snake_case	,       add_special_tokens=__snake_case)
			self.assertListEqual(__snake_case	,       __snake_case)

			_UpperCamelCase   :		List[str]			=						self.get_rust_tokenizer()
			_UpperCamelCase   :		Any			=						tokenizer.encode(__snake_case)
			_UpperCamelCase   :		Optional[int]			=						rust_tokenizer.encode(__snake_case)
			self.assertListEqual(__snake_case	,       __snake_case)






		@slow
		def 		A__					(				self):
			# fmt: off
			_UpperCamelCase   :		Union[str, Any]			=						{'input_ids': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 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, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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

			# moussaKam/mbarthez is a french model. So we also use french texts.
			_UpperCamelCase   :		Optional[int]			=						[
			    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
			    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
			    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
			    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
			    'telles que la traduction et la synthèse de texte.',
			]

			self.tokenizer_integration_test_util(
			    expected_encoding=__snake_case	,       model_name='moussaKam/mbarthez'	,       revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'	,       sequences=__snake_case	,       )







import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		[
    ["""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 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]	)						->				Optional[int]:




	'''simple docstring'''




	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		_UpperCamelCase   :		List[Any]			=						k.replace(UpperCAmelCase_							,       UpperCAmelCase_	)

	if k.startswith('encoder'	):
		_UpperCamelCase   :		Optional[Any]			=						k.replace('.attn'							,       '.self_attn'	)
		_UpperCamelCase   :		Optional[int]			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'final_layer_norm'	)
	elif k.startswith('decoder'	):
		_UpperCamelCase   :		Any			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'encoder_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm3'							,       'final_layer_norm'	)
	return k






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		_UpperCamelCase   :		Optional[int]			=						sd.pop(UpperCAmelCase_	)
		_UpperCamelCase   :		str			=						k.replace('layernorm_embedding'							,       'layer_norm'	)
		assert new_k not in sd
		_UpperCamelCase   :		Tuple			=						v


lowerCAmelCase__								=		["""START"""]






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Any	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)
	_UpperCamelCase   :		int			=						model['model']
	_UpperCamelCase   :		List[Any]			=						BlenderbotConfig.from_json_file(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						BlenderbotForConditionalGeneration(UpperCAmelCase_	)
	_UpperCamelCase   :		int			=						m.model.state_dict().keys()
	_UpperCamelCase   :		Union[str, Any]			=						[]
	_UpperCamelCase   :		int			=						{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		_UpperCamelCase   :		Optional[int]			=						rename_state_dict_key(UpperCAmelCase_	)
		if new_k not in valid_keys:
			failures.append([k, new_k]	)
		else:
			_UpperCamelCase   :		int			=						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__":
							lowerCAmelCase__								=		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"""
							)
							lowerCAmelCase__								=		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)




