

import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class 					__UpperCamelCase					(       a__	,    unittest.TestCase      ):
	_lowercase	: Dict												=	RealmTokenizer
	_lowercase	: Optional[Any]												=	RealmTokenizerFast
	_lowercase	: Any												=	True
	_lowercase	: Union[str, Any]												=	True
	_lowercase	: List[str]												=	filter_non_english







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						super().setUp()

						__magic_name__				=     [
						    '''[UNK]''',
						    '''[CLS]''',
						    '''[SEP]''',
						    '''[PAD]''',
						    '''[MASK]''',
						    '''want''',
						    '''##want''',
						    '''##ed''',
						    '''wa''',
						    '''un''',
						    '''runn''',
						    '''##ing''',
						    ''',''',
						    '''low''',
						    '''lowest''',
						]
						__magic_name__				=     os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['vocab_file']   )
						with open(self.vocab_file	,			'w'	,			encoding='utf-8'   ) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			Tuple   ):

						'''simple docstring'''





						__magic_name__				=     '''UNwant\u00E9d,running'''
						__magic_name__				=     '''unwanted, running'''
						return input_text, output_text







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.tokenizer_class(self.vocab_file   )

						__magic_name__				=     tokenizer.tokenize('UNwant\u00E9d,running'   )
						self.assertListEqual(_lowerCamelCase	,			['un', '##want', '##ed', ',', 'runn', '##ing']   )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase   )	,			[9, 6, 7, 12, 10, 11]   )







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''simple docstring'''





						if not self.test_rust_tokenizer:
											return

						__magic_name__				=     self.get_tokenizer()
						__magic_name__				=     self.get_rust_tokenizer()

						__magic_name__				=     '''UNwant\u00E9d,running'''

						__magic_name__				=     tokenizer.tokenize(_lowerCamelCase   )
						__magic_name__				=     rust_tokenizer.tokenize(_lowerCamelCase   )
						self.assertListEqual(_lowerCamelCase	,			_lowerCamelCase   )

						__magic_name__				=     tokenizer.encode(_lowerCamelCase	,			add_special_tokens=_lowerCamelCase   )
						__magic_name__				=     rust_tokenizer.encode(_lowerCamelCase	,			add_special_tokens=_lowerCamelCase   )
						self.assertListEqual(_lowerCamelCase	,			_lowerCamelCase   )

						__magic_name__				=     self.get_rust_tokenizer()
						__magic_name__				=     tokenizer.encode(_lowerCamelCase   )
						__magic_name__				=     rust_tokenizer.encode(_lowerCamelCase   )
						self.assertListEqual(_lowerCamelCase	,			_lowerCamelCase   )

						# With lower casing
						__magic_name__				=     self.get_tokenizer(do_lower_case=_lowerCamelCase   )
						__magic_name__				=     self.get_rust_tokenizer(do_lower_case=_lowerCamelCase   )

						__magic_name__				=     '''UNwant\u00E9d,running'''

						__magic_name__				=     tokenizer.tokenize(_lowerCamelCase   )
						__magic_name__				=     rust_tokenizer.tokenize(_lowerCamelCase   )
						self.assertListEqual(_lowerCamelCase	,			_lowerCamelCase   )

						__magic_name__				=     tokenizer.encode(_lowerCamelCase	,			add_special_tokens=_lowerCamelCase   )
						__magic_name__				=     rust_tokenizer.encode(_lowerCamelCase	,			add_special_tokens=_lowerCamelCase   )
						self.assertListEqual(_lowerCamelCase	,			_lowerCamelCase   )

						__magic_name__				=     self.get_rust_tokenizer()
						__magic_name__				=     tokenizer.encode(_lowerCamelCase   )
						__magic_name__				=     rust_tokenizer.encode(_lowerCamelCase   )
						self.assertListEqual(_lowerCamelCase	,			_lowerCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     BasicTokenizer()

						self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'   )	,			['ah', '\u535A', '\u63A8', 'zz']   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     BasicTokenizer(do_lower_case=_lowerCamelCase   )

						self.assertListEqual(
						    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '   )	,			['hello', '!', 'how', 'are', 'you', '?']   )
						self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )	,			['hello']   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     BasicTokenizer(do_lower_case=_lowerCamelCase	,			strip_accents=_lowerCamelCase   )

						self.assertListEqual(
						    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )	,			['hällo', '!', 'how', 'are', 'you', '?']   )
						self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )	,			['h\u00E9llo']   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





						__magic_name__				=     BasicTokenizer(do_lower_case=_lowerCamelCase	,			strip_accents=_lowerCamelCase   )

						self.assertListEqual(
						    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )	,			['hallo', '!', 'how', 'are', 'you', '?']   )
						self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )	,			['hello']   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						__magic_name__				=     BasicTokenizer(do_lower_case=_lowerCamelCase   )

						self.assertListEqual(
						    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )	,			['hallo', '!', 'how', 'are', 'you', '?']   )
						self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )	,			['hello']   )







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''simple docstring'''





						__magic_name__				=     BasicTokenizer(do_lower_case=_lowerCamelCase   )

						self.assertListEqual(
						    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '   )	,			['HeLLo', '!', 'how', 'Are', 'yoU', '?']   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





						__magic_name__				=     BasicTokenizer(do_lower_case=_lowerCamelCase	,			strip_accents=_lowerCamelCase   )

						self.assertListEqual(
						    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )	,			['HäLLo', '!', 'how', 'Are', 'yoU', '?']   )







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						__magic_name__				=     BasicTokenizer(do_lower_case=_lowerCamelCase	,			strip_accents=_lowerCamelCase   )

						self.assertListEqual(
						    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )	,			['HaLLo', '!', 'how', 'Are', 'yoU', '?']   )







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						__magic_name__				=     BasicTokenizer(do_lower_case=_lowerCamelCase	,			never_split=['[UNK]']   )

						self.assertListEqual(
						    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'   )	,			['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']   )







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						__magic_name__				=     ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

						__magic_name__				=     {}
						for i, token in enumerate(_lowerCamelCase   ):
											__magic_name__				=     i
						__magic_name__				=     WordpieceTokenizer(vocab=_lowerCamelCase	,			unk_token='[UNK]'   )

						self.assertListEqual(tokenizer.tokenize(''   )	,			[]   )

						self.assertListEqual(tokenizer.tokenize('unwanted running'   )	,			['un', '##want', '##ed', 'runn', '##ing']   )

						self.assertListEqual(tokenizer.tokenize('unwantedX running'   )	,			['[UNK]', 'runn', '##ing']   )







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						self.assertTrue(_is_whitespace(' '   )   )
						self.assertTrue(_is_whitespace('\t'   )   )
						self.assertTrue(_is_whitespace('\r'   )   )
						self.assertTrue(_is_whitespace('\n'   )   )
						self.assertTrue(_is_whitespace('\u00A0'   )   )

						self.assertFalse(_is_whitespace('A'   )   )
						self.assertFalse(_is_whitespace('-'   )   )







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

						'''simple docstring'''





						self.assertTrue(_is_control('\u0005'   )   )

						self.assertFalse(_is_control('A'   )   )
						self.assertFalse(_is_control(' '   )   )
						self.assertFalse(_is_control('\t'   )   )
						self.assertFalse(_is_control('\r'   )   )







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

						'''simple docstring'''





						self.assertTrue(_is_punctuation('-'   )   )
						self.assertTrue(_is_punctuation('$'   )   )
						self.assertTrue(_is_punctuation('`'   )   )
						self.assertTrue(_is_punctuation('.'   )   )

						self.assertFalse(_is_punctuation('A'   )   )
						self.assertFalse(_is_punctuation(' '   )   )







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.get_tokenizer()

						# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
						self.assertListEqual([tokenizer.tokenize(_lowerCamelCase   ) for t in ['Test', '\xad', 'test']]	,			[['[UNK]'], [], ['[UNK]']]   )

						if self.test_rust_tokenizer:
											__magic_name__				=     self.get_rust_tokenizer()
											self.assertListEqual(
											    [rust_tokenizer.tokenize(_lowerCamelCase   ) for t in ['Test', '\xad', 'test']]	,			[['[UNK]'], [], ['[UNK]']]   )







	@slow
	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						__magic_name__				=     self.tokenizer_class.from_pretrained('bert-base-uncased'   )

						__magic_name__				=     tokenizer.encode('sequence builders'	,			add_special_tokens=_lowerCamelCase   )
						__magic_name__				=     tokenizer.encode('multi-sequence build'	,			add_special_tokens=_lowerCamelCase   )

						__magic_name__				=     tokenizer.build_inputs_with_special_tokens(_lowerCamelCase   )
						__magic_name__				=     tokenizer.build_inputs_with_special_tokens(_lowerCamelCase	,			_lowerCamelCase   )

						assert encoded_sentence == [1_01] + text + [1_02]
						assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																__magic_name__				=     self.rust_tokenizer_class.from_pretrained(_lowerCamelCase	,			**_lowerCamelCase   )

																__magic_name__				=     F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
																__magic_name__				=     tokenizer_r.encode_plus(
																    _lowerCamelCase	,			return_attention_mask=_lowerCamelCase	,			return_token_type_ids=_lowerCamelCase	,			return_offsets_mapping=_lowerCamelCase	,			add_special_tokens=_lowerCamelCase	,			)

																__magic_name__				=     tokenizer_r.do_lower_case if hasattr(_lowerCamelCase	,			'do_lower_case'   ) else False
																__magic_name__				=     (
																    [
																        ((0, 0), tokenizer_r.cls_token),
																        ((0, 1), '''A'''),
																        ((1, 2), ''','''),
																        ((3, 5), '''na'''),
																        ((5, 6), '''##ï'''),
																        ((6, 8), '''##ve'''),
																        ((9, 15), tokenizer_r.mask_token),
																        ((16, 21), '''Allen'''),
																        ((21, 23), '''##NL'''),
																        ((23, 24), '''##P'''),
																        ((25, 33), '''sentence'''),
																        ((33, 34), '''.'''),
																        ((0, 0), tokenizer_r.sep_token),
																    ]
																    if not do_lower_case
																    else [
																        ((0, 0), tokenizer_r.cls_token),
																        ((0, 1), '''a'''),
																        ((1, 2), ''','''),
																        ((3, 8), '''naive'''),
																        ((9, 15), tokenizer_r.mask_token),
																        ((16, 21), '''allen'''),
																        ((21, 23), '''##nl'''),
																        ((23, 24), '''##p'''),
																        ((25, 33), '''sentence'''),
																        ((33, 34), '''.'''),
																        ((0, 0), tokenizer_r.sep_token),
																    ]
																)

																self.assertEqual(
																    [e[1] for e in expected_results]	,			tokenizer_r.convert_ids_to_tokens(tokens['input_ids']   )   )
																self.assertEqual([e[0] for e in expected_results]	,			tokens['offset_mapping']   )







	@slow
	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''simple docstring'''





						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																__magic_name__				=     self.rust_tokenizer_class.from_pretrained(_lowerCamelCase	,			**_lowerCamelCase   )
																__magic_name__				=     self.tokenizer_class.from_pretrained(_lowerCamelCase	,			**_lowerCamelCase   )
																__magic_name__				=     [['''Hello world!''', '''Nice to meet you!'''], ['''The cute cat.''', '''The adorable dog.''']]

																__magic_name__				=     tokenizer_r.batch_encode_candidates(_lowerCamelCase	,			max_length=10	,			return_tensors='np'   )
																__magic_name__				=     tokenizer_p.batch_encode_candidates(_lowerCamelCase	,			max_length=10	,			return_tensors='np'   )

																__magic_name__				=     (2, 2, 10)

																self.assertEqual(encoded_sentence_r['input_ids'].shape	,			_lowerCamelCase   )
																self.assertEqual(encoded_sentence_r['attention_mask'].shape	,			_lowerCamelCase   )
																self.assertEqual(encoded_sentence_r['token_type_ids'].shape	,			_lowerCamelCase   )

																self.assertEqual(encoded_sentence_p['input_ids'].shape	,			_lowerCamelCase   )
																self.assertEqual(encoded_sentence_p['attention_mask'].shape	,			_lowerCamelCase   )
																self.assertEqual(encoded_sentence_p['token_type_ids'].shape	,			_lowerCamelCase   )






import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
      raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
a__				:      Dict    		=     logging.get_logger(__name__)

a__				:      str    		=     '''Hello world! cécé herlolip'''







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = os.path.split(a__   )
    SCREAMING_SNAKE_CASE				:				Any				      = DataaVecTextModel.from_pretrained(
        a__ ,						checkpoint_file=a__   )
    dataavec.eval()  # disable dropout
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.models[0]
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.sentence_encoder
    SCREAMING_SNAKE_CASE				:				List[str]				      = DataaVecTextConfig(
        vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings ,						hidden_size=dataavec_model.args.encoder_embed_dim ,						num_hidden_layers=dataavec_model.args.encoder_layers ,						num_attention_heads=dataavec_model.args.encoder_attention_heads ,						intermediate_size=dataavec_model.args.encoder_ffn_embed_dim ,						max_position_embeddings=514 ,						type_vocab_size=1 ,						layer_norm_eps=1e-5 ,						)
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
    print('''Our BERT config:''' ,						a__   )

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = DataaVecTextForSequenceClassification(a__   ) if classification_head else DataaVecTextForMaskedLM(a__   )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec_sent_encoder.embed_tokens.weight
    SCREAMING_SNAKE_CASE				:				int				      = dataavec_sent_encoder.embed_positions.weight
    SCREAMING_SNAKE_CASE				:				int				      = torch.zeros_like(
        model.dataavec_text.embeddings.token_type_embeddings.weight   )  # just zero them out b/c data2vec doesn't use them.
    SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_sent_encoder.layernorm_embedding.weight
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_sent_encoder.layernorm_embedding.bias

    for i in range(config.num_hidden_layers   ):
        # Encoder: start of layer
        SCREAMING_SNAKE_CASE				:				BertLayer				      = model.dataavec_text.encoder.layer[i]
        SCREAMING_SNAKE_CASE				:				TransformerSentenceEncoderLayer				      = dataavec_sent_encoder.layers[i]

        # self attention
        SCREAMING_SNAKE_CASE				:				BertSelfAttention				      = layer.attention.self
        assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )
        assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )
        assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )

        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.q_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.q_proj.bias
        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.k_proj.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.self_attn.k_proj.bias
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.v_proj.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.self_attn.v_proj.bias

        # self-attention output
        SCREAMING_SNAKE_CASE				:				BertSelfOutput				      = layer.attention.output
        assert (
            self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
        ), F"""Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.out_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.out_proj.bias
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        SCREAMING_SNAKE_CASE				:				BertIntermediate				      = layer.intermediate
        assert (
            intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
        ), F"""Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_layer.fca.bias

        # output
        SCREAMING_SNAKE_CASE				:				BertOutput				      = layer.output
        assert (
            bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
        ), F"""Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				int				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.fca.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.final_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.final_layer_norm.bias
        # end of layer

    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].dense.weight
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].dense.bias
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].out_proj.bias
    else:
        # LM Head
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_model.encoder.lm_head.dense.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_model.encoder.lm_head.dense.bias
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_model.encoder.lm_head.layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.lm_head.layer_norm.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_model.encoder.lm_head.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model.encoder.lm_head.bias

    # Let's check that we get the same results.
    SCREAMING_SNAKE_CASE				:				torch.Tensor				      = dataavec.encode(a__   ).unsqueeze(0   )  # batch of size 1

    SCREAMING_SNAKE_CASE				:				List[str]				      = model(a__   )[0]
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(a__   )   )
    else:
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model(a__   )[0]
    print(our_output.shape ,						their_output.shape   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.max(torch.abs(our_output - their_output   )   ).item()
    print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.allclose(a__ ,						a__ ,						atol=1e-3   )
    print('''Do both models output the same tensors?''' ,						'''🔥''' if success else '''💩'''   )
    if not success:
        raise Exception('''Something went wRoNg'''   )

    pathlib.Path(a__   ).mkdir(parents=a__ ,						exist_ok=a__   )
    print(F"""Saving model to {pytorch_dump_folder_path}"""   )
    model.save_pretrained(a__   )


if __name__ == "__main__":
      a__				:      str    		=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      parser.add_argument(
          '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
      )
      a__				:      int    		=     parser.parse_args()
      convert_dataavec_checkpoint_to_pytorch(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )
