
"""simple docstring"""


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

__A					=			{
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

__A					=			datasets.load_dataset('xnli', split='test+validation')

__A					=			0
__A					=			0
__A					=			0
__A					=			0



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					List[Any]:
	if spm_diff == list(reversed(__UpperCamelCase			)			):
		# AAA -> AA+A vs A+AA case.
		return True
	elif len(__UpperCamelCase			) == len(__UpperCamelCase			) and fast.decode(__UpperCamelCase			) == fast.decode(__UpperCamelCase			):
		# Second order OK
		# Barrich -> Barr + ich vs Bar + rich
		return True
	_lowerCAmelCase								=slow.encode(slow.decode(__UpperCamelCase			)			)
	_lowerCAmelCase								=fast.encode(fast.decode(__UpperCamelCase			)			)
	if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
		# Type 3 error.
		# Snehagatha ->
		#       Sne, h, aga, th, a
		#       Sne, ha, gat, ha
		# Encoding the wrong with sp does not even recover what spm gave us
		# It fits tokenizer however...
		return True
	return False



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					Optional[int]:
	_lowerCAmelCase								=fast.encode_plus(__UpperCamelCase			)[0]
	_lowerCAmelCase								=enc.offsets
	_lowerCAmelCase		,				_lowerCAmelCase								=offsets[idx], offsets[idx - 1]
	if curr is not None and line[curr[0] : curr[1]] == "\u200f":
		return True
	if prev is not None and line[prev[0] : prev[1]] == "\u200f":
		return True



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					str:
	# Encoding can be the same with same result AAA -> A + AA vs AA + A
	# We can check that we use at least exactly the same number of tokens.
	for i, (spm_id, tok_id) in enumerate(zip(__UpperCamelCase       ,					__UpperCamelCase			)			):
		if spm_id != tok_id:
			break
	_lowerCAmelCase								=i
	for i, (spm_id, tok_id) in enumerate(zip(reversed(__UpperCamelCase			)       ,					reversed(__UpperCamelCase			)			)			):
		if spm_id != tok_id:
			break
	_lowerCAmelCase								=len(__UpperCamelCase			) - i

	_lowerCAmelCase								=spm_ids[first:last]
	_lowerCAmelCase								=tok_ids[first:last]

	if check_diff(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			):
		return True

	if check_LTR_mark(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			):
		return True

	if last - first > 5:
		# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
		_lowerCAmelCase								=Counter(spm_ids[first:last]			)
		_lowerCAmelCase								=Counter(tok_ids[first:last]			)

		_lowerCAmelCase								={spm_ for (spm_, si) in spms.items() if toks.get(spm_       ,					0			) == si}
		_lowerCAmelCase								=3
		for i in range(last - first - min_width			):
			if all(spm_ids[first + i + j] in removable_tokens for j in range(__UpperCamelCase			)			):
				_lowerCAmelCase								=[
				    k
				    for k in range(last - first - min_width			)
				    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
				]
				for j in possible_matches:
					if check_diff(spm_ids[first : first + i]       ,					tok_ids[first : first + j]       ,					__UpperCamelCase       ,					__UpperCamelCase			) and check_details(
					    __UpperCamelCase       ,					spm_ids[first + i : last]       ,					tok_ids[first + j : last]       ,					__UpperCamelCase       ,					__UpperCamelCase       ,					):
						return True

	print(F'''Spm: {[fast.decode([spm_ids[i]]			) for i in range(__UpperCamelCase       ,					__UpperCamelCase			)]}'''			)
	try:
		print(F'''Tok: {[fast.decode([tok_ids[i]]			) for i in range(__UpperCamelCase       ,					__UpperCamelCase			)]}'''			)
	except Exception:
		pass

	_lowerCAmelCase								=fast.decode(spm_ids[:first]			)
	_lowerCAmelCase								=fast.decode(spm_ids[last:]			)
	_lowerCAmelCase								=fast.decode(spm_ids[first:last]			)
	print()
	print(__UpperCamelCase			)
	return False



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					Dict:
	global perfect
	global imperfect
	global wrong
	global total

	_lowerCAmelCase								=slow.encode(__UpperCamelCase			)
	_lowerCAmelCase								=fast.encode(__UpperCamelCase			)

	_lowerCAmelCase								=False
	total += 1

	if slow_ids != fast_ids:
		if check_details(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			):
			_lowerCAmelCase								=True
			imperfect += 1
		else:
			wrong += 1
	else:
		perfect += 1

	if total % 10000 == 0:
		print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''			)

	if skip_assert:
		return

	assert (
	    slow_ids == fast_ids
	), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__UpperCamelCase			)}\n{fast.tokenize(__UpperCamelCase			)}'''



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase			)							->					List[str]:
	global batch_total
	for i in range(len(__UpperCamelCase			)			):
		# premise, all languages
		for text in dataset[i]["premise"].values():
			test_string(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)

		# hypothesis, all languages
		for text in dataset[i]["hypothesis"]["translation"]:
			test_string(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)


if __name__ == "__main__":
			for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
						__A					=			list(slow_class.max_model_input_sizes.keys())
						for checkpoint in checkpoint_names:
									__A					=			0
									__A					=			0
									__A					=			0
									__A					=			0

									print(F"""========================== Checking {name}: {checkpoint} ==========================""")
									__A					=			slow_class.from_pretrained(checkpoint, force_download=True)
									__A					=			fast_class.from_pretrained(checkpoint, force_download=True)
									test_tokenizer(slow, fast)
									print(F"""Accuracy {perfect * 100 / total:.2f}""")





"""simple docstring"""


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					=			logging.get_logger(__name__)

__A					=			'Hello world! cécé herlolip'



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					List[Any]:
	_lowerCAmelCase		,				_lowerCAmelCase								=os.path.split(__UpperCamelCase			)
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase       ,					checkpoint_file=__UpperCamelCase			)
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=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:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""       ,					__UpperCamelCase			)

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase			) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase			)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

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

		# self attention
		_lowerCAmelCase								=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)			)}'''
		)

		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase			).unsqueeze(0			)  # batch of size 1

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

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


if __name__ == "__main__":
			__A					=			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					=			parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



