
"""simple docstring"""




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
						raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
						raise Exception('requires fairseq < v2')

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

lowerCAmelCase__				=					'Hello, World!'
lowerCAmelCase__				=					'en_XX'



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			Path('''data_bin'''     )
		SCREAMING_SNAKE_CASE_    			=			FairseqXmodModel.from_pretrained(
		    model_name_or_path=str(Path(__a     ).parent     ),				checkpoint_file=Path(__a     ).name,				_name='''xmod_base''',				arch='''xmod_base''',				task='''multilingual_masked_lm''',				data_name_or_path=str(__a     ),				bpe='''sentencepiece''',				sentencepiece_model=str(Path(__a     ).parent / '''sentencepiece.bpe.model'''     ),				src_dict=str(data_dir / '''dict.txt'''     ),				)
		xmod.eval()  # disable dropout
		print(__a     )

		SCREAMING_SNAKE_CASE_    			=			xmod.model.encoder.sentence_encoder
		SCREAMING_SNAKE_CASE_    			=			XmodConfig(
		    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings,				hidden_size=xmod.cfg.model.encoder_embed_dim,				num_hidden_layers=xmod.cfg.model.encoder_layers,				num_attention_heads=xmod.cfg.model.encoder_attention_heads,				intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim,				max_position_embeddings=514,				type_vocab_size=1,				layer_norm_eps=1E-5,				pre_norm=xmod.cfg.model.encoder_normalize_before,				adapter_reduction_factor=getattr(xmod.cfg.model,				'''bottleneck''',				2     ),				adapter_layer_norm=xmod.cfg.model.adapter_layer_norm,				adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm,				ln_before_adapter=xmod.cfg.model.ln_before_adapter,				languages=xmod.cfg.model.languages,				)
		if classification_head:
				SCREAMING_SNAKE_CASE_    			=			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

		print('''Our X-MOD config:''',				__a     )

		SCREAMING_SNAKE_CASE_    			=			XmodForSequenceClassification(__a     ) if classification_head else XmodForMaskedLM(__a     )
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		SCREAMING_SNAKE_CASE_    			=			xmod_sent_encoder.embed_tokens.weight
		SCREAMING_SNAKE_CASE_    			=			xmod_sent_encoder.embed_positions.weight
		SCREAMING_SNAKE_CASE_    			=			torch.zeros_like(
		    model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c xmod doesn't use them.

		SCREAMING_SNAKE_CASE_    			=			xmod_sent_encoder.layernorm_embedding.weight
		SCREAMING_SNAKE_CASE_    			=			xmod_sent_encoder.layernorm_embedding.bias

		for i in range(config.num_hidden_layers     ):
				# Encoder: start of layer
				SCREAMING_SNAKE_CASE_    			=			model.roberta.encoder.layer[i]
				SCREAMING_SNAKE_CASE_    			=			xmod_sent_encoder.layers[i]

				# self attention
				SCREAMING_SNAKE_CASE_    			=			layer.attention.self
				if not (
				    xmod_layer.self_attn.k_proj.weight.data.shape
				    == xmod_layer.self_attn.q_proj.weight.data.shape
				    == xmod_layer.self_attn.v_proj.weight.data.shape
				    == torch.Size((config.hidden_size, config.hidden_size)     )
				):
						raise AssertionError('''Dimensions of self-attention weights do not match.'''     )

				SCREAMING_SNAKE_CASE_    			=			xmod_layer.self_attn.q_proj.weight
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.self_attn.q_proj.bias
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.self_attn.k_proj.weight
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.self_attn.k_proj.bias
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.self_attn.v_proj.weight
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.self_attn.v_proj.bias

				# self-attention output
				SCREAMING_SNAKE_CASE_    			=			layer.attention.output
				if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
						raise AssertionError('''Dimensions of self-attention output weights do not match.'''     )
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.self_attn.out_proj.weight
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.self_attn.out_proj.bias
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.self_attn_layer_norm.weight
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.self_attn_layer_norm.bias

				# intermediate
				SCREAMING_SNAKE_CASE_    			=			layer.intermediate
				if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
						raise AssertionError('''Dimensions of intermediate weights do not match.'''     )
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.fca.weight
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.fca.bias

				# output
				SCREAMING_SNAKE_CASE_    			=			layer.output
				if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
						raise AssertionError('''Dimensions of feed-forward weights do not match.'''     )
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.fca.weight
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.fca.bias
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.final_layer_norm.weight
				SCREAMING_SNAKE_CASE_    			=			xmod_layer.final_layer_norm.bias
				if bert_output.adapter_layer_norm is not None:
						SCREAMING_SNAKE_CASE_    			=			xmod_layer.adapter_layer_norm.weight
						SCREAMING_SNAKE_CASE_    			=			xmod_layer.adapter_layer_norm.bias

				if sorted(bert_output.adapter_modules.keys()     ) != sorted(xmod_layer.adapter_modules.keys()     ):
						raise AssertionError('''Lists of language adapters do not match.'''     )
				for lang_code, adapter in xmod_layer.adapter_modules.items():
						SCREAMING_SNAKE_CASE_    			=			bert_output.adapter_modules[lang_code]
						SCREAMING_SNAKE_CASE_    			=			xmod_layer.adapter_modules[lang_code]
						SCREAMING_SNAKE_CASE_    			=			from_adapter.fca.weight
						SCREAMING_SNAKE_CASE_    			=			from_adapter.fca.bias
						SCREAMING_SNAKE_CASE_    			=			from_adapter.fca.weight
						SCREAMING_SNAKE_CASE_    			=			from_adapter.fca.bias

				# end of layer

		if xmod_sent_encoder.layer_norm is not None:
				SCREAMING_SNAKE_CASE_    			=			xmod_sent_encoder.layer_norm.weight
				SCREAMING_SNAKE_CASE_    			=			xmod_sent_encoder.layer_norm.bias

		if classification_head:
				SCREAMING_SNAKE_CASE_    			=			xmod.model.classification_heads['''mnli'''].dense.weight
				SCREAMING_SNAKE_CASE_    			=			xmod.model.classification_heads['''mnli'''].dense.bias
				SCREAMING_SNAKE_CASE_    			=			xmod.model.classification_heads['''mnli'''].out_proj.weight
				SCREAMING_SNAKE_CASE_    			=			xmod.model.classification_heads['''mnli'''].out_proj.bias
		else:
				# LM Head
				SCREAMING_SNAKE_CASE_    			=			xmod.model.encoder.lm_head.dense.weight
				SCREAMING_SNAKE_CASE_    			=			xmod.model.encoder.lm_head.dense.bias
				SCREAMING_SNAKE_CASE_    			=			xmod.model.encoder.lm_head.layer_norm.weight
				SCREAMING_SNAKE_CASE_    			=			xmod.model.encoder.lm_head.layer_norm.bias
				SCREAMING_SNAKE_CASE_    			=			xmod.model.encoder.lm_head.weight
				SCREAMING_SNAKE_CASE_    			=			xmod.model.encoder.lm_head.bias

		# Let's check that we get the same results.
		SCREAMING_SNAKE_CASE_    			=			xmod.encode(__a     ).unsqueeze(0     )  # batch of size 1
		model.roberta.set_default_language(__a     )

		SCREAMING_SNAKE_CASE_    			=			model(__a     )[0]
		if classification_head:
				SCREAMING_SNAKE_CASE_    			=			xmod.model.classification_heads['''mnli'''](xmod.extract_features(__a     )     )
		else:
				SCREAMING_SNAKE_CASE_    			=			xmod.model(__a,				lang_id=[SAMPLE_LANGUAGE]     )[0]
		print(our_output.shape,				their_output.shape     )
		SCREAMING_SNAKE_CASE_    			=			torch.max(torch.abs(our_output - their_output     )     ).item()
		print(F'max_absolute_diff = {max_absolute_diff}'     )  # ~ 1e-7
		SCREAMING_SNAKE_CASE_    			=			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'''     )

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


if __name__ == "__main__":
						lowerCAmelCase__				=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--xmod_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.'
						)
						lowerCAmelCase__				=					parser.parse_args()
						convert_xmod_checkpoint_to_pytorch(
						    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)

"""simple docstring"""




import datasets

from .evaluate import evaluate


lowerCAmelCase__				=					'\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

lowerCAmelCase__				=					'\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'

lowerCAmelCase__				=					'\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the SQuAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_metric = datasets.load_metric("squad")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0}\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,      _KWARGS_DESCRIPTION )
class 			snake_case  (				datasets.Metric ):

							def _lowercase       (self     ):

									"""simple docstring"""







									return datasets.MetricInfo(
									    description=_DESCRIPTION							,							citation=_CITATION							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
									        {
									            '''predictions''': {'''id''': datasets.Value('''string'''     ), '''prediction_text''': datasets.Value('''string'''     )},
									            '''references''': {
									                '''id''': datasets.Value('''string'''     ),
									                '''answers''': datasets.features.Sequence(
									                    {
									                        '''text''': datasets.Value('''string'''     ),
									                        '''answer_start''': datasets.Value('''int32'''     ),
									                    }     ),
									            },
									        }     )							,							codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,							reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,							)







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
									SCREAMING_SNAKE_CASE_    			=			[
									    {
									        '''paragraphs''': [
									            {
									                '''qas''': [
									                    {
									                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
									                        '''id''': ref['''id'''],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									SCREAMING_SNAKE_CASE_    			=			evaluate(dataset=SCREAMING_SNAKE_CASE_							,							predictions=SCREAMING_SNAKE_CASE_     )
									return score