
import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
	raise Exception('''requires fairseq >= 1.0.0a''')

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

UpperCAmelCase	      = '''Hello world! cécé herlolip'''






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    FairseqRobertaModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
	roberta.eval()  # disable dropout
	lowercase       =    roberta.model.encoder.sentence_encoder
	lowercase       =    XLMRobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings ,							hidden_size=roberta.cfg.model.encoder_embed_dim ,							num_hidden_layers=roberta.cfg.model.encoder_layers ,							num_attention_heads=roberta.cfg.model.encoder_attention_heads ,							intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim ,							max_position_embeddings=514 ,							type_vocab_size=1 ,							layer_norm_eps=1e-5 ,							)
	if classification_head:
		lowercase       =    roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

	print('Our RoBERTa config:' ,							__SCREAMING_SNAKE_CASE							)

	lowercase       =    XLMRobertaXLForSequenceClassification(__SCREAMING_SNAKE_CASE							) if classification_head else XLMRobertaXLForMaskedLM(__SCREAMING_SNAKE_CASE							)
	model.eval()

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

	lowercase       =    roberta_sent_encoder.layer_norm.weight
	lowercase       =    roberta_sent_encoder.layer_norm.bias

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

		lowercase       =    layer.attention
		lowercase       =    roberta_layer.self_attn_layer_norm.weight
		lowercase       =    roberta_layer.self_attn_layer_norm.bias

		# self attention
		lowercase       =    layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)							)
		)

		lowercase       =    roberta_layer.self_attn.q_proj.weight
		lowercase       =    roberta_layer.self_attn.q_proj.bias
		lowercase       =    roberta_layer.self_attn.k_proj.weight
		lowercase       =    roberta_layer.self_attn.k_proj.bias
		lowercase       =    roberta_layer.self_attn.v_proj.weight
		lowercase       =    roberta_layer.self_attn.v_proj.bias

		# self-attention output
		lowercase       =    layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		lowercase       =    roberta_layer.self_attn.out_proj.weight
		lowercase       =    roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		lowercase       =    roberta_layer.final_layer_norm.weight
		lowercase       =    roberta_layer.final_layer_norm.bias

		# intermediate
		lowercase       =    layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase       =    roberta_layer.fca.weight
		lowercase       =    roberta_layer.fca.bias

		# output
		lowercase       =    layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase       =    roberta_layer.fca.weight
		lowercase       =    roberta_layer.fca.bias
		# end of layer

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

	# Let's check that we get the same results.
	lowercase       =    roberta.encode(__SCREAMING_SNAKE_CASE							).unsqueeze(0							)  # batch of size 1

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

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


if __name__ == "__main__":
	UpperCAmelCase	      = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--roberta_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.'''
	)
	UpperCAmelCase	      = parser.parse_args()
	convert_xlm_roberta_xl_checkpoint_to_pytorch(
	    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)



import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow







class        A_   :




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		return None







class        A_   :




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case       ):
		return None







class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       [
	    # (model_name, model_kwargs)
	    ("""bert-base-cased""", {}),
	    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
	]





	@require_tf
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case		,    'tf'		,    12		,    **snake_case       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case		,    'pt'		,    12		,    **snake_case       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import BertModel

		lowercase       =    ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
		with NamedTemporaryFile(mode='w+t'       ) as vocab_file:
			vocab_file.write('\n'.join(snake_case       )       )
			vocab_file.flush()
			lowercase       =    BertTokenizerFast(vocab_file.name       )

		with TemporaryDirectory() as bert_save_dir:
			lowercase       =    BertModel(BertConfig(vocab_size=len(snake_case       )       )       )
			model.save_pretrained(snake_case       )
			self._test_export(snake_case		,    'pt'		,    12		,    snake_case       )





	@require_tf
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase       =    self._test_export(snake_case		,    'tf'		,    12		,    **snake_case       )
			lowercase       =    quantize(Path(snake_case       )       )

			# Ensure the actual quantized model is not bigger than the original one
			if quantized_path.stat().st_size >= Path(snake_case       ).stat().st_size:
				self.fail('Quantized model is bigger than initial ONNX model'       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase       =    self._test_export(snake_case		,    'pt'		,    12		,    **snake_case       )
			lowercase       =    quantize(snake_case       )

			# Ensure the actual quantized model is not bigger than the original one
			if quantized_path.stat().st_size >= Path(snake_case       ).stat().st_size:
				self.fail('Quantized model is bigger than initial ONNX model'       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case=None		,    **snake_case       ):
		try:
			# Compute path
			with TemporaryDirectory() as tempdir:
				lowercase       =    Path(snake_case       ).joinpath('model.onnx'       )

			# Remove folder if exists
			if path.parent.exists():
				path.parent.rmdir()

			# Export
			convert(snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    **snake_case       )

			return path
		except Exception as e:
			self.fail(snake_case       )





	@require_torch
	@require_tokenizers
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import BertModel

		lowercase       =    BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'       )       )
		lowercase       =    BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'       )
		self._test_infer_dynamic_axis(snake_case		,    snake_case		,    'pt'       )





	@require_tf
	@require_tokenizers
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import TFBertModel

		lowercase       =    TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'       )       )
		lowercase       =    BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'       )
		self._test_infer_dynamic_axis(snake_case		,    snake_case		,    'tf'       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    FeatureExtractionPipeline(snake_case		,    snake_case       )

		lowercase       =    ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    infer_shapes(snake_case		,    snake_case       )

		# Assert all variables are present
		self.assertEqual(len(snake_case       )		,    len(snake_case       )       )
		self.assertTrue(all(var_name in shapes for var_name in variable_names       )       )
		self.assertSequenceEqual(variable_names[:3]		,    snake_case       )
		self.assertSequenceEqual(variable_names[3:]		,    snake_case       )

		# Assert inputs are {0: batch, 1: sequence}
		for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
			self.assertDictEqual(shapes[var_name]		,    {0: 'batch', 1: 'sequence'}       )

		# Assert outputs are {0: batch, 1: sequence} and {0: batch}
		self.assertDictEqual(shapes['output_0']		,    {0: 'batch', 1: 'sequence'}       )
		self.assertDictEqual(shapes['output_1']		,    {0: 'batch'}       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ['input_ids', 'attention_mask', 'token_type_ids']
		lowercase       =    {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
		lowercase  ,					lowercase       =    ensure_valid_input(FuncContiguousArgs()		,    snake_case		,    snake_case       )

		# Should have exactly the same number of args (all are valid)
		self.assertEqual(len(snake_case       )		,    3       )

		# Should have exactly the same input names
		self.assertEqual(set(snake_case       )		,    set(snake_case       )       )

		# Parameter should be reordered according to their respective place in the function:
		# (input_ids, token_type_ids, attention_mask)
		self.assertEqual(snake_case		,    (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])       )

		# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
		lowercase  ,					lowercase       =    ensure_valid_input(FuncNonContiguousArgs()		,    snake_case		,    snake_case       )

		# Should have exactly the one arg (all before the one not provided "some_other_args")
		self.assertEqual(len(snake_case       )		,    1       )
		self.assertEqual(len(snake_case       )		,    1       )

		# Should have only "input_ids"
		self.assertEqual(inputs_args[0]		,    tokens['input_ids']       )
		self.assertEqual(ordered_input_names[0]		,    'input_ids'       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    generate_identified_filename(Path('/home/something/my_fake_model.onnx'       )		,    '-test'       )
		self.assertEqual('/home/something/my_fake_model-test.onnx'		,    generated.as_posix()       )

