"""simple docstring"""





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   :						Tuple      =    logging.get_logger(__name__)

_UpperCamelCase   :						Any      =    "Hello world! cécé herlolip"





def 				a_     (      _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : List[str]     ):





       '''simple docstring'''

       lowercase__			:		Optional[Any]       =	FairseqRobertaModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
       roberta.eval()  # disable dropout
       lowercase__			:		Union[str, Any]       =	roberta.model.encoder.sentence_encoder
       lowercase__			:		Optional[int]       =	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__			:		List[str]       =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

       print('Our RoBERTa config:'   , __SCREAMING_SNAKE_CASE     )

       lowercase__			:		Optional[Any]       =	XLMRobertaXLForSequenceClassification(__SCREAMING_SNAKE_CASE     ) if classification_head else XLMRobertaXLForMaskedLM(__SCREAMING_SNAKE_CASE     )
       model.eval()

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

       lowercase__			:		List[str]       =	roberta_sent_encoder.layer_norm.weight
       lowercase__			:		Optional[Any]       =	roberta_sent_encoder.layer_norm.bias

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

              lowercase__			:		str       =	layer.attention
              lowercase__			:		Dict       =	roberta_layer.self_attn_layer_norm.weight
              lowercase__			:		Dict       =	roberta_layer.self_attn_layer_norm.bias

              # self attention
              lowercase__			:		List[str]       =	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__			:		int       =	roberta_layer.self_attn.q_proj.weight
              lowercase__			:		str       =	roberta_layer.self_attn.q_proj.bias
              lowercase__			:		Union[str, Any]       =	roberta_layer.self_attn.k_proj.weight
              lowercase__			:		Union[str, Any]       =	roberta_layer.self_attn.k_proj.bias
              lowercase__			:		int       =	roberta_layer.self_attn.v_proj.weight
              lowercase__			:		Any       =	roberta_layer.self_attn.v_proj.bias

              # self-attention output
              lowercase__			:		List[str]       =	layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              lowercase__			:		Tuple       =	roberta_layer.self_attn.out_proj.weight
              lowercase__			:		Optional[int]       =	roberta_layer.self_attn.out_proj.bias

              # this one is final layer norm
              lowercase__			:		Optional[Any]       =	roberta_layer.final_layer_norm.weight
              lowercase__			:		List[str]       =	roberta_layer.final_layer_norm.bias

              # intermediate
              lowercase__			:		Optional[Any]       =	layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              lowercase__			:		List[str]       =	roberta_layer.fca.weight
              lowercase__			:		str       =	roberta_layer.fca.bias

              # output
              lowercase__			:		int       =	layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              lowercase__			:		Dict       =	roberta_layer.fca.weight
              lowercase__			:		Optional[int]       =	roberta_layer.fca.bias
              # end of layer

       if classification_head:
              lowercase__			:		Union[str, Any]       =	roberta.model.classification_heads['mnli'].dense.weight
              lowercase__			:		Optional[int]       =	roberta.model.classification_heads['mnli'].dense.bias
              lowercase__			:		Optional[int]       =	roberta.model.classification_heads['mnli'].out_proj.weight
              lowercase__			:		Optional[int]       =	roberta.model.classification_heads['mnli'].out_proj.bias
       else:
              # LM Head
              lowercase__			:		Tuple       =	roberta.model.encoder.lm_head.dense.weight
              lowercase__			:		Optional[int]       =	roberta.model.encoder.lm_head.dense.bias
              lowercase__			:		Tuple       =	roberta.model.encoder.lm_head.layer_norm.weight
              lowercase__			:		int       =	roberta.model.encoder.lm_head.layer_norm.bias
              lowercase__			:		List[str]       =	roberta.model.encoder.lm_head.weight
              lowercase__			:		Tuple       =	roberta.model.encoder.lm_head.bias

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

       lowercase__			:		Tuple       =	model(__SCREAMING_SNAKE_CASE     )[0]
       if classification_head:
              lowercase__			:		List[str]       =	roberta.model.classification_heads['mnli'](roberta.extract_features(__SCREAMING_SNAKE_CASE     )     )
       else:
              lowercase__			:		Tuple       =	roberta.model(__SCREAMING_SNAKE_CASE     )[0]
       print(our_output.shape   , their_output.shape     )
       lowercase__			:		List[Any]       =	torch.max(torch.abs(our_output - their_output     )     ).item()
       print(f"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
       lowercase__			:		Optional[Any]       =	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   :						Union[str, Any]      =    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   :						int      =    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()       )

