
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()
lowercase        =  logging.get_logger(__name__)

lowercase        =  '''Hello world! cécé herlolip'''



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      bool       )  ->    List[str]:
							lowerCamelCase_							  =					FairseqRobertaModel.from_pretrained(UpperCAmelCase__       )
							roberta.eval()  # disable dropout
							lowerCamelCase_							  =					roberta.model.encoder.sentence_encoder
							lowerCamelCase_							  =					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=5_1_4			,				type_vocab_size=1			,				layer_norm_eps=1e-5			,				)
							if classification_head:
														lowerCamelCase_							  =					roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

							print("""Our RoBERTa config:"""			,				UpperCAmelCase__       )

							lowerCamelCase_							  =					XLMRobertaXLForSequenceClassification(UpperCAmelCase__       ) if classification_head else XLMRobertaXLForMaskedLM(UpperCAmelCase__       )
							model.eval()

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

							lowerCamelCase_							  =					roberta_sent_encoder.layer_norm.weight
							lowerCamelCase_							  =					roberta_sent_encoder.layer_norm.bias

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

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

														# self attention
														lowerCamelCase_							  =					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)       )
														)

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

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

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

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

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

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

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

							lowerCamelCase_							  =					model(UpperCAmelCase__       )[0]
							if classification_head:
														lowerCamelCase_							  =					roberta.model.classification_heads["""mnli"""](roberta.extract_features(UpperCAmelCase__       )       )
							else:
														lowerCamelCase_							  =					roberta.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__":
		lowercase        =  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.'''
		)
		lowercase        =  parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import numpy
		import tensorflow as tf

		from transformers import (
		    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BertConfig,
		    DPRConfig,
		    TFDPRContextEncoder,
		    TFDPRQuestionEncoder,
		    TFDPRReader,
		)





class       __A:

					def __init__(	self					:     str   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Any=1_3   ,					__UpperCamelCase					:     Dict=7   ,					__UpperCamelCase					:     int=True   ,					__UpperCamelCase					:     Optional[Any]=True   ,					__UpperCamelCase					:     Optional[Any]=True   ,					__UpperCamelCase					:     Dict=True   ,					__UpperCamelCase					:     Dict=9_9   ,					__UpperCamelCase					:     Optional[int]=3_2   ,					__UpperCamelCase					:     List[Any]=2   ,					__UpperCamelCase					:     Optional[int]=4   ,					__UpperCamelCase					:     List[Any]=3_7   ,					__UpperCamelCase					:     Optional[Any]="gelu"   ,					__UpperCamelCase					:     List[str]=0.1   ,					__UpperCamelCase					:     Any=0.1   ,					__UpperCamelCase					:     int=5_1_2   ,					__UpperCamelCase					:     List[Any]=1_6   ,					__UpperCamelCase					:     List[Any]=2   ,					__UpperCamelCase					:     List[str]=0.02   ,					__UpperCamelCase					:     Any=3   ,					__UpperCamelCase					:     int=4   ,					__UpperCamelCase					:     Optional[Any]=None   ,					__UpperCamelCase					:     Union[str, Any]=0   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					seq_length
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_input_mask
												lowerCamelCase_							  =					use_token_type_ids
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					vocab_size
												lowerCamelCase_							  =					hidden_size
												lowerCamelCase_							  =					num_hidden_layers
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					intermediate_size
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					hidden_dropout_prob
												lowerCamelCase_							  =					attention_probs_dropout_prob
												lowerCamelCase_							  =					max_position_embeddings
												lowerCamelCase_							  =					type_vocab_size
												lowerCamelCase_							  =					type_sequence_label_size
												lowerCamelCase_							  =					initializer_range
												lowerCamelCase_							  =					num_labels
												lowerCamelCase_							  =					num_choices
												lowerCamelCase_							  =					scope
												lowerCamelCase_							  =					projection_dim

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					ids_tensor([self.batch_size, self.seq_length]   ,					self.vocab_size   )

												lowerCamelCase_							  =					None
												if self.use_input_mask:
																			# follow test_modeling_tf_ctrl.py
																			lowerCamelCase_							  =					random_attention_mask([self.batch_size, self.seq_length]   )

												lowerCamelCase_							  =					None
												if self.use_token_type_ids:
																			lowerCamelCase_							  =					ids_tensor([self.batch_size, self.seq_length]   ,					self.type_vocab_size   )

												lowerCamelCase_							  =					None
												lowerCamelCase_							  =					None
												lowerCamelCase_							  =					None
												if self.use_labels:
																			lowerCamelCase_							  =					ids_tensor([self.batch_size]   ,					self.type_sequence_label_size   )
																			lowerCamelCase_							  =					ids_tensor([self.batch_size, self.seq_length]   ,					self.num_labels   )
																			lowerCamelCase_							  =					ids_tensor([self.batch_size]   ,					self.num_choices   )

												lowerCamelCase_							  =					BertConfig(
												    vocab_size=self.vocab_size   ,					hidden_size=self.hidden_size   ,					num_hidden_layers=self.num_hidden_layers   ,					num_attention_heads=self.num_attention_heads   ,					intermediate_size=self.intermediate_size   ,					hidden_act=self.hidden_act   ,					hidden_dropout_prob=self.hidden_dropout_prob   ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,					max_position_embeddings=self.max_position_embeddings   ,					type_vocab_size=self.type_vocab_size   ,					is_decoder=__UpperCamelCase   ,					initializer_range=self.initializer_range   ,					)
												lowerCamelCase_							  =					DPRConfig(projection_dim=self.projection_dim   ,					**config.to_dict()   )

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					TFDPRContextEncoder(config=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   )
												self.parent.assertEqual(result.pooler_output.shape   ,					(self.batch_size, self.projection_dim or self.hidden_size)   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					TFDPRQuestionEncoder(config=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   )
												self.parent.assertEqual(result.pooler_output.shape   ,					(self.batch_size, self.projection_dim or self.hidden_size)   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					TFDPRReader(config=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   )

												self.parent.assertEqual(result.start_logits.shape   ,					(self.batch_size, self.seq_length)   )
												self.parent.assertEqual(result.end_logits.shape   ,					(self.batch_size, self.seq_length)   )
												self.parent.assertEqual(result.relevance_logits.shape   ,					(self.batch_size,)   )



					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												(
												    (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   
												)							  =					config_and_inputs
												lowerCamelCase_							  =					{"""input_ids""": input_ids}
												return config, inputs_dict





@require_tf
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (
					    (
					        TFDPRContextEncoder,
					        TFDPRQuestionEncoder,
					        TFDPRReader,
					    )
					    if is_tf_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE			    =    {'''feature-extraction''': TFDPRQuestionEncoder} if is_tf_available() else {}

					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					TFDPRModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   ,					hidden_size=3_7   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												self.config_tester.run_common_tests()

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_dpr_context_encoder(*__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_dpr_question_encoder(*__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_dpr_reader(*__UpperCamelCase   )



					@slow
					def    lowercase__      (	self					:     Optional[int]   ):
												for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					TFDPRContextEncoder.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )

												for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					TFDPRContextEncoder.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )

												for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					TFDPRQuestionEncoder.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )

												for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					TFDPRReader.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )





@require_tf
class       __A(				unittest.TestCase       ):

					@slow
					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""   )

												lowerCamelCase_							  =					tf.constant(
												    [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]   )  # [CLS] hello, is my dog cute? [SEP]
												lowerCamelCase_							  =					model(__UpperCamelCase   )[0]  # embedding shape = (1, 768)
												# compare the actual values for a slice.
												lowerCamelCase_							  =					tf.constant(
												    [
												        [
												            0.03236253,
												            0.12753335,
												            0.16818509,
												            0.00279786,
												            0.3896933,
												            0.24264945,
												            0.2178971,
												            -0.02335227,
												            -0.08481959,
												            -0.14324117,
												        ]
												    ]   )
												self.assertTrue(numpy.allclose(output[:, :1_0].numpy()   ,					expected_slice.numpy()   ,					atol=1E-4   )   )





