






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 __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  str:





						"""simple docstring"""



						_UpperCAmelCase ,			_UpperCAmelCase     =      os.path.split(__lowerCAmelCase     )
						_UpperCAmelCase     =      DataaVecTextModel.from_pretrained(
						    __lowerCAmelCase			,		checkpoint_file=__lowerCAmelCase     )
						dataavec.eval()  # disable dropout
						_UpperCAmelCase     =      dataavec.models[0]
						_UpperCAmelCase     =      dataavec_model.encoder.sentence_encoder
						_UpperCAmelCase     =      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:
												_UpperCAmelCase     =      dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'			,		__lowerCAmelCase     )

						_UpperCAmelCase     =      DataaVecTextForSequenceClassification(__lowerCAmelCase     ) if classification_head else DataaVecTextForMaskedLM(__lowerCAmelCase     )
						model.eval()

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

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

												# self attention
												_UpperCAmelCase     =      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)     )}"""
												)

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

												# self-attention output
												_UpperCAmelCase     =      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}"""
												_UpperCAmelCase     =      dataavec_layer.self_attn.out_proj.weight
												_UpperCAmelCase     =      dataavec_layer.self_attn.out_proj.bias
												_UpperCAmelCase     =      dataavec_layer.self_attn_layer_norm.weight
												_UpperCAmelCase     =      dataavec_layer.self_attn_layer_norm.bias

												# intermediate
												_UpperCAmelCase     =      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}"""
												_UpperCAmelCase     =      dataavec_layer.fca.weight
												_UpperCAmelCase     =      dataavec_layer.fca.bias

												# output
												_UpperCAmelCase     =      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}"""
												_UpperCAmelCase     =      dataavec_layer.fca.weight
												_UpperCAmelCase     =      dataavec_layer.fca.bias
												_UpperCAmelCase     =      dataavec_layer.final_layer_norm.weight
												_UpperCAmelCase     =      dataavec_layer.final_layer_norm.bias
												# end of layer

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

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

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

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


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
		)








import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=				MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	UpperCamelCase__								=				TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							if isinstance(model.config	,   UpperCAmelCase   ):
													_UpperCAmelCase ,			_UpperCAmelCase     =      list(tokenizer.lang_code_to_id.keys()   )[:2]
													_UpperCAmelCase     =      TranslationPipeline(model=UpperCAmelCase	,   tokenizer=UpperCAmelCase	,   src_lang=UpperCAmelCase	,   tgt_lang=UpperCAmelCase   )
							else:
													_UpperCAmelCase     =      TranslationPipeline(model=UpperCAmelCase	,   tokenizer=UpperCAmelCase   )
							return translator, ["Some string", "Some other text"]

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      translator('Some string'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}]   )

							_UpperCAmelCase     =      translator(['Some string']   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}]   )

							_UpperCAmelCase     =      translator(['Some string', 'other string']   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}, {'translation_text': ANY(UpperCAmelCase   )}]   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_ro'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='pt'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
							                ' Beide Beide'
							            )
							        }
							    ]	,   )

	@require_tf
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_ro'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='tf'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
							                ' Beide Beide'
							            )
							        }
							    ]	,   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_de'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='pt'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
							                ' urine urine urine urine urine urine urine'
							            )
							        }
							    ]	,   )

	@require_tf
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_de'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='tf'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
							                ' urine urine urine urine urine urine urine'
							            )
							        }
							    ]	,   )





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	@require_torch
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							with self.assertRaises(UpperCAmelCase   ):
													pipeline(task='translation_cn_to_ar'   )

							# but we do for this one
							_UpperCAmelCase     =      pipeline(task='translation_en_to_de'   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'en'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'de'   )

	@require_torch
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )
							_UpperCAmelCase     =      MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )

							_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase	,   tokenizer=UpperCAmelCase   )
							# Missing src_lang, tgt_lang
							with self.assertRaises(UpperCAmelCase   ):
													translator('This is a test'   )

							_UpperCAmelCase     =      translator('This is a test'	,   src_lang='en_XX'	,   tgt_lang='ar_AR'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'هذا إختبار'}]   )

							_UpperCAmelCase     =      translator('This is a test'	,   src_lang='en_XX'	,   tgt_lang='hi_IN'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'यह एक परीक्षण है'}]   )

							# src_lang, tgt_lang can be defined at pipeline call time
							_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase	,   tokenizer=UpperCAmelCase	,   src_lang='en_XX'	,   tgt_lang='ar_AR'   )
							_UpperCAmelCase     =      translator('This is a test'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'هذا إختبار'}]   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'patrickvonplaten/t5-tiny-random'
							_UpperCAmelCase     =      pipeline(task='translation_cn_to_ar'	,   model=UpperCAmelCase   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'cn'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'ar'   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'patrickvonplaten/t5-tiny-random'
							with pytest.warns(UpperCAmelCase	,   match=R'.*translation_en_to_de.*'   ):
													_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase   )
							self.assertEqual(translator.task	,   'translation_en_to_de'   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'en'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'de'   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							with self.assertRaises(UpperCAmelCase   ):
													pipeline(task='translation'   )
