
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
	from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__a							  =						get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_torch
@require_torchaudio
@require_sentencepiece
class        UpperCamelCase__(    unittest.TestCase	):

	"""simple docstring"""

	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





			A				=tempfile.mkdtemp()

			A				=["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
			A				=dict(zip(__UpperCAmelCase							,			range(len(__UpperCAmelCase ) ) ) )
			A				=Path(self.tmpdirname )
			save_json(__UpperCAmelCase							,			save_dir / VOCAB_FILES_NAMES["vocab_file"] )
			if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
					copyfile(__UpperCAmelCase							,			save_dir / VOCAB_FILES_NAMES["spm_file"] )

			A				=SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
			tokenizer.save_pretrained(self.tmpdirname )

			A				={
			    "feature_size": 24,
			    "num_mel_bins": 24,
			    "padding_value": 0.0,
			    "sampling_rate": 1_60_00,
			    "return_attention_mask": False,
			    "do_normalize": True,
			}
			save_json(__UpperCAmelCase							,			save_dir / FEATURE_EXTRACTOR_NAME )







	def        _a					(      self				:			List[Any]							,			**snake_case__				:			Optional[int] ):
			"""simple docstring"""





			return SpeechaTextTokenizer.from_pretrained(self.tmpdirname							,			**__UpperCAmelCase )







	def        _a					(      self				:			Any							,			**snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname							,			**__UpperCAmelCase )







	def        _a					(      self				:			List[str] ):
			"""simple docstring"""





			shutil.rmtree(self.tmpdirname )







	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			A				=self.get_tokenizer()
			A				=self.get_feature_extractor()

			A				=SpeechaTextProcessor(tokenizer=__UpperCAmelCase							,			feature_extractor=__UpperCAmelCase )

			processor.save_pretrained(self.tmpdirname )
			A				=SpeechaTextProcessor.from_pretrained(self.tmpdirname )

			self.assertEqual(processor.tokenizer.get_vocab()							,			tokenizer.get_vocab() )
			self.assertIsInstance(processor.tokenizer							,			__UpperCAmelCase )

			self.assertEqual(processor.feature_extractor.to_json_string()							,			feature_extractor.to_json_string() )
			self.assertIsInstance(processor.feature_extractor							,			__UpperCAmelCase )







	def        _a					(      self				:			List[str] ):
			"""simple docstring"""





			A				=SpeechaTextProcessor(
			    tokenizer=self.get_tokenizer()							,			feature_extractor=self.get_feature_extractor() )
			processor.save_pretrained(self.tmpdirname )

			A				=self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)" )
			A				=self.get_feature_extractor(do_normalize=__UpperCAmelCase							,			padding_value=1.0 )

			A				=SpeechaTextProcessor.from_pretrained(
			    self.tmpdirname							,			bos_token="(BOS)"							,			eos_token="(EOS)"							,			do_normalize=__UpperCAmelCase							,			padding_value=1.0 )

			self.assertEqual(processor.tokenizer.get_vocab()							,			tokenizer_add_kwargs.get_vocab() )
			self.assertIsInstance(processor.tokenizer							,			__UpperCAmelCase )

			self.assertEqual(processor.feature_extractor.to_json_string()							,			feature_extractor_add_kwargs.to_json_string() )
			self.assertIsInstance(processor.feature_extractor							,			__UpperCAmelCase )







	def        _a					(      self				:			List[str] ):
			"""simple docstring"""





			A				=self.get_feature_extractor()
			A				=self.get_tokenizer()

			A				=SpeechaTextProcessor(tokenizer=__UpperCAmelCase							,			feature_extractor=__UpperCAmelCase )

			A				=floats_list((3, 10_00) )

			A				=feature_extractor(__UpperCAmelCase							,			return_tensors="np" )
			A				=processor(__UpperCAmelCase							,			return_tensors="np" )

			for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()							,			input_processor[key].sum()							,			delta=1E-2 )







	def        _a					(      self				:			Optional[int] ):
			"""simple docstring"""





			A				=self.get_feature_extractor()
			A				=self.get_tokenizer()

			A				=SpeechaTextProcessor(tokenizer=__UpperCAmelCase							,			feature_extractor=__UpperCAmelCase )

			A				="This is a test string"

			A				=processor(text=__UpperCAmelCase )

			A				=tokenizer(__UpperCAmelCase )

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]							,			encoded_processor[key] )







	def        _a					(      self				:			Optional[Any] ):
			"""simple docstring"""





			A				=self.get_feature_extractor()
			A				=self.get_tokenizer()

			A				=SpeechaTextProcessor(tokenizer=__UpperCAmelCase							,			feature_extractor=__UpperCAmelCase )

			A				=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			A				=processor.batch_decode(__UpperCAmelCase )
			A				=tokenizer.batch_decode(__UpperCAmelCase )

			self.assertListEqual(__UpperCAmelCase							,			__UpperCAmelCase )







	def        _a					(      self				:			Union[str, Any] ):
			"""simple docstring"""





			A				=self.get_feature_extractor()
			A				=self.get_tokenizer()

			A				=SpeechaTextProcessor(tokenizer=__UpperCAmelCase							,			feature_extractor=__UpperCAmelCase )

			self.assertListEqual(
			    processor.model_input_names							,			feature_extractor.model_input_names							,			msg="`processor` and `feature_extractor` model input names do not match"							,			)






"""simple docstring"""


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        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					List[Any]:
	_lowerCAmelCase		,				_lowerCAmelCase								=os.path.split(__UpperCamelCase			)
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase       ,					checkpoint_file=__UpperCamelCase			)
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=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:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""       ,					__UpperCamelCase			)

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase			) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase			)
	model.eval()

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

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

		# self attention
		_lowerCAmelCase								=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)			)}'''
		)

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

		# self-attention output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

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

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

	_lowerCAmelCase								=model(__UpperCamelCase			)[0]
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase			)			)
	else:
		_lowerCAmelCase								=dataavec_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__":
			__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
			)



