

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()
lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  '''Hello world! cécé herlolip'''


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					__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__":
		lowerCamelCase   					=  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.'''
		)
		lowerCamelCase   					=  parser.parse_args()
		convert_dataavec_checkpoint_to_pytorch(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)









import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

lowerCamelCase   					=  logging.get_logger(__name__)


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase       =				F"""{sampling_rate}"""
					__lowerCAmelCase       =				'1'
					__lowerCAmelCase       =				'f32le'
					__lowerCAmelCase       =				[
					    'ffmpeg',
					    '-i',
					    'pipe:0',
					    '-ac',
					    ac,
					    '-ar',
					    ar,
					    '-f',
					    format_for_conversion,
					    '-hide_banner',
					    '-loglevel',
					    'quiet',
					    'pipe:1',
					]

					try:
										__lowerCAmelCase       =				subprocess.Popen(UpperCAmelCase__		,       stdin=subprocess.PIPE		,       stdout=subprocess.PIPE   )
					except FileNotFoundError:
										raise ValueError('ffmpeg was not found but is required to load audio files from filename'   )
					__lowerCAmelCase       =				ffmpeg_process.communicate(UpperCAmelCase__   )
					__lowerCAmelCase       =				output_stream[0]

					__lowerCAmelCase       =				np.frombuffer(UpperCAmelCase__		,       np.floataa   )
					if audio.shape[0] == 0:
										raise ValueError('Malformed soundfile'   )
					return audio





@add_end_docstrings(_a				)
class      snake_case_      (					_a				):


							"""simple docstring"""





							def __init__(			self       ,    *_A       ,    **_A				):
												# Default, might be overriden by the model.config.
												__lowerCAmelCase       =				5
												super().__init__(*_A       ,    **_A				)

												if self.framework != "pt":
																	raise ValueError(F"""The {self.__class__} is only available in PyTorch."""				)

												self.check_model_type(_A				)




							def __call__(			self       ,    _A       ,    **_A       ,    ):
												return super().__call__(_A       ,    **_A				)




							def 							A__	(			self       ,    _A=None       ,    **_A				):
												# No parameters on this pipeline right now
												__lowerCAmelCase       =				{}
												if top_k is not None:
																	if top_k > self.model.config.num_labels:
																						__lowerCAmelCase       =				self.model.config.num_labels
																	__lowerCAmelCase       =				top_k
												return {}, {}, postprocess_params




							def 							A__	(			self       ,    _A				):
												if isinstance(_A       ,    _A				):
																	if inputs.startswith('http://'				) or inputs.startswith('https://'				):
																						# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																						# like http_huggingface_co.png
																						__lowerCAmelCase       =				requests.get(_A				).content
																	else:
																						with open(_A       ,    'rb'				) as f:
																											__lowerCAmelCase       =				f.read()

												if isinstance(_A       ,    _A				):
																	__lowerCAmelCase       =				ffmpeg_read(_A       ,    self.feature_extractor.sampling_rate				)

												if isinstance(_A       ,    _A				):
																	# Accepting `"array"` which is the key defined in `datasets` for
																	# better integration
																	if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
																						raise ValueError(
																						    'When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '
																						    '"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
																						    'containing the sampling_rate associated with that array'				)

																	__lowerCAmelCase       =				inputs.pop('raw'       ,    _A				)
																	if _inputs is None:
																						# Remove path which will not be used from `datasets`.
																						inputs.pop('path'       ,    _A				)
																						__lowerCAmelCase       =				inputs.pop('array'       ,    _A				)
																	__lowerCAmelCase       =				inputs.pop('sampling_rate'				)
																	__lowerCAmelCase       =				_inputs
																	if in_sampling_rate != self.feature_extractor.sampling_rate:
																						import torch

																						if is_torchaudio_available():
																											from torchaudio import functional as F
																						else:
																											raise ImportError(
																											    'torchaudio is required to resample audio samples in AudioClassificationPipeline. '
																											    'The torchaudio package can be installed through: `pip install torchaudio`.'				)

																						__lowerCAmelCase       =				F.resample(
																						    torch.from_numpy(_A				)       ,    _A       ,    self.feature_extractor.sampling_rate				).numpy()

												if not isinstance(_A       ,    np.ndarray				):
																	raise ValueError('We expect a numpy ndarray as input'				)
												if len(inputs.shape				) != 1:
																	raise ValueError('We expect a single channel audio input for AudioClassificationPipeline'				)

												__lowerCAmelCase       =				self.feature_extractor(
												    _A       ,    sampling_rate=self.feature_extractor.sampling_rate       ,    return_tensors='pt'				)
												return processed




							def 							A__	(			self       ,    _A				):
												__lowerCAmelCase       =				self.model(**_A				)
												return model_outputs




							def 							A__	(			self       ,    _A       ,    _A=5				):
												__lowerCAmelCase       =				model_outputs.logits[0].softmax(-1				)
												__lowerCAmelCase,						__lowerCAmelCase       =				probs.topk(_A				)

												__lowerCAmelCase       =				scores.tolist()
												__lowerCAmelCase       =				ids.tolist()

												__lowerCAmelCase       =				[{'score': score, 'label': self.model.config.idalabel[_id]} for score, _id in zip(_A       ,    _A				)]

												return labels






