'''simple docstring'''




import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
						raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
						raise Exception('''requires mxnet == 1.5.0''')

logging.set_verbosity_info()
__lowercase          =  logging.get_logger(__name__)

__lowercase          =  '''The Nymphenburg Palace is a beautiful palace in Munich!'''






def 				snake_case__							(			_A:					str      ,   _A:					str )    ->	str:

		'''simple docstring'''


		lowerCAmelCase   					=  {
		    """attention_cell""": """multi_head""",
		    """num_layers""": 4,
		    """units""": 1024,
		    """hidden_size""": 768,
		    """max_length""": 512,
		    """num_heads""": 8,
		    """scaled""": True,
		    """dropout""": 0.1,
		    """use_residual""": True,
		    """embed_size""": 1024,
		    """embed_dropout""": 0.1,
		    """word_embed""": None,
		    """layer_norm_eps""": 1e-5,
		    """token_type_vocab_size""": 2,
		}

		lowerCAmelCase   					=  bort_4_8_768_1024_hparams

		# Let's construct the original Bort model here
		# Taken from official BERT implementation, see:
		# https://github.com/alexa/bort/blob/master/bort/bort.py
		lowerCAmelCase   					=  BERTEncoder(
		    attention_cell=predefined_args["""attention_cell"""]      ,   num_layers=predefined_args["""num_layers"""]      ,   units=predefined_args["""units"""]      ,   hidden_size=predefined_args["""hidden_size"""]      ,   max_length=predefined_args["""max_length"""]      ,   num_heads=predefined_args["""num_heads"""]      ,   scaled=predefined_args["""scaled"""]      ,   dropout=predefined_args["""dropout"""]      ,   output_attention=_A      ,   output_all_encodings=_A      ,   use_residual=predefined_args["""use_residual"""]      ,   activation=predefined_args.get("""activation"""      ,   """gelu""" )      ,   layer_norm_eps=predefined_args.get("""layer_norm_eps"""      ,   _A )      ,   )

		# Vocab information needs to be fetched first
		# It's the same as RoBERTa, so RobertaTokenizer can be used later
		lowerCAmelCase   					=  """openwebtext_ccnews_stories_books_cased"""

		# Specify download folder to Gluonnlp's vocab
		lowerCAmelCase   					=  os.path.join(get_home_dir()      ,   """models""" )
		lowerCAmelCase   					=  _load_vocab(_A      ,   _A      ,   _A      ,   cls=_A )

		lowerCAmelCase   					=  nlp.model.BERTModel(
		    _A      ,   len(_A )      ,   units=predefined_args["""units"""]      ,   embed_size=predefined_args["""embed_size"""]      ,   embed_dropout=predefined_args["""embed_dropout"""]      ,   word_embed=predefined_args["""word_embed"""]      ,   use_pooler=_A      ,   use_token_type_embed=_A      ,   token_type_vocab_size=predefined_args["""token_type_vocab_size"""]      ,   use_classifier=_A      ,   use_decoder=_A      ,   )

		original_bort.load_parameters(_A      ,   cast_dtype=_A      ,   ignore_extra=_A )
		lowerCAmelCase   					=  original_bort._collect_params_with_prefix()

		# Build our config 🤗
		lowerCAmelCase   					=  {
		    """architectures""": ["""BertForMaskedLM"""],
		    """attention_probs_dropout_prob""": predefined_args["""dropout"""],
		    """hidden_act""": """gelu""",
		    """hidden_dropout_prob""": predefined_args["""dropout"""],
		    """hidden_size""": predefined_args["""embed_size"""],
		    """initializer_range""": 0.02,
		    """intermediate_size""": predefined_args["""hidden_size"""],
		    """layer_norm_eps""": predefined_args["""layer_norm_eps"""],
		    """max_position_embeddings""": predefined_args["""max_length"""],
		    """model_type""": """bort""",
		    """num_attention_heads""": predefined_args["""num_heads"""],
		    """num_hidden_layers""": predefined_args["""num_layers"""],
		    """pad_token_id""": 1,  # 2 = BERT, 1 = RoBERTa
		    """type_vocab_size""": 1,  # 2 = BERT, 1 = RoBERTa
		    """vocab_size""": len(_A ),
		}

		lowerCAmelCase   					=  BertConfig.from_dict(_A )
		lowerCAmelCase   					=  BertForMaskedLM(_A )
		hf_bort_model.eval()

		# Parameter mapping table (Gluonnlp to Transformers)
		# * denotes layer index
		#
		# | Gluon Parameter                                                | Transformers Parameter
		# | -------------------------------------------------------------- | ----------------------
		# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
		# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
		# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
		# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
		# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
		# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
		# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

		# Helper function to convert MXNET Arrays to PyTorch
		def to_torch(_A:					int ) -> nn.Parameter:
				return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )

		# Check param shapes and map new HF param back
		def check_and_map_params(_A:					Tuple      ,   _A:					str ):
				lowerCAmelCase   					=  hf_param.shape

				lowerCAmelCase   					=  to_torch(params[gluon_param] )
				lowerCAmelCase   					=  gluon_param.shape

				assert (
				    shape_hf == shape_gluon
				), f"The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"

				return gluon_param

		lowerCAmelCase   					=  check_and_map_params(
		    hf_bort_model.bert.embeddings.word_embeddings.weight      ,   """word_embed.0.weight""" )
		lowerCAmelCase   					=  check_and_map_params(
		    hf_bort_model.bert.embeddings.position_embeddings.weight      ,   """encoder.position_weight""" )
		lowerCAmelCase   					=  check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.bias      ,   """encoder.layer_norm.beta""" )
		lowerCAmelCase   					=  check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.weight      ,   """encoder.layer_norm.gamma""" )

		# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
		lowerCAmelCase   					=  torch.zeros_like(
		    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )

		for i in range(hf_bort_config.num_hidden_layers ):
				lowerCAmelCase   					=  hf_bort_model.bert.encoder.layer[i]

				# self attention
				lowerCAmelCase   					=  layer.attention.self

				lowerCAmelCase   					=  check_and_map_params(
				    self_attn.key.bias.data      ,   f"encoder.transformer_cells.{i}.attention_cell.proj_key.bias" )

				lowerCAmelCase   					=  check_and_map_params(
				    self_attn.key.weight.data      ,   f"encoder.transformer_cells.{i}.attention_cell.proj_key.weight" )
				lowerCAmelCase   					=  check_and_map_params(
				    self_attn.query.bias.data      ,   f"encoder.transformer_cells.{i}.attention_cell.proj_query.bias" )
				lowerCAmelCase   					=  check_and_map_params(
				    self_attn.query.weight.data      ,   f"encoder.transformer_cells.{i}.attention_cell.proj_query.weight" )
				lowerCAmelCase   					=  check_and_map_params(
				    self_attn.value.bias.data      ,   f"encoder.transformer_cells.{i}.attention_cell.proj_value.bias" )
				lowerCAmelCase   					=  check_and_map_params(
				    self_attn.value.weight.data      ,   f"encoder.transformer_cells.{i}.attention_cell.proj_value.weight" )

				# self attention output
				lowerCAmelCase   					=  layer.attention.output

				lowerCAmelCase   					=  check_and_map_params(
				    self_output.dense.bias      ,   f"encoder.transformer_cells.{i}.proj.bias" )
				lowerCAmelCase   					=  check_and_map_params(
				    self_output.dense.weight      ,   f"encoder.transformer_cells.{i}.proj.weight" )
				lowerCAmelCase   					=  check_and_map_params(
				    self_output.LayerNorm.bias      ,   f"encoder.transformer_cells.{i}.layer_norm.beta" )
				lowerCAmelCase   					=  check_and_map_params(
				    self_output.LayerNorm.weight      ,   f"encoder.transformer_cells.{i}.layer_norm.gamma" )

				# intermediate
				lowerCAmelCase   					=  layer.intermediate

				lowerCAmelCase   					=  check_and_map_params(
				    intermediate.dense.bias      ,   f"encoder.transformer_cells.{i}.ffn.ffn_1.bias" )
				lowerCAmelCase   					=  check_and_map_params(
				    intermediate.dense.weight      ,   f"encoder.transformer_cells.{i}.ffn.ffn_1.weight" )

				# output
				lowerCAmelCase   					=  layer.output

				lowerCAmelCase   					=  check_and_map_params(
				    bert_output.dense.bias      ,   f"encoder.transformer_cells.{i}.ffn.ffn_2.bias" )
				lowerCAmelCase   					=  check_and_map_params(
				    bert_output.dense.weight      ,   f"encoder.transformer_cells.{i}.ffn.ffn_2.weight" )
				lowerCAmelCase   					=  check_and_map_params(
				    bert_output.LayerNorm.bias      ,   f"encoder.transformer_cells.{i}.ffn.layer_norm.beta" )
				lowerCAmelCase   					=  check_and_map_params(
				    bert_output.LayerNorm.weight      ,   f"encoder.transformer_cells.{i}.ffn.layer_norm.gamma" )

		# Save space and energy 🎄
		hf_bort_model.half()

		# Compare output of both models
		lowerCAmelCase   					=  RobertaTokenizer.from_pretrained("""roberta-base""" )

		lowerCAmelCase   					=  tokenizer.encode_plus(_A )["""input_ids"""]

		# Get gluon output
		lowerCAmelCase   					=  mx.nd.array([input_ids] )
		lowerCAmelCase   					=  original_bort(inputs=_A      ,   token_types=[] )

		# Get Transformer output (save and reload model again)
		hf_bort_model.save_pretrained(_A )
		lowerCAmelCase   					=  BertModel.from_pretrained(_A )
		hf_bort_model.eval()

		lowerCAmelCase   					=  tokenizer.encode_plus(_A      ,   return_tensors="""pt""" )
		lowerCAmelCase   					=  hf_bort_model(**_A )[0]

		lowerCAmelCase   					=  output_gluon[0].asnumpy()
		lowerCAmelCase   					=  output_hf[0].detach().numpy()

		lowerCAmelCase   					=  np.max(np.abs(hf_layer - gluon_layer ) ).item()
		lowerCAmelCase   					=  np.allclose(_A      ,   _A      ,   atol=1e-3 )

		if success:
				print("""✔️ Both model do output the same tensors""" )
		else:
				print("""❌ Both model do **NOT** output the same tensors""" )
				print("""Absolute difference is:"""      ,   _A )


if __name__ == "__main__":
						__lowercase          =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						__lowercase          =  parser.parse_args()
						convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     str      =       '''EncodecFeatureExtractor'''
			UpperCAmelCase_			:     Dict      =       ('''T5Tokenizer''', '''T5TokenizerFast''')



			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					super().__init__(__lowerCAmelCase       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  self.feature_extractor
					lowerCAmelCase   					=  False



			def 							a_       (   self       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True):




					"""simple docstring"""


					return self.tokenizer.get_decoder_prompt_ids(task=__lowerCAmelCase       ,    language=__lowerCAmelCase       ,    no_timestamps=__lowerCAmelCase)



			def __call__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					if self._in_target_context_manager:
							return self.current_processor(*__lowerCAmelCase       ,    **__lowerCAmelCase)

					lowerCAmelCase   					=  kwargs.pop("""audio"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""sampling_rate"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""text"""       ,    __lowerCAmelCase)
					if len(__lowerCAmelCase) > 0:
							lowerCAmelCase   					=  args[0]
							lowerCAmelCase   					=  args[1:]

					if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process.""")

					if text is not None:
							lowerCAmelCase   					=  self.tokenizer(__lowerCAmelCase       ,    **__lowerCAmelCase)

					if audio is not None:
							lowerCAmelCase   					=  self.feature_extractor(__lowerCAmelCase       ,    *__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    **__lowerCAmelCase)

					if audio is None:
							return inputs

					elif text is None:
							return audio_inputs

					else:
							lowerCAmelCase   					=  audio_inputs["""input_values"""]
							if "padding_mask" in audio_inputs:
									lowerCAmelCase   					=  audio_inputs["""padding_mask"""]
							return inputs



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  kwargs.pop("""audio"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""padding_mask"""       ,    __lowerCAmelCase)

					if len(__lowerCAmelCase) > 0:
							lowerCAmelCase   					=  args[0]
							lowerCAmelCase   					=  args[1:]

					if audio_values is not None:
							return self._decode_audio(__lowerCAmelCase       ,    padding_mask=__lowerCAmelCase)
					else:
							return self.tokenizer.batch_decode(*__lowerCAmelCase       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.decode(*__lowerCAmelCase       ,    **__lowerCAmelCase)







			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  audio_values.shape

					if padding_mask is None:
							return list(__lowerCAmelCase)

					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)

					# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
					# token (so that the generated audio values are **not** treated as padded tokens)
					lowerCAmelCase   					=  seq_len - padding_mask.shape[-1]
					lowerCAmelCase   					=  1 - self.feature_extractor.padding_value
					lowerCAmelCase   					=  np.pad(__lowerCAmelCase       ,    ((0, 0), (0, difference))       ,    """constant"""       ,    constant_values=__lowerCAmelCase)

					lowerCAmelCase   					=  audio_values.tolist()
					for i in range(__lowerCAmelCase):
							lowerCAmelCase   					=  np.asarray(audio_values[i])[
							    padding_mask[i][None, :] != self.feature_extractor.padding_value
							]
							lowerCAmelCase   					=  sliced_audio.reshape(__lowerCAmelCase       ,    -1)

					return audio_values




