"""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()
_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		"The Nymphenburg Palace is a beautiful palace in Munich!"

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			{
							    """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,
							}

							A__       :     List[Any]								=			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
							A__       :     Union[str, Any]								=			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=lowerCAmelCase			,  output_all_encodings=lowerCAmelCase			,  use_residual=predefined_args["""use_residual"""]			,  activation=predefined_args.get("""activation"""			,  """gelu"""							)			,  layer_norm_eps=predefined_args.get("""layer_norm_eps"""			,  lowerCAmelCase							)			,  )

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

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

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

							original_bort.load_parameters(lowerCAmelCase			,  cast_dtype=lowerCAmelCase			,  ignore_extra=lowerCAmelCase							)
							A__       :     int								=			original_bort._collect_params_with_prefix()

							# Build our config 🤗
							A__       :     Dict								=			{
							    """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(lowerCAmelCase							),
							}

							A__       :     Any								=			BertConfig.from_dict(lowerCAmelCase							)
							A__       :     int								=			BertForMaskedLM(lowerCAmelCase							)
							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(lowerCAmelCase							) -> 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(lowerCAmelCase			,  lowerCAmelCase							):
														A__       :     List[Any]								=			hf_param.shape

														A__       :     Any								=			to_torch(params[gluon_param]							)
														A__       :     Optional[Any]								=			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

							A__       :     Union[str, Any]								=			check_and_map_params(
							    hf_bort_model.bert.embeddings.word_embeddings.weight			,  """word_embed.0.weight"""							)
							A__       :     int								=			check_and_map_params(
							    hf_bort_model.bert.embeddings.position_embeddings.weight			,  """encoder.position_weight"""							)
							A__       :     List[Any]								=			check_and_map_params(
							    hf_bort_model.bert.embeddings.LayerNorm.bias			,  """encoder.layer_norm.beta"""							)
							A__       :     Any								=			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)
							A__       :     Tuple								=			torch.zeros_like(
							    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data							)

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

														# self attention
														A__       :     BertSelfAttention								=			layer.attention.self

														A__       :     str								=			check_and_map_params(
														    self_attn.key.bias.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''							)

														A__       :     Optional[int]								=			check_and_map_params(
														    self_attn.key.weight.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''							)
														A__       :     Dict								=			check_and_map_params(
														    self_attn.query.bias.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''							)
														A__       :     Dict								=			check_and_map_params(
														    self_attn.query.weight.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''							)
														A__       :     Tuple								=			check_and_map_params(
														    self_attn.value.bias.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''							)
														A__       :     Dict								=			check_and_map_params(
														    self_attn.value.weight.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''							)

														# self attention output
														A__       :     BertSelfOutput								=			layer.attention.output

														A__       :     Dict								=			check_and_map_params(
														    self_output.dense.bias			,  F'''encoder.transformer_cells.{i}.proj.bias'''							)
														A__       :     str								=			check_and_map_params(
														    self_output.dense.weight			,  F'''encoder.transformer_cells.{i}.proj.weight'''							)
														A__       :     str								=			check_and_map_params(
														    self_output.LayerNorm.bias			,  F'''encoder.transformer_cells.{i}.layer_norm.beta'''							)
														A__       :     Any								=			check_and_map_params(
														    self_output.LayerNorm.weight			,  F'''encoder.transformer_cells.{i}.layer_norm.gamma'''							)

														# intermediate
														A__       :     BertIntermediate								=			layer.intermediate

														A__       :     str								=			check_and_map_params(
														    intermediate.dense.bias			,  F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''							)
														A__       :     List[str]								=			check_and_map_params(
														    intermediate.dense.weight			,  F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''							)

														# output
														A__       :     BertOutput								=			layer.output

														A__       :     Optional[Any]								=			check_and_map_params(
														    bert_output.dense.bias			,  F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''							)
														A__       :     Tuple								=			check_and_map_params(
														    bert_output.dense.weight			,  F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''							)
														A__       :     Union[str, Any]								=			check_and_map_params(
														    bert_output.LayerNorm.bias			,  F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''							)
														A__       :     Any								=			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
							A__       :     Optional[Any]								=			RobertaTokenizer.from_pretrained("""roberta-base"""							)

							A__       :     Any								=			tokenizer.encode_plus(lowerCAmelCase							)["""input_ids"""]

							# Get gluon output
							A__       :     Any								=			mx.nd.array([input_ids]							)
							A__       :     str								=			original_bort(inputs=lowerCAmelCase			,  token_types=[]							)

							# Get Transformer output (save and reload model again)
							hf_bort_model.save_pretrained(lowerCAmelCase							)
							A__       :     Optional[int]								=			BertModel.from_pretrained(lowerCAmelCase							)
							hf_bort_model.eval()

							A__       :     List[Any]								=			tokenizer.encode_plus(lowerCAmelCase			,  return_tensors="""pt"""							)
							A__       :     Union[str, Any]								=			hf_bort_model(**lowerCAmelCase							)[0]

							A__       :     List[Any]								=			output_gluon[0].asnumpy()
							A__       :     Any								=			output_hf[0].detach().numpy()

							A__       :     Tuple								=			np.max(np.abs(hf_layer - gluon_layer							)							).item()
							A__       :     Tuple								=			np.allclose(lowerCAmelCase			,  lowerCAmelCase			,  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:"""			,  lowerCAmelCase							)


if __name__ == "__main__":
				_UpperCamelCase								=		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."
				)
				_UpperCamelCase								=		parser.parse_args()
				convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""



import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


_UpperCamelCase								=		{
    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
    "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}

def  _A(		lowerCAmelCase							):
							A__       :     Any								=			["""layers""", """blocks"""]
							for k in ignore_keys:
														state_dict.pop(lowerCAmelCase			,  lowerCAmelCase							)


_UpperCamelCase								=		{
    "blocks": "layers",
    "mlp.0": "fc1",
    "mlp.2": "fc2",
    "mlp_ln": "final_layer_norm",
    ".attn.query": ".self_attn.q_proj",
    ".attn.key": ".self_attn.k_proj",
    ".attn.value": ".self_attn.v_proj",
    ".attn_ln": ".self_attn_layer_norm",
    ".attn.out": ".self_attn.out_proj",
    ".cross_attn.query": ".encoder_attn.q_proj",
    ".cross_attn.key": ".encoder_attn.k_proj",
    ".cross_attn.value": ".encoder_attn.v_proj",
    ".cross_attn_ln": ".encoder_attn_layer_norm",
    ".cross_attn.out": ".encoder_attn.out_proj",
    "decoder.ln.": "decoder.layer_norm.",
    "encoder.ln.": "encoder.layer_norm.",
    "token_embedding": "embed_tokens",
    "encoder.positional_embedding": "encoder.embed_positions.weight",
    "decoder.positional_embedding": "decoder.embed_positions.weight",
    "ln_post": "layer_norm",
}

def  _A(		lowerCAmelCase							):
							A__       :     str								=			list(s_dict.keys()							)
							for key in keys:
														A__       :     Optional[Any]								=			key
														for k, v in WHISPER_MAPPING.items():
																					if k in key:
																												A__       :     Optional[Any]								=			new_key.replace(lowerCAmelCase			,  lowerCAmelCase							)

														print(F'''{key} -> {new_key}'''							)

														A__       :     Any								=			s_dict.pop(lowerCAmelCase							)
							return s_dict

def  _A(		lowerCAmelCase							):
							A__				,							A__       :     Union[str, Any]								=			emb.weight.shape
							A__       :     Optional[Any]								=			nn.Linear(lowerCAmelCase			,  lowerCAmelCase			,  bias=lowerCAmelCase							)
							A__       :     int								=			emb.weight.data
							return lin_layer

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							os.makedirs(lowerCAmelCase			,  exist_ok=lowerCAmelCase							)
							A__       :     Union[str, Any]								=			os.path.basename(lowerCAmelCase							)

							A__       :     str								=			url.split("""/"""							)[-2]
							A__       :     Union[str, Any]								=			os.path.join(lowerCAmelCase			,  lowerCAmelCase							)

							if os.path.exists(lowerCAmelCase							) and not os.path.isfile(lowerCAmelCase							):
														raise RuntimeError(F'''{download_target} exists and is not a regular file'''							)

							if os.path.isfile(lowerCAmelCase							):
														A__       :     Tuple								=			open(lowerCAmelCase			,  """rb"""							).read()
														if hashlib.shaaaa(lowerCAmelCase							).hexdigest() == expected_shaaaa:
																					return model_bytes
														else:
																					warnings.warn(F'''{download_target} exists, but the SHA256 checksum does not match; re-downloading the file'''							)

							with urllib.request.urlopen(lowerCAmelCase							) as source, open(lowerCAmelCase			,  """wb"""							) as output:
														with tqdm(
														    total=int(source.info().get("""Content-Length"""							)							)			,  ncols=80			,  unit="""iB"""			,  unit_scale=lowerCAmelCase			,  unit_divisor=1024							) as loop:
																					while True:
																												A__       :     Dict								=			source.read(8192							)
																												if not buffer:
																																			break

																												output.write(lowerCAmelCase							)
																												loop.update(len(lowerCAmelCase							)							)

							A__       :     List[str]								=			open(lowerCAmelCase			,  """rb"""							).read()
							if hashlib.shaaaa(lowerCAmelCase							).hexdigest() != expected_shaaaa:
														raise RuntimeError(
														    """Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."""							)

							return model_bytes

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							if ".pt" not in checkpoint_path:
														A__       :     str								=			_download(_MODELS[checkpoint_path]							)
							else:
														A__       :     Optional[int]								=			torch.load(lowerCAmelCase			,  map_location="""cpu"""							)
							A__       :     List[str]								=			original_checkpoint["""dims"""]
							A__       :     List[Any]								=			original_checkpoint["""model_state_dict"""]
							A__       :     Dict								=			state_dict["""decoder.token_embedding.weight"""]
							remove_ignore_keys_(lowerCAmelCase							)
							rename_keys(lowerCAmelCase							)
							A__       :     Any								=			True
							A__       :     List[str]								=			state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

							A__       :     str								=			WhisperConfig(
							    vocab_size=dimensions["""n_vocab"""]			,  encoder_ffn_dim=lowerCAmelCase			,  decoder_ffn_dim=lowerCAmelCase			,  num_mel_bins=dimensions["""n_mels"""]			,  d_model=dimensions["""n_audio_state"""]			,  max_target_positions=dimensions["""n_text_ctx"""]			,  encoder_layers=dimensions["""n_audio_layer"""]			,  encoder_attention_heads=dimensions["""n_audio_head"""]			,  decoder_layers=dimensions["""n_text_layer"""]			,  decoder_attention_heads=dimensions["""n_text_state"""]			,  max_source_positions=dimensions["""n_audio_ctx"""]			,  )

							A__       :     int								=			WhisperForConditionalGeneration(lowerCAmelCase							)
							A__				,							A__       :     List[Any]								=			model.model.load_state_dict(lowerCAmelCase			,  strict=lowerCAmelCase							)
							if len(lowerCAmelCase							) > 0 and not set(lowerCAmelCase							) <= {
							    "encoder.embed_positions.weights",
							    "decoder.embed_positions.weights",
							}:
														raise ValueError(
														    """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
														    F''' but all the following weights are missing {missing}'''							)

							if tie_embeds:
														A__       :     Tuple								=			make_linear_from_emb(model.model.decoder.embed_tokens							)
							else:
														A__       :     Any								=			proj_out_weights

							model.save_pretrained(lowerCAmelCase							)


if __name__ == "__main__":
				_UpperCamelCase								=		argparse.ArgumentParser()
				# # Required parameters
				parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				_UpperCamelCase								=		parser.parse_args()

				convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





