


"""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()
A     :   Dict			    =			logging.get_logger(__name__)

A     :   List[str]			    =			"The Nymphenburg Palace is a beautiful palace in Munich!"


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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=_UpperCamelCase    , output_all_encodings=_UpperCamelCase    , use_residual=predefined_args["use_residual"]    , activation=predefined_args.get("activation"    , "gelu"      )    , layer_norm_eps=predefined_args.get("layer_norm_eps"    , _UpperCamelCase      )    , )

			# 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(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , cls=_UpperCamelCase      )

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

			original_bort.load_parameters(_UpperCamelCase    , cast_dtype=_UpperCamelCase    , ignore_extra=_UpperCamelCase      )
			__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(_UpperCamelCase      ),
			}

			__lowerCAmelCase				    =			BertConfig.from_dict(_UpperCamelCase      )
			__lowerCAmelCase				    =			BertForMaskedLM(_UpperCamelCase      )
			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(_UpperCamelCase      ) -> 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(_UpperCamelCase    , _UpperCamelCase      ):
						__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(_UpperCamelCase      )["input_ids"]

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

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

			__lowerCAmelCase				    =			tokenizer.encode_plus(_UpperCamelCase    , return_tensors="pt"      )
			__lowerCAmelCase				    =			hf_bort_model(**_UpperCamelCase      )[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(_UpperCamelCase    , _UpperCamelCase    , 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:"    , _UpperCamelCase      )


if __name__ == "__main__":
	A     :   Optional[int]			    =			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."
	)
	A     :   Optional[Any]			    =			parser.parse_args()
	convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			tempfile.mkdtemp()

										__lowerCAmelCase				    =			[
										    "[UNK]",
										    "[CLS]",
										    "[SEP]",
										    "[PAD]",
										    "[MASK]",
										    "的",
										    "价",
										    "格",
										    "是",
										    "15",
										    "便",
										    "alex",
										    "##andra",
										    "，",
										    "。",
										    "-",
										    "t",
										    "shirt",
										]
										__lowerCAmelCase				    =			os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES["vocab_file"]	)
										with open(self.vocab_file    ,   "w"    ,   encoding="utf-8"	) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)

										__lowerCAmelCase				    =			{
										    "do_resize": True,
										    "size": {"height": 2_24, "width": 2_24},
										    "do_center_crop": True,
										    "crop_size": {"height": 18, "width": 18},
										    "do_normalize": True,
										    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
										    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
										    "do_convert_rgb": True,
										}
										__lowerCAmelCase				    =			os.path.join(self.tmpdirname    ,   __a	)
										with open(self.image_processor_file    ,   "w"    ,   encoding="utf-8"	) as fp:
													json.dump(__a    ,   __a	)

							def 			snake_case				(     self    ,   **__a	):
										return BertTokenizer.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self    ,   **__a	):
										return BertTokenizerFast.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self    ,   **__a	):
										return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self	):
										shutil.rmtree(self.tmpdirname	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			[np.random.randint(2_55    ,   size=(3, 30, 4_00)    ,   dtype=np.uinta	)]

										__lowerCAmelCase				    =			[Image.fromarray(np.moveaxis(__a    ,   0    ,   -1	)	) for x in image_inputs]

										return image_inputs

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_tokenizer()
										__lowerCAmelCase				    =			self.get_rust_tokenizer()
										__lowerCAmelCase				    =			self.get_image_processor()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)
										processor_slow.save_pretrained(self.tmpdirname	)
										__lowerCAmelCase				    =			ChineseCLIPProcessor.from_pretrained(self.tmpdirname    ,   use_fast=__a	)

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)
										processor_fast.save_pretrained(self.tmpdirname	)
										__lowerCAmelCase				    =			ChineseCLIPProcessor.from_pretrained(self.tmpdirname	)

										self.assertEqual(processor_slow.tokenizer.get_vocab()    ,   tokenizer_slow.get_vocab()	)
										self.assertEqual(processor_fast.tokenizer.get_vocab()    ,   tokenizer_fast.get_vocab()	)
										self.assertEqual(tokenizer_slow.get_vocab()    ,   tokenizer_fast.get_vocab()	)
										self.assertIsInstance(processor_slow.tokenizer    ,   __a	)
										self.assertIsInstance(processor_fast.tokenizer    ,   __a	)

										self.assertEqual(processor_slow.image_processor.to_json_string()    ,   image_processor.to_json_string()	)
										self.assertEqual(processor_fast.image_processor.to_json_string()    ,   image_processor.to_json_string()	)
										self.assertIsInstance(processor_slow.image_processor    ,   __a	)
										self.assertIsInstance(processor_fast.image_processor    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=self.get_tokenizer()    ,   image_processor=self.get_image_processor()	)
										processor.save_pretrained(self.tmpdirname	)

										__lowerCAmelCase				    =			self.get_tokenizer(cls_token="(CLS)"    ,   sep_token="(SEP)"	)
										__lowerCAmelCase				    =			self.get_image_processor(do_normalize=__a	)

										__lowerCAmelCase				    =			ChineseCLIPProcessor.from_pretrained(
										    self.tmpdirname    ,   cls_token="(CLS)"    ,   sep_token="(SEP)"    ,   do_normalize=__a	)

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

										self.assertEqual(processor.image_processor.to_json_string()    ,   image_processor_add_kwargs.to_json_string()	)
										self.assertIsInstance(processor.image_processor    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			self.prepare_image_inputs()

										__lowerCAmelCase				    =			image_processor(__a    ,   return_tensors="np"	)
										__lowerCAmelCase				    =			processor(images=__a    ,   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 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			"Alexandra，T-shirt的价格是15便士。"

										__lowerCAmelCase				    =			processor(text=__a	)

										__lowerCAmelCase				    =			tokenizer(__a	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			"Alexandra，T-shirt的价格是15便士。"
										__lowerCAmelCase				    =			self.prepare_image_inputs()

										__lowerCAmelCase				    =			processor(text=__a    ,   images=__a	)

										self.assertListEqual(list(inputs.keys()	)    ,   ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)

										# test if it raises when no input is passed
										with pytest.raises(__a	):
													processor()

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

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

										__lowerCAmelCase				    =			processor.batch_decode(__a	)
										__lowerCAmelCase				    =			tokenizer.batch_decode(__a	)

										self.assertListEqual(__a    ,   __a	)



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			"Alexandra，T-shirt的价格是15便士。"
										__lowerCAmelCase				    =			self.prepare_image_inputs()

										__lowerCAmelCase				    =			processor(text=__a    ,   images=__a	)

										self.assertListEqual(list(inputs.keys()	)    ,   processor.model_input_names	)




