






"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


lowercase    			=       logging.get_logger(__name__)

class 						A_							(      _UpperCamelCase  ):

				'''simple docstring'''





				UpperCAmelCase__        =				['''pixel_values''']







				def __init__(							self							:						List[str]						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Dict[str, int] = None						,    __lowerCamelCase							:						float = None						,    __lowerCamelCase							:						PILImageResampling = PILImageResampling.BILINEAR						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Union[int, float] = 1 / 2_5_5						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[Union[float, List[float]]] = None						,    __lowerCamelCase							:						Optional[Union[float, List[float]]] = None						,    **__lowerCamelCase							:						Tuple						,    )				->					int:
										super().__init__(**__lowerCamelCase	)
										__magic_name__            =     size if size is not None else {"shortest_edge": 3_8_4}
										__magic_name__            =     get_size_dict(__lowerCamelCase						,    default_to_square=__lowerCamelCase	)

										__magic_name__            =     do_resize
										__magic_name__            =     size
										# Default value set here for backwards compatibility where the value in config is None
										__magic_name__            =     crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
										__magic_name__            =     resample
										__magic_name__            =     do_rescale
										__magic_name__            =     rescale_factor
										__magic_name__            =     do_normalize
										__magic_name__            =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__magic_name__            =     image_std if image_std is not None else IMAGENET_STANDARD_STD







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						np.ndarray						,    __lowerCamelCase							:						Dict[str, int]						,    __lowerCamelCase							:						float						,    __lowerCamelCase							:						PILImageResampling = PILImageResampling.BICUBIC						,    __lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None						,    **__lowerCamelCase							:						Tuple						,    )				->					Union[str, Any]:
										__magic_name__            =     get_size_dict(__lowerCamelCase						,    default_to_square=__lowerCamelCase	)
										if "shortest_edge" not in size:
																raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''	)
										__magic_name__            =     size["shortest_edge"]

										if shortest_edge < 3_8_4:
																# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																__magic_name__            =     int(shortest_edge / crop_pct	)
																__magic_name__            =     get_resize_output_image_size(__lowerCamelCase						,    size=__lowerCamelCase						,    default_to_square=__lowerCamelCase	)
																__magic_name__            =     resize(image=__lowerCamelCase						,    size=__lowerCamelCase						,    resample=__lowerCamelCase						,    data_format=__lowerCamelCase						,    **__lowerCamelCase	)
																# then crop to (shortest_edge, shortest_edge)
																return center_crop(image=__lowerCamelCase						,    size=(shortest_edge, shortest_edge)						,    data_format=__lowerCamelCase						,    **__lowerCamelCase	)
										else:
																# warping (no cropping) when evaluated at 384 or larger
																return resize(
																    __lowerCamelCase						,    size=(shortest_edge, shortest_edge)						,    resample=__lowerCamelCase						,    data_format=__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						np.ndarray						,    __lowerCamelCase							:						Union[int, float]						,    __lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None						,    **__lowerCamelCase							:						List[str]						,    )				->					Union[str, Any]:
										return rescale(__lowerCamelCase						,    scale=__lowerCamelCase						,    data_format=__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						np.ndarray						,    __lowerCamelCase							:						Union[float, List[float]]						,    __lowerCamelCase							:						Union[float, List[float]]						,    __lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None						,    **__lowerCamelCase							:						List[Any]						,    )				->					List[str]:
										return normalize(__lowerCamelCase						,    mean=__lowerCamelCase						,    std=__lowerCamelCase						,    data_format=__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						ImageInput						,    __lowerCamelCase							:						bool = None						,    __lowerCamelCase							:						Dict[str, int] = None						,    __lowerCamelCase							:						float = None						,    __lowerCamelCase							:						PILImageResampling = None						,    __lowerCamelCase							:						bool = None						,    __lowerCamelCase							:						float = None						,    __lowerCamelCase							:						bool = None						,    __lowerCamelCase							:						Optional[Union[float, List[float]]] = None						,    __lowerCamelCase							:						Optional[Union[float, List[float]]] = None						,    __lowerCamelCase							:						Optional[Union[str, TensorType]] = None						,    __lowerCamelCase							:						ChannelDimension = ChannelDimension.FIRST						,    **__lowerCamelCase							:						Optional[int]						,    )				->					int:
										__magic_name__            =     do_resize if do_resize is not None else self.do_resize
										__magic_name__            =     crop_pct if crop_pct is not None else self.crop_pct
										__magic_name__            =     resample if resample is not None else self.resample
										__magic_name__            =     do_rescale if do_rescale is not None else self.do_rescale
										__magic_name__            =     rescale_factor if rescale_factor is not None else self.rescale_factor
										__magic_name__            =     do_normalize if do_normalize is not None else self.do_normalize
										__magic_name__            =     image_mean if image_mean is not None else self.image_mean
										__magic_name__            =     image_std if image_std is not None else self.image_std

										__magic_name__            =     size if size is not None else self.size
										__magic_name__            =     get_size_dict(__lowerCamelCase						,    default_to_square=__lowerCamelCase	)

										__magic_name__            =     make_list_of_images(__lowerCamelCase	)

										if not valid_images(__lowerCamelCase	):
																raise ValueError(
																    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																    "torch.Tensor, tf.Tensor or jax.ndarray."	)

										if do_resize and size is None or resample is None:
																raise ValueError("Size and resample must be specified if do_resize is True."	)

										if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
																raise ValueError("crop_pct must be specified if size < 384."	)

										if do_rescale and rescale_factor is None:
																raise ValueError("Rescale factor must be specified if do_rescale is True."	)

										if do_normalize and (image_mean is None or image_std is None):
																raise ValueError("Image mean and std must be specified if do_normalize is True."	)

										# All transformations expect numpy arrays.
										__magic_name__            =     [to_numpy_array(__lowerCamelCase	) for image in images]

										if do_resize:
																__magic_name__            =     [self.resize(image=__lowerCamelCase						,    size=__lowerCamelCase						,    crop_pct=__lowerCamelCase						,    resample=__lowerCamelCase	) for image in images]

										if do_rescale:
																__magic_name__            =     [self.rescale(image=__lowerCamelCase						,    scale=__lowerCamelCase	) for image in images]

										if do_normalize:
																__magic_name__            =     [self.normalize(image=__lowerCamelCase						,    mean=__lowerCamelCase						,    std=__lowerCamelCase	) for image in images]

										__magic_name__            =     [to_channel_dimension_format(__lowerCamelCase						,    __lowerCamelCase	) for image in images]

										__magic_name__            =     {"pixel_values": images}
										return BatchFeature(data=__lowerCamelCase						,    tensor_type=__lowerCamelCase	)











"""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 _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     {
						    "attention_cell": "multi_head",
						    "num_layers": 4,
						    "units": 1_0_2_4,
						    "hidden_size": 7_6_8,
						    "max_length": 5_1_2,
						    "num_heads": 8,
						    "scaled": True,
						    "dropout": 0.1,
						    "use_residual": True,
						    "embed_size": 1_0_2_4,
						    "embed_dropout": 0.1,
						    "word_embed": None,
						    "layer_norm_eps": 1E-5,
						    "token_type_vocab_size": 2,
						}

						__magic_name__            =     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
						__magic_name__            =     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
						__magic_name__            =     "openwebtext_ccnews_stories_books_cased"

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

						__magic_name__            =     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   )
						__magic_name__            =     original_bort._collect_params_with_prefix()

						# Build our config 🤗
						__magic_name__            =     {
						    "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": 0.0_2,
						    "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   ),
						}

						__magic_name__            =     BertConfig.from_dict(__lowerCamelCase   )
						__magic_name__            =     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:Tuple   ) -> 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:str ,						__lowerCamelCase:Union[str, Any]   ):
												__magic_name__            =     hf_param.shape

												__magic_name__            =     to_torch(params[gluon_param]   )
												__magic_name__            =     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

						__magic_name__            =     check_and_map_params(
						    hf_bort_model.bert.embeddings.word_embeddings.weight ,						"word_embed.0.weight"   )
						__magic_name__            =     check_and_map_params(
						    hf_bort_model.bert.embeddings.position_embeddings.weight ,						"encoder.position_weight"   )
						__magic_name__            =     check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.bias ,						"encoder.layer_norm.beta"   )
						__magic_name__            =     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)
						__magic_name__            =     torch.zeros_like(
						    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data   )

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

												# self attention
												__magic_name__            =     layer.attention.self

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

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

												# self attention output
												__magic_name__            =     layer.attention.output

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

												# intermediate
												__magic_name__            =     layer.intermediate

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

												# output
												__magic_name__            =     layer.output

												__magic_name__            =     check_and_map_params(
												    bert_output.dense.bias ,						f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''   )
												__magic_name__            =     check_and_map_params(
												    bert_output.dense.weight ,						f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''   )
												__magic_name__            =     check_and_map_params(
												    bert_output.LayerNorm.bias ,						f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''   )
												__magic_name__            =     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
						__magic_name__            =     RobertaTokenizer.from_pretrained("roberta-base"   )

						__magic_name__            =     tokenizer.encode_plus(__lowerCamelCase   )["input_ids"]

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

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

						__magic_name__            =     tokenizer.encode_plus(__lowerCamelCase ,						return_tensors="pt"   )
						__magic_name__            =     hf_bort_model(**__lowerCamelCase   )[0]

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

						__magic_name__            =     np.max(np.abs(hf_layer - gluon_layer   )   ).item()
						__magic_name__            =     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__":
			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)



