

'''simple docstring'''

from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
       import torch

if is_tf_available():
       import tensorflow as tf


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			["image_processor"]
       lowerCAmelCase_												=			"SamImageProcessor"







       def __init__(				self      :			Optional[int]    ,   __lowercase      :			Union[str, Any]   ):







            """simple docstring"""







            super().__init__(__lowercase   )
            __lowercase      		=self.image_processor
            __lowercase      		=-10
            __lowercase      		=self.image_processor.size['longest_edge']







       def __call__(				self      :			Dict    ,   __lowercase      :			List[str]=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			Any=None    ,   __lowercase      :			Optional[int]=None    ,   __lowercase      :			Optional[Union[str, TensorType]] = None    ,   **__lowercase      :			List[Any]    ,   ):







            """simple docstring"""







            __lowercase      		=self.image_processor(
                __lowercase    ,   return_tensors=__lowercase    ,   **__lowercase    ,   )

            # pop arguments that are not used in the foward but used nevertheless
            __lowercase      		=encoding_image_processor['original_sizes']

            if hasattr(__lowercase    ,   'numpy'   ):  # Checks if Torch or TF tensor
                 __lowercase      		=original_sizes.numpy()

            __lowercase		,  __lowercase		,  __lowercase      		=self._check_and_preprocess_points(
                input_points=__lowercase    ,   input_labels=__lowercase    ,   input_boxes=__lowercase    ,   )

            __lowercase      		=self._normalize_and_convert(
                __lowercase    ,   __lowercase    ,   input_points=__lowercase    ,   input_labels=__lowercase    ,   input_boxes=__lowercase    ,   return_tensors=__lowercase    ,   )

            return encoding_image_processor







       def       snake_case (				self      :			Dict    ,   __lowercase      :			Any    ,   __lowercase      :			str    ,   __lowercase      :			List[Any]=None    ,   __lowercase      :			int=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			List[Any]="pt"    ,   ):







            """simple docstring"""







            if input_points is not None:
                 if len(__lowercase   ) != len(__lowercase   ):
                      __lowercase      		=[
                          self._normalize_coordinates(self.target_size    ,   __lowercase    ,   original_sizes[0]   ) for point in input_points
                      ]
                 else:
                      __lowercase      		=[
                          self._normalize_coordinates(self.target_size    ,   __lowercase    ,   __lowercase   )
                          for point, original_size in zip(__lowercase    ,   __lowercase   )
                      ]
                 # check that all arrays have the same shape
                 if not all(point.shape == input_points[0].shape for point in input_points   ):
                      if input_labels is not None:
                           __lowercase		,  __lowercase      		=self._pad_points_and_labels(__lowercase    ,   __lowercase   )

                 __lowercase      		=np.array(__lowercase   )

            if input_labels is not None:
                 __lowercase      		=np.array(__lowercase   )

            if input_boxes is not None:
                 if len(__lowercase   ) != len(__lowercase   ):
                      __lowercase      		=[
                          self._normalize_coordinates(self.target_size    ,   __lowercase    ,   original_sizes[0]    ,   is_bounding_box=__lowercase   )
                          for box in input_boxes
                      ]
                 else:
                      __lowercase      		=[
                          self._normalize_coordinates(self.target_size    ,   __lowercase    ,   __lowercase    ,   is_bounding_box=__lowercase   )
                          for box, original_size in zip(__lowercase    ,   __lowercase   )
                      ]
                 __lowercase      		=np.array(__lowercase   )

            if input_boxes is not None:
                 if return_tensors == "pt":
                      __lowercase      		=torch.from_numpy(__lowercase   )
                      # boxes batch size of 1 by default
                      __lowercase      		=input_boxes.unsqueeze(1   ) if len(input_boxes.shape   ) != 3 else input_boxes
                 elif return_tensors == "tf":
                      __lowercase      		=tf.convert_to_tensor(__lowercase   )
                      # boxes batch size of 1 by default
                      __lowercase      		=tf.expand_dims(__lowercase    ,   1   ) if len(input_boxes.shape   ) != 3 else input_boxes
                 encoding_image_processor.update({'input_boxes': input_boxes}   )
            if input_points is not None:
                 if return_tensors == "pt":
                      __lowercase      		=torch.from_numpy(__lowercase   )
                      # point batch size of 1 by default
                      __lowercase      		=input_points.unsqueeze(1   ) if len(input_points.shape   ) != 4 else input_points
                 elif return_tensors == "tf":
                      __lowercase      		=tf.convert_to_tensor(__lowercase   )
                      # point batch size of 1 by default
                      __lowercase      		=tf.expand_dims(__lowercase    ,   1   ) if len(input_points.shape   ) != 4 else input_points
                 encoding_image_processor.update({'input_points': input_points}   )
            if input_labels is not None:
                 if return_tensors == "pt":
                      __lowercase      		=torch.from_numpy(__lowercase   )
                      # point batch size of 1 by default
                      __lowercase      		=input_labels.unsqueeze(1   ) if len(input_labels.shape   ) != 3 else input_labels
                 elif return_tensors == "tf":
                      __lowercase      		=tf.convert_to_tensor(__lowercase   )
                      # point batch size of 1 by default
                      __lowercase      		=tf.expand_dims(__lowercase    ,   1   ) if len(input_labels.shape   ) != 3 else input_labels
                 encoding_image_processor.update({'input_labels': input_labels}   )

            return encoding_image_processor







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			Tuple   ):







            """simple docstring"""







            __lowercase      		=max([point.shape[0] for point in input_points]   )
            __lowercase      		=[]
            for i, point in enumerate(__lowercase   ):
                 if point.shape[0] != expected_nb_points:
                      __lowercase      		=np.concatenate(
                          [point, np.zeros((expected_nb_points - point.shape[0], 2)   ) + self.point_pad_value]    ,   axis=0   )
                      __lowercase      		=np.append(input_labels[i]    ,   [self.point_pad_value]   )
                 processed_input_points.append(__lowercase   )
            __lowercase      		=processed_input_points
            return input_points, input_labels







       def       snake_case (				self      :			Dict    ,   __lowercase      :			int    ,   __lowercase      :			np.ndarray    ,   __lowercase      :			int    ,   __lowercase      :			str=False   ):







            """simple docstring"""







            __lowercase		,  __lowercase      		=original_size
            __lowercase		,  __lowercase      		=self.image_processor._get_preprocess_shape(__lowercase    ,   longest_edge=__lowercase   )
            __lowercase      		=deepcopy(__lowercase   ).astype(__lowercase   )

            if is_bounding_box:
                 __lowercase      		=coords.reshape(-1    ,   2    ,   2   )

            __lowercase      		=coords[..., 0] * (new_w / old_w)
            __lowercase      		=coords[..., 1] * (new_h / old_h)

            if is_bounding_box:
                 __lowercase      		=coords.reshape(-1    ,   4   )

            return coords







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Any=None    ,   __lowercase      :			Tuple=None    ,   __lowercase      :			str=None    ,   ):







            """simple docstring"""







            if input_points is not None:
                 if hasattr(__lowercase    ,   'numpy'   ):  # Checks for TF or Torch tensor
                      __lowercase      		=input_points.numpy().tolist()

                 if not isinstance(__lowercase    ,   __lowercase   ) or not isinstance(input_points[0]    ,   __lowercase   ):
                      raise ValueError('Input points must be a list of list of floating points.'   )
                 __lowercase      		=[np.array(__lowercase   ) for input_point in input_points]
            else:
                 __lowercase      		=None

            if input_labels is not None:
                 if hasattr(__lowercase    ,   'numpy'   ):
                      __lowercase      		=input_labels.numpy().tolist()

                 if not isinstance(__lowercase    ,   __lowercase   ) or not isinstance(input_labels[0]    ,   __lowercase   ):
                      raise ValueError('Input labels must be a list of list integers.'   )
                 __lowercase      		=[np.array(__lowercase   ) for label in input_labels]
            else:
                 __lowercase      		=None

            if input_boxes is not None:
                 if hasattr(__lowercase    ,   'numpy'   ):
                      __lowercase      		=input_boxes.numpy().tolist()

                 if (
                     not isinstance(__lowercase    ,   __lowercase   )
                     or not isinstance(input_boxes[0]    ,   __lowercase   )
                     or not isinstance(input_boxes[0][0]    ,   __lowercase   )
                 ):
                      raise ValueError('Input boxes must be a list of list of list of floating points.'   )
                 __lowercase      		=[np.array(__lowercase   ).astype(np.floataa   ) for box in input_boxes]
            else:
                 __lowercase      		=None

            return input_points, input_labels, input_boxes







       @property
       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            __lowercase      		=self.image_processor.model_input_names
            return list(dict.fromkeys(__lowercase   )   )







       def       snake_case (				self      :			Tuple    ,   *__lowercase      :			Optional[int]    ,   **__lowercase      :			Dict   ):







            """simple docstring"""







            return self.image_processor.post_process_masks(*__lowercase    ,   **__lowercase   )





'''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 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		={
         'attention_cell': 'multi_head',
         'num_layers': 4,
         'units': 10_24,
         'hidden_size': 7_68,
         'max_length': 5_12,
         'num_heads': 8,
         'scaled': True,
         'dropout': 0.1,
         'use_residual': True,
         'embed_size': 10_24,
         'embed_dropout': 0.1,
         'word_embed': None,
         'layer_norm_eps': 1E-5,
         'token_type_vocab_size': 2,
     }

     __lowercase      		=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
     __lowercase      		=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=lowercase__,       output_all_encodings=lowercase__,       use_residual=predefined_args['use_residual'],       activation=predefined_args.get('activation',       'gelu'			),       layer_norm_eps=predefined_args.get('layer_norm_eps',       lowercase__			),       )

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

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

     __lowercase      		=nlp.model.BERTModel(
         lowercase__,       len(lowercase__			),       units=predefined_args['units'],       embed_size=predefined_args['embed_size'],       embed_dropout=predefined_args['embed_dropout'],       word_embed=predefined_args['word_embed'],       use_pooler=lowercase__,       use_token_type_embed=lowercase__,       token_type_vocab_size=predefined_args['token_type_vocab_size'],       use_classifier=lowercase__,       use_decoder=lowercase__,       )

     original_bort.load_parameters(lowercase__,       cast_dtype=lowercase__,       ignore_extra=lowercase__			)
     __lowercase      		=original_bort._collect_params_with_prefix()

     # Build our config 🤗
     __lowercase      		={
         '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(lowercase__			),
     }

     __lowercase      		=BertConfig.from_dict(lowercase__			)
     __lowercase      		=BertForMaskedLM(lowercase__			)
     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(lowercase__    :	Optional[Any]			) -> 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(lowercase__    :	Optional[Any],       lowercase__    :	Any			):
          __lowercase      		=hf_param.shape

          __lowercase      		=to_torch(params[gluon_param]			)
          __lowercase      		=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

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

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

          # self attention
          __lowercase      		=layer.attention.self

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

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

          # self attention output
          __lowercase      		=layer.attention.output

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

          # intermediate
          __lowercase      		=layer.intermediate

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

          # output
          __lowercase      		=layer.output

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

     __lowercase      		=tokenizer.encode_plus(lowercase__			)['input_ids']

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

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

     __lowercase      		=tokenizer.encode_plus(lowercase__,       return_tensors='pt'			)
     __lowercase      		=hf_bort_model(**lowercase__			)[0]

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

     __lowercase      		=np.max(np.abs(hf_layer - gluon_layer			)			).item()
     __lowercase      		=np.allclose(lowercase__,       lowercase__,       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:',       lowercase__			)


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)


