"""simple docstring"""




from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
     import PIL

# soft dependency
if is_pytesseract_available():
     import pytesseract

_a      :		int           =   logging.get_logger(__name__)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any			,_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       Any	)       ->      Union[str, Any]:
  return [
      int(1000 * (box[0] / width)	),
      int(1000 * (box[1] / height)	),
      int(1000 * (box[2] / width)	),
      int(1000 * (box[3] / height)	),
  ]





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       np.ndarray			,_lowerCamelCase						:       Optional[str]			,_lowerCamelCase						:       Optional[str] = None	)       ->      Optional[Any]:
  _lowerCAmelCase						:       Dict  			=     tesseract_config if tesseract_config is not None else """"""

  # apply OCR
  _lowerCAmelCase						:       Optional[Any]  			=     to_pil_image(_lowerCamelCase	)
  _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     pil_image.size
  _lowerCAmelCase						:       Dict  			=     pytesseract.image_to_data(_lowerCamelCase			,lang=_lowerCamelCase			,output_type="""dict"""			,config=_lowerCamelCase	)
  _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

  # filter empty words and corresponding coordinates
  _lowerCAmelCase						:       List[str]  			=     [idx for idx, word in enumerate(_lowerCamelCase	) if not word.strip()]
  _lowerCAmelCase						:       List[Any]  			=     [word for idx, word in enumerate(_lowerCamelCase	) if idx not in irrelevant_indices]
  _lowerCAmelCase						:       Tuple  			=     [coord for idx, coord in enumerate(_lowerCamelCase	) if idx not in irrelevant_indices]
  _lowerCAmelCase						:       Dict  			=     [coord for idx, coord in enumerate(_lowerCamelCase	) if idx not in irrelevant_indices]
  _lowerCAmelCase						:       Dict  			=     [coord for idx, coord in enumerate(_lowerCamelCase	) if idx not in irrelevant_indices]
  _lowerCAmelCase						:       Optional[Any]  			=     [coord for idx, coord in enumerate(_lowerCamelCase	) if idx not in irrelevant_indices]

  # turn coordinates into (left, top, left+width, top+height) format
  _lowerCAmelCase						:       List[str]  			=     []
  for x, y, w, h in zip(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	):
    _lowerCAmelCase						:       Optional[Any]  			=     [x, y, x + w, y + h]
    actual_boxes.append(_lowerCamelCase	)

  # finally, normalize the bounding boxes
  _lowerCAmelCase						:       int  			=     []
  for box in actual_boxes:
    normalized_boxes.append(normalize_box(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)	)

  assert len(_lowerCamelCase	) == len(_lowerCamelCase	), "Not as many words as there are bounding boxes"

  return words, normalized_boxes






class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[int]							=		["pixel_values"]






     def __init__(		self				,     a__ = True				,     a__ = None				,     a__ = PILImageResampling.BILINEAR				,     a__ = True				,     a__ = None				,     a__ = ""				,     **a__				,     ):
       super().__init__(**a__  )
       _lowerCAmelCase						:       Tuple  			=     size if size is not None else {"""height""": 224, """width""": 224}
       _lowerCAmelCase						:       Dict  			=     get_size_dict(a__  )

       _lowerCAmelCase						:       int  			=     do_resize
       _lowerCAmelCase						:       Optional[int]  			=     size
       _lowerCAmelCase						:       Optional[Any]  			=     resample
       _lowerCAmelCase						:       List[Any]  			=     apply_ocr
       _lowerCAmelCase						:       Dict  			=     ocr_lang
       _lowerCAmelCase						:       Tuple  			=     tesseract_config






     def     __A  (		self				,     a__				,     a__				,     a__ = PILImageResampling.BILINEAR				,     a__ = None				,     **a__				,     ):
       _lowerCAmelCase						:       Dict  			=     get_size_dict(a__  )
       if "height" not in size or "width" not in size:
         raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"  )
       _lowerCAmelCase						:       Optional[int]  			=     (size["""height"""], size["""width"""])
       return resize(a__				,     size=a__				,     resample=a__				,     data_format=a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = ChannelDimension.FIRST				,     **a__				,     ):
       _lowerCAmelCase						:       str  			=     do_resize if do_resize is not None else self.do_resize
       _lowerCAmelCase						:       str  			=     size if size is not None else self.size
       _lowerCAmelCase						:       Optional[Any]  			=     get_size_dict(a__  )
       _lowerCAmelCase						:       List[Any]  			=     resample if resample is not None else self.resample
       _lowerCAmelCase						:       Any  			=     apply_ocr if apply_ocr is not None else self.apply_ocr
       _lowerCAmelCase						:       Dict  			=     ocr_lang if ocr_lang is not None else self.ocr_lang
       _lowerCAmelCase						:       int  			=     tesseract_config if tesseract_config is not None else self.tesseract_config

       _lowerCAmelCase						:       str  			=     make_list_of_images(a__  )

       if not valid_images(a__  ):
         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:
         raise ValueError("""Size must be specified if do_resize is True."""  )

       # All transformations expect numpy arrays.
       _lowerCAmelCase						:       Optional[int]  			=     [to_numpy_array(a__  ) for image in images]

       if apply_ocr:
         requires_backends(self				,     """pytesseract"""  )
         _lowerCAmelCase						:       List[str]  			=     []
         _lowerCAmelCase						:       Optional[Any]  			=     []
         for image in images:
           _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     apply_tesseract(a__				,     a__				,     a__  )
           words_batch.append(a__  )
           boxes_batch.append(a__  )

       if do_resize:
         _lowerCAmelCase						:       List[Any]  			=     [self.resize(image=a__				,     size=a__				,     resample=a__  ) for image in images]

       # flip color channels from RGB to BGR (as Detectron2 requires this)
       _lowerCAmelCase						:       Union[str, Any]  			=     [flip_channel_order(a__  ) for image in images]
       _lowerCAmelCase						:       Tuple  			=     [to_channel_dimension_format(a__				,     a__  ) for image in images]

       _lowerCAmelCase						:       Any  			=     BatchFeature(data={"""pixel_values""": images}				,     tensor_type=a__  )

       if apply_ocr:
         _lowerCAmelCase						:       Optional[Any]  			=     words_batch
         _lowerCAmelCase						:       Optional[int]  			=     boxes_batch
       return data








import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
     raise Exception("""requires fairseq >= 1.0.0a""")

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

UpperCamelCase								=	"""Hello world! cécé herlolip"""


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[Any]		     =					FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE       )
  roberta.eval()  # disable dropout
  A_      :				Dict		     =					roberta.model.encoder.sentence_encoder
  A_      :				Optional[Any]		     =					XLMRobertaConfig(
      vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings ,			hidden_size=roberta.cfg.model.encoder_embed_dim ,			num_hidden_layers=roberta.cfg.model.encoder_layers ,			num_attention_heads=roberta.cfg.model.encoder_attention_heads ,			intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim ,			max_position_embeddings=514 ,			type_vocab_size=1 ,			layer_norm_eps=1e-5 ,			)
  if classification_head:
    A_      :				Optional[int]		     =					roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

  print('''Our RoBERTa config:''' ,			SCREAMING_SNAKE_CASE       )

  A_      :				List[str]		     =					XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE       ) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE       )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  A_      :				str		     =					roberta_sent_encoder.embed_tokens.weight
  A_      :				int		     =					roberta_sent_encoder.embed_positions.weight
  A_      :				Union[str, Any]		     =					torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight       )  # just zero them out b/c RoBERTa doesn't use them.

  A_      :				Union[str, Any]		     =					roberta_sent_encoder.layer_norm.weight
  A_      :				int		     =					roberta_sent_encoder.layer_norm.bias

  for i in range(config.num_hidden_layers       ):
    # Encoder: start of layer
    A_      :				BertLayer		     =					model.roberta.encoder.layer[i]
    A_      :				TransformerSentenceEncoderLayer		     =					roberta_sent_encoder.layers[i]

    A_      :				RobertaAttention		     =					layer.attention
    A_      :				Dict		     =					roberta_layer.self_attn_layer_norm.weight
    A_      :				str		     =					roberta_layer.self_attn_layer_norm.bias

    # self attention
    A_      :				BertSelfAttention		     =					layer.attention.self
    assert (
        roberta_layer.self_attn.k_proj.weight.data.shape
        == roberta_layer.self_attn.q_proj.weight.data.shape
        == roberta_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)       )
    )

    A_      :				str		     =					roberta_layer.self_attn.q_proj.weight
    A_      :				List[str]		     =					roberta_layer.self_attn.q_proj.bias
    A_      :				int		     =					roberta_layer.self_attn.k_proj.weight
    A_      :				List[Any]		     =					roberta_layer.self_attn.k_proj.bias
    A_      :				Dict		     =					roberta_layer.self_attn.v_proj.weight
    A_      :				int		     =					roberta_layer.self_attn.v_proj.bias

    # self-attention output
    A_      :				BertSelfOutput		     =					layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    A_      :				Any		     =					roberta_layer.self_attn.out_proj.weight
    A_      :				Optional[Any]		     =					roberta_layer.self_attn.out_proj.bias

    # this one is final layer norm
    A_      :				Any		     =					roberta_layer.final_layer_norm.weight
    A_      :				int		     =					roberta_layer.final_layer_norm.bias

    # intermediate
    A_      :				BertIntermediate		     =					layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    A_      :				int		     =					roberta_layer.fca.weight
    A_      :				List[str]		     =					roberta_layer.fca.bias

    # output
    A_      :				BertOutput		     =					layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    A_      :				Optional[int]		     =					roberta_layer.fca.weight
    A_      :				List[Any]		     =					roberta_layer.fca.bias
    # end of layer

  if classification_head:
    A_      :				str		     =					roberta.model.classification_heads['''mnli'''].dense.weight
    A_      :				int		     =					roberta.model.classification_heads['''mnli'''].dense.bias
    A_      :				str		     =					roberta.model.classification_heads['''mnli'''].out_proj.weight
    A_      :				Dict		     =					roberta.model.classification_heads['''mnli'''].out_proj.bias
  else:
    # LM Head
    A_      :				int		     =					roberta.model.encoder.lm_head.dense.weight
    A_      :				List[str]		     =					roberta.model.encoder.lm_head.dense.bias
    A_      :				Optional[Any]		     =					roberta.model.encoder.lm_head.layer_norm.weight
    A_      :				int		     =					roberta.model.encoder.lm_head.layer_norm.bias
    A_      :				Optional[int]		     =					roberta.model.encoder.lm_head.weight
    A_      :				Dict		     =					roberta.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  A_      :				torch.Tensor		     =					roberta.encode(SCREAMING_SNAKE_CASE       ).unsqueeze(0       )  # batch of size 1

  A_      :				Union[str, Any]		     =					model(SCREAMING_SNAKE_CASE       )[0]
  if classification_head:
    A_      :				str		     =					roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE       )       )
  else:
    A_      :				int		     =					roberta.model(SCREAMING_SNAKE_CASE       )[0]
  print(our_output.shape ,			their_output.shape       )
  A_      :				Any		     =					torch.max(torch.abs(our_output - their_output       )       ).item()
  print(f'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
  A_      :				Tuple		     =					torch.allclose(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			atol=1e-3       )
  print('''Do both models output the same tensors?''' ,			'''🔥''' if success else '''💩'''       )
  if not success:
    raise Exception('''Something went wRoNg'''       )

  pathlib.Path(SCREAMING_SNAKE_CASE       ).mkdir(parents=SCREAMING_SNAKE_CASE ,			exist_ok=SCREAMING_SNAKE_CASE       )
  print(f'''Saving model to {pytorch_dump_folder_path}'''       )
  model.save_pretrained(SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     UpperCamelCase								=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
     )
     UpperCamelCase								=	parser.parse_args()
     convert_xlm_roberta_xl_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )




