'''simple docstring'''


import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
       raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
       raise Exception("""requires fairseq < v2""")

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

__snake_case						  =		"""Hello, World!"""
__snake_case						  =		"""en_XX"""

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[Any]:
  lowercase_     =       Path("""data_bin"""						)
  lowercase_     =       FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(SCREAMING_SNAKE_CASE_						).parent						) ,	checkpoint_file=Path(SCREAMING_SNAKE_CASE_						).name ,	_name="""xmod_base""" ,	arch="""xmod_base""" ,	task="""multilingual_masked_lm""" ,	data_name_or_path=str(SCREAMING_SNAKE_CASE_						) ,	bpe="""sentencepiece""" ,	sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE_						).parent / """sentencepiece.bpe.model"""						) ,	src_dict=str(data_dir / """dict.txt"""						) ,	)
  xmod.eval()  # disable dropout
  print(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       xmod.model.encoder.sentence_encoder
  lowercase_     =       XmodConfig(
      vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,	hidden_size=xmod.cfg.model.encoder_embed_dim ,	num_hidden_layers=xmod.cfg.model.encoder_layers ,	num_attention_heads=xmod.cfg.model.encoder_attention_heads ,	intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,	max_position_embeddings=5_14 ,	type_vocab_size=1 ,	layer_norm_eps=1e-5 ,	pre_norm=xmod.cfg.model.encoder_normalize_before ,	adapter_reduction_factor=getattr(xmod.cfg.model ,	"""bottleneck""" ,	2						) ,	adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,	adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,	ln_before_adapter=xmod.cfg.model.ln_before_adapter ,	languages=xmod.cfg.model.languages ,	)
  if classification_head:
    lowercase_     =       xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

  print("""Our X-MOD config:""" ,	SCREAMING_SNAKE_CASE_						)

  lowercase_     =       XmodForSequenceClassification(SCREAMING_SNAKE_CASE_						) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE_						)
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  lowercase_     =       xmod_sent_encoder.embed_tokens.weight
  lowercase_     =       xmod_sent_encoder.embed_positions.weight
  lowercase_     =       torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c xmod doesn't use them.

  lowercase_     =       xmod_sent_encoder.layernorm_embedding.weight
  lowercase_     =       xmod_sent_encoder.layernorm_embedding.bias

  for i in range(config.num_hidden_layers						):
    # Encoder: start of layer
    lowercase_     =       model.roberta.encoder.layer[i]
    lowercase_     =       xmod_sent_encoder.layers[i]

    # self attention
    lowercase_     =       layer.attention.self
    if not (
        xmod_layer.self_attn.k_proj.weight.data.shape
        == xmod_layer.self_attn.q_proj.weight.data.shape
        == xmod_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)						)
    ):
      raise AssertionError("""Dimensions of self-attention weights do not match."""						)

    lowercase_     =       xmod_layer.self_attn.q_proj.weight
    lowercase_     =       xmod_layer.self_attn.q_proj.bias
    lowercase_     =       xmod_layer.self_attn.k_proj.weight
    lowercase_     =       xmod_layer.self_attn.k_proj.bias
    lowercase_     =       xmod_layer.self_attn.v_proj.weight
    lowercase_     =       xmod_layer.self_attn.v_proj.bias

    # self-attention output
    lowercase_     =       layer.attention.output
    if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
      raise AssertionError("""Dimensions of self-attention output weights do not match."""						)
    lowercase_     =       xmod_layer.self_attn.out_proj.weight
    lowercase_     =       xmod_layer.self_attn.out_proj.bias
    lowercase_     =       xmod_layer.self_attn_layer_norm.weight
    lowercase_     =       xmod_layer.self_attn_layer_norm.bias

    # intermediate
    lowercase_     =       layer.intermediate
    if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of intermediate weights do not match."""						)
    lowercase_     =       xmod_layer.fca.weight
    lowercase_     =       xmod_layer.fca.bias

    # output
    lowercase_     =       layer.output
    if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of feed-forward weights do not match."""						)
    lowercase_     =       xmod_layer.fca.weight
    lowercase_     =       xmod_layer.fca.bias
    lowercase_     =       xmod_layer.final_layer_norm.weight
    lowercase_     =       xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      lowercase_     =       xmod_layer.adapter_layer_norm.weight
      lowercase_     =       xmod_layer.adapter_layer_norm.bias

    if sorted(bert_output.adapter_modules.keys()						) != sorted(xmod_layer.adapter_modules.keys()						):
      raise AssertionError("""Lists of language adapters do not match."""						)
    for lang_code, adapter in xmod_layer.adapter_modules.items():
      lowercase_     =       bert_output.adapter_modules[lang_code]
      lowercase_     =       xmod_layer.adapter_modules[lang_code]
      lowercase_     =       from_adapter.fca.weight
      lowercase_     =       from_adapter.fca.bias
      lowercase_     =       from_adapter.fca.weight
      lowercase_     =       from_adapter.fca.bias

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    lowercase_     =       xmod_sent_encoder.layer_norm.weight
    lowercase_     =       xmod_sent_encoder.layer_norm.bias

  if classification_head:
    lowercase_     =       xmod.model.classification_heads["""mnli"""].dense.weight
    lowercase_     =       xmod.model.classification_heads["""mnli"""].dense.bias
    lowercase_     =       xmod.model.classification_heads["""mnli"""].out_proj.weight
    lowercase_     =       xmod.model.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    lowercase_     =       xmod.model.encoder.lm_head.dense.weight
    lowercase_     =       xmod.model.encoder.lm_head.dense.bias
    lowercase_     =       xmod.model.encoder.lm_head.layer_norm.weight
    lowercase_     =       xmod.model.encoder.lm_head.layer_norm.bias
    lowercase_     =       xmod.model.encoder.lm_head.weight
    lowercase_     =       xmod.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  lowercase_     =       xmod.encode(SCREAMING_SNAKE_CASE_						).unsqueeze(0						)  # batch of size 1
  model.roberta.set_default_language(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       model(SCREAMING_SNAKE_CASE_						)[0]
  if classification_head:
    lowercase_     =       xmod.model.classification_heads["""mnli"""](xmod.extract_features(SCREAMING_SNAKE_CASE_						)						)
  else:
    lowercase_     =       xmod.model(SCREAMING_SNAKE_CASE_ ,	lang_id=[SAMPLE_LANGUAGE]						)[0]
  print(our_output.shape ,	their_output.shape						)
  lowercase_     =       torch.max(torch.abs(our_output - their_output						)						).item()
  print(f"""max_absolute_diff = {max_absolute_diff}"""						)  # ~ 1e-7
  lowercase_     =       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"""						)

  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__":
       __snake_case						  =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--xmod_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."""
       )
       __snake_case						  =		parser.parse_args()
       convert_xmod_checkpoint_to_pytorch(
           args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )


'''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

__snake_case						  =		logging.get_logger(__name__)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  return [
      int(10_00 * (box[0] / width)						),
      int(10_00 * (box[1] / height)						),
      int(10_00 * (box[2] / width)						),
      int(10_00 * (box[3] / height)						),
  ]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = None						)			->List[Any]:
  lowercase_     =       tesseract_config if tesseract_config is not None else """"""

  # apply OCR
  lowercase_     =       to_pil_image(SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       pil_image.size
  lowercase_     =       pytesseract.image_to_data(SCREAMING_SNAKE_CASE_ ,	lang=SCREAMING_SNAKE_CASE_ ,	output_type="""dict""" ,	config=SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

  # filter empty words and corresponding coordinates
  lowercase_     =       [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE_						) if not word.strip()]
  lowercase_     =       [word for idx, word in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]

  # turn coordinates into (left, top, left+width, top+height) format
  lowercase_     =       []
  for x, y, w, h in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       [x, y, x + w, y + h]
    actual_boxes.append(SCREAMING_SNAKE_CASE_						)

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

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

  return words, normalized_boxes





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						['''pixel_values''']




  def __init__(		self      :       Tuple   ,				lowercase_      :       bool = True   ,				lowercase_      :       Dict[str, int] = None   ,				lowercase_      :       PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase_      :       bool = True   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[str] = ""   ,				**lowercase_      :       Optional[int]   ,				):







    '''simple docstring'''


    super().__init__(**lowercase_      )
    lowercase_     =       size if size is not None else {"""height""": 224, """width""": 224}
    lowercase_     =       get_size_dict(lowercase_      )

    lowercase_     =       do_resize
    lowercase_     =       size
    lowercase_     =       resample
    lowercase_     =       apply_ocr
    lowercase_     =       ocr_lang
    lowercase_     =       tesseract_config




  def 		lowerCamelCase__  (		self      :       Optional[int]   ,				lowercase_      :       np.ndarray   ,				lowercase_      :       Dict[str, int]   ,				lowercase_      :       PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase_      :       Optional[Union[str, ChannelDimension]] = None   ,				**lowercase_      :       int   ,				):







    '''simple docstring'''


    lowercase_     =       get_size_dict(lowercase_      )
    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()}"""      )
    lowercase_     =       (size["""height"""], size["""width"""])
    return resize(lowercase_   ,				size=lowercase_   ,				resample=lowercase_   ,				data_format=lowercase_   ,				**lowercase_      )





  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       ImageInput   ,				lowercase_      :       bool = None   ,				lowercase_      :       Dict[str, int] = None   ,				lowercase_      :       PILImageResampling = None   ,				lowercase_      :       bool = None   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[Union[str, TensorType]] = None   ,				lowercase_      :       ChannelDimension = ChannelDimension.FIRST   ,				**lowercase_      :       Dict   ,				):







    '''simple docstring'''


    lowercase_     =       do_resize if do_resize is not None else self.do_resize
    lowercase_     =       size if size is not None else self.size
    lowercase_     =       get_size_dict(lowercase_      )
    lowercase_     =       resample if resample is not None else self.resample
    lowercase_     =       apply_ocr if apply_ocr is not None else self.apply_ocr
    lowercase_     =       ocr_lang if ocr_lang is not None else self.ocr_lang
    lowercase_     =       tesseract_config if tesseract_config is not None else self.tesseract_config

    lowercase_     =       make_list_of_images(lowercase_      )

    if not valid_images(lowercase_      ):
      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.
    lowercase_     =       [to_numpy_array(lowercase_      ) for image in images]

    if apply_ocr:
      requires_backends(self   ,				"""pytesseract"""      )
      lowercase_     =       []
      lowercase_     =       []
      for image in images:
        lowercase_     ,	lowercase_     =       apply_tesseract(lowercase_   ,				lowercase_   ,				lowercase_      )
        words_batch.append(lowercase_      )
        boxes_batch.append(lowercase_      )

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

    # flip color channels from RGB to BGR (as Detectron2 requires this)
    lowercase_     =       [flip_channel_order(lowercase_      ) for image in images]
    lowercase_     =       [to_channel_dimension_format(lowercase_   ,				lowercase_      ) for image in images]

    lowercase_     =       BatchFeature(data={"""pixel_values""": images}   ,				tensor_type=lowercase_      )

    if apply_ocr:
      lowercase_     =       words_batch
      lowercase_     =       boxes_batch
    return data

