
"""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()
UpperCamelCase			:       Optional[Any]									=				logging.get_logger(__name__)

UpperCamelCase			:       Union[str, Any]									=				"Hello, World!"
UpperCamelCase			:       List[str]									=				"en_XX"






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

 __UpperCamelCase       						= xmod.model.encoder.sentence_encoder
 __UpperCamelCase       						= 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_1_4       ,      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:
  __UpperCamelCase       						= xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

 print('Our X-MOD config:'       ,      snake_case	)

 __UpperCamelCase       						= XmodForSequenceClassification(snake_case	) if classification_head else XmodForMaskedLM(snake_case	)
 model.eval()

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

 __UpperCamelCase       						= xmod_sent_encoder.layernorm_embedding.weight
 __UpperCamelCase       						= xmod_sent_encoder.layernorm_embedding.bias

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

  # self attention
  __UpperCamelCase       						= 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.'	)

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

  # self-attention output
  __UpperCamelCase       						= 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.'	)
  __UpperCamelCase       						= xmod_layer.self_attn.out_proj.weight
  __UpperCamelCase       						= xmod_layer.self_attn.out_proj.bias
  __UpperCamelCase       						= xmod_layer.self_attn_layer_norm.weight
  __UpperCamelCase       						= xmod_layer.self_attn_layer_norm.bias

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

  # output
  __UpperCamelCase       						= layer.output
  if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
   raise AssertionError('Dimensions of feed-forward weights do not match.'	)
  __UpperCamelCase       						= xmod_layer.fca.weight
  __UpperCamelCase       						= xmod_layer.fca.bias
  __UpperCamelCase       						= xmod_layer.final_layer_norm.weight
  __UpperCamelCase       						= xmod_layer.final_layer_norm.bias
  if bert_output.adapter_layer_norm is not None:
   __UpperCamelCase       						= xmod_layer.adapter_layer_norm.weight
   __UpperCamelCase       						= 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():
   __UpperCamelCase       						= bert_output.adapter_modules[lang_code]
   __UpperCamelCase       						= xmod_layer.adapter_modules[lang_code]
   __UpperCamelCase       						= from_adapter.fca.weight
   __UpperCamelCase       						= from_adapter.fca.bias
   __UpperCamelCase       						= from_adapter.fca.weight
   __UpperCamelCase       						= from_adapter.fca.bias

  # end of layer

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

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

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

 __UpperCamelCase       						= model(snake_case	)[0]
 if classification_head:
  __UpperCamelCase       						= xmod.model.classification_heads['mnli'](xmod.extract_features(snake_case	)	)
 else:
  __UpperCamelCase       						= xmod.model(snake_case       ,      lang_id=[SAMPLE_LANGUAGE]	)[0]
 print(our_output.shape       ,      their_output.shape	)
 __UpperCamelCase       						= torch.max(torch.abs(our_output - their_output	)	).item()
 print(f'max_absolute_diff = {max_absolute_diff}'	)  # ~ 1e-7
 __UpperCamelCase       						= torch.allclose(snake_case       ,      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(snake_case	).mkdir(parents=snake_case       ,      exist_ok=snake_case	)
 print(f'Saving model to {pytorch_dump_folder_path}'	)
 model.save_pretrained(snake_case	)


if __name__ == "__main__":
      UpperCamelCase			:       Optional[int]									=				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."
      )
      UpperCamelCase			:       Tuple									=				parser.parse_args()
      convert_xmod_checkpoint_to_pytorch(
          args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )





"""simple docstring"""

import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BridgeTowerImageProcessor

class __lowerCAmelCase	(   unittest.TestCase    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase = True			,  __UpperCAmelCase = None			,  __UpperCAmelCase = 32			,  __UpperCAmelCase = True			,  __UpperCAmelCase = 1 / 255			,  __UpperCAmelCase = True			,  __UpperCAmelCase = True			,  __UpperCAmelCase = [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]			,  __UpperCAmelCase = [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]			,  __UpperCAmelCase = True			,  __UpperCAmelCase=7			,  __UpperCAmelCase=30			,  __UpperCAmelCase=400			,  __UpperCAmelCase=3			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= parent
      __UpperCamelCase       						= do_resize
      __UpperCamelCase       						= size if size is not None else {'shortest_edge': 288}
      __UpperCamelCase       						= size_divisor
      __UpperCamelCase       						= do_rescale
      __UpperCamelCase       						= rescale_factor
      __UpperCamelCase       						= do_normalize
      __UpperCamelCase       						= do_center_crop
      __UpperCamelCase       						= image_mean
      __UpperCamelCase       						= image_std
      __UpperCamelCase       						= do_pad
      __UpperCamelCase       						= batch_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= min_resolution
      __UpperCamelCase       						= max_resolution





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=False   ):

      '''simple docstring'''




      if not batched:
       __UpperCamelCase       						= self.size['shortest_edge']
       __UpperCamelCase       						= image_inputs[0]
       if isinstance(__UpperCAmelCase			,  Image.Image   ):
        __UpperCamelCase						,			__UpperCamelCase       						= image.size
       else:
        __UpperCamelCase						,			__UpperCamelCase       						= image.shape[1], image.shape[2]
       __UpperCamelCase       						= size / min(__UpperCAmelCase			,  __UpperCAmelCase   )
       if h < w:
        __UpperCamelCase						,			__UpperCamelCase       						= size, scale * w
       else:
        __UpperCamelCase						,			__UpperCamelCase       						= scale * h, size

       __UpperCamelCase       						= int((1333 / 800) * size   )
       if max(__UpperCAmelCase			,  __UpperCAmelCase   ) > max_size:
        __UpperCamelCase       						= max_size / max(__UpperCAmelCase			,  __UpperCAmelCase   )
        __UpperCamelCase       						= newh * scale
        __UpperCamelCase       						= neww * scale

       __UpperCamelCase						,			__UpperCamelCase       						= int(newh + 0.5   ), int(neww + 0.5   )
       __UpperCamelCase						,			__UpperCamelCase       						= (
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       __UpperCamelCase       						= []
       for image in image_inputs:
        __UpperCamelCase						,			__UpperCamelCase       						= self.get_expected_values([image]   )
        expected_values.append((expected_height, expected_width)   )
       __UpperCamelCase       						= max(__UpperCAmelCase			,  key=lambda __UpperCAmelCase   : item[0]   )[0]
       __UpperCamelCase       						= max(__UpperCAmelCase			,  key=lambda __UpperCAmelCase   : item[1]   )[1]

      return expected_height, expected_width


@require_torch
@require_vision
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		BridgeTowerImageProcessor if is_vision_available() else None





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= BridgeTowerImageProcessingTester(self   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'image_mean'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'image_std'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'do_normalize'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'do_resize'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'size'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'size_divisor'   )   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      __UpperCamelCase       						= prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__UpperCAmelCase   )
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase			,  Image.Image   )

      # Test not batched input
      __UpperCamelCase       						= image_processing(image_inputs[0]			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      __UpperCamelCase       						= prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__UpperCAmelCase			,  numpify=__UpperCAmelCase   )
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase			,  np.ndarray   )

      # Test not batched input
      __UpperCamelCase       						= image_processing(image_inputs[0]			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      __UpperCamelCase       						= prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__UpperCAmelCase			,  torchify=__UpperCAmelCase   )
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase			,  torch.Tensor   )

      # Test not batched input
      __UpperCamelCase       						= image_processing(image_inputs[0]			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )



