





import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE     ,		unittest.TestCase ):
  __lowerCAmelCase   :						List[Any]							=   CLIPTokenizer
  __lowerCAmelCase   :						Optional[int]							=   CLIPTokenizerFast
  __lowerCAmelCase   :						Union[str, Any]							=   True
  __lowerCAmelCase   :						Any							=   {}
  __lowerCAmelCase   :						int							=   False


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       super().setUp()

       # fmt: off
       A_          =       ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
       # fmt: on
       A_          =       dict(zip(__snake_case ,range(len(__snake_case ) ) ) )
       A_          =       ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
       A_          =       {'''unk_token''': '''<unk>'''}

       A_          =       os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['''vocab_file'''] )
       A_          =       os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['''merges_file'''] )
       with open(self.vocab_file ,'''w''' ,encoding='''utf-8''' ) as fp:
            fp.write(json.dumps(__snake_case ) + '''\n''' )
       with open(self.merges_file ,'''w''' ,encoding='''utf-8''' ) as fp:
            fp.write('''\n'''.join(__snake_case ) )


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       kwargs.update(self.special_tokens_map )
       return CLIPTokenizer.from_pretrained(self.tmpdirname ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       kwargs.update(self.special_tokens_map )
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       A_          =       '''lower newer'''
       A_          =       '''lower newer'''
       return input_text, output_text


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       CLIPTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
       A_          =       '''lower newer'''
       A_          =       ['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
       A_          =       tokenizer.tokenize(__snake_case )
       self.assertListEqual(__snake_case ,__snake_case )

       A_          =       tokens + [tokenizer.unk_token]
       A_          =       [1_0, 2, 1_6, 9, 3, 2, 1_6, 2_0]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) ,__snake_case )


  @require_ftfy
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
                 A_          =       self.tokenizer_class.from_pretrained(__snake_case ,**__snake_case )
                 A_          =       self.rust_tokenizer_class.from_pretrained(__snake_case ,**__snake_case )

                 A_          =       '''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
                 A_          =       tokenizer_s.tokenize(__snake_case )
                 A_          =       tokenizer_r.tokenize(__snake_case )

                 self.assertListEqual(__snake_case ,__snake_case )

                 # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                 # with Tilde) encoded in 2 different ways
                 A_          =       '''xa\u0303y''' + ''' ''' + '''x\xe3y'''
                 A_          =       tokenizer_s.tokenize(__snake_case )
                 A_          =       tokenizer_r.tokenize(__snake_case )

                 self.assertListEqual(__snake_case ,__snake_case )

                 # Test that the tokenization is identical on unicode of space type
                 A_          =       [
                     '''\u0009''',  # (horizontal tab, '\t')
                     '''\u000B''',  # (vertical tab)
                     '''\u000C''',  # (form feed)
                     '''\u0020''',  # (space, ' ')
                     '''\u200E''',  # (left-to-right mark):w
                     '''\u200F''',  # (right-to-left mark)
                 ]
                 for unicode_seq in spaces_unicodes:
                      A_          =       tokenizer_s.tokenize(__snake_case )
                      A_          =       tokenizer_r.tokenize(__snake_case )

                      self.assertListEqual(__snake_case ,__snake_case )

                 # Test that the tokenization is identical on unicode of line break type
                 A_          =       [
                     '''\u000A''',  # (line feed, '\n')
                     '''\r\n''',  # (carriage return and line feed, '\r\n')
                     '''\u000D''',  # (carriage return, '\r')
                     '''\r''',  # (carriage return, '\r')
                     '''\u000D''',  # (carriage return, '\r')
                     '''\u2028''',  # (line separator)
                     '''\u2029''',  # (paragraph separator)
                     # "\u0085", # (next line)
                 ]

                 # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                 # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                 # space (and thus into an empty list).

                 for unicode_seq in line_break_unicodes:
                      A_          =       tokenizer_s.tokenize(__snake_case )
                      A_          =       tokenizer_r.tokenize(__snake_case )

                      self.assertListEqual(__snake_case ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
                 A_          =       '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                 A_          =       f'{text_of_1_token} {text_of_1_token}'

                 A_          =       self.rust_tokenizer_class.from_pretrained(
                     __snake_case ,use_fast=__snake_case ,)
                 A_          =       tokenizer_r(__snake_case ,return_offsets_mapping=__snake_case ,add_special_tokens=__snake_case )
                 self.assertEqual(encoding.offset_mapping[0] ,(0, len(__snake_case )) )
                 self.assertEqual(
                     encoding.offset_mapping[1] ,(len(__snake_case ) + 1, len(__snake_case ) + 1 + len(__snake_case )) ,)

                 A_          =       f' {text}'

                 A_          =       self.rust_tokenizer_class.from_pretrained(
                     __snake_case ,use_fast=__snake_case ,)
                 A_          =       tokenizer_r(__snake_case ,return_offsets_mapping=__snake_case ,add_special_tokens=__snake_case )
                 self.assertEqual(encoding.offset_mapping[0] ,(1, 1 + len(__snake_case )) )
                 self.assertEqual(
                     encoding.offset_mapping[1] ,(1 + len(__snake_case ) + 1, 1 + len(__snake_case ) + 1 + len(__snake_case )) ,)


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       with self.assertRaises(__snake_case ) as context:
            self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer''' )

       self.assertTrue(
           context.exception.args[0].startswith(
               '''The `backend_tokenizer` provided does not match the expected format.''' ) )


  @require_ftfy
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       super().test_tokenization_python_rust_equals()




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       pass










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()
a__      :     Any         =		logging.get_logger(__name__)

a__      :     Tuple         =		'Hello, World!'
a__      :     Any         =		'en_XX'

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:bool				)	->				Optional[Any]:
     '''simple docstring'''


     A_          =       Path('''data_bin'''				)
     A_          =       FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(_UpperCAmelCase				).parent				)  ,						checkpoint_file=Path(_UpperCAmelCase				).name  ,						_name='''xmod_base'''  ,						arch='''xmod_base'''  ,						task='''multilingual_masked_lm'''  ,						data_name_or_path=str(_UpperCAmelCase				)  ,						bpe='''sentencepiece'''  ,						sentencepiece_model=str(Path(_UpperCAmelCase				).parent / '''sentencepiece.bpe.model'''				)  ,						src_dict=str(data_dir / '''dict.txt'''				)  ,						)
     xmod.eval()  # disable dropout
     print(_UpperCAmelCase				)

     A_          =       xmod.model.encoder.sentence_encoder
     A_          =       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=514  ,						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:
          A_          =       xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

     A_          =       XmodForSequenceClassification(_UpperCAmelCase				) if classification_head else XmodForMaskedLM(_UpperCAmelCase				)
     model.eval()

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

     A_          =       xmod_sent_encoder.layernorm_embedding.weight
     A_          =       xmod_sent_encoder.layernorm_embedding.bias

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

          # self attention
          A_          =       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.'''				)

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

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

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

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

          # end of layer

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

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

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

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

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


if __name__ == "__main__":
      a__      :     List[str]         =		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.'
      )
      a__      :     Tuple         =		parser.parse_args()
      convert_xmod_checkpoint_to_pytorch(
          args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )



