






import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






class 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  ProphetNetTokenizer
      __snake_case       =  False







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->Optional[Any]:




       """simple docstring"""


       super().setUp()

       a		  =      [
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       a		  =      os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''vocab_file''']  )
       with open(self.vocab_file ,					'''w''' ,					encoding='''utf-8'''  ) as vocab_writer:
        vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )







      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						str  )    ->Dict:




       """simple docstring"""


       a		  =      '''UNwant\u00E9d,running'''
       a		  =      '''unwanted, running'''
       return input_text, output_text







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      self.tokenizer_class(self.vocab_file  )

       a		  =      tokenizer.tokenize('''UNwant\u00E9d,running'''  )
       self.assertListEqual(__UpperCAmelCase ,					['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase  ) ,					[9, 6, 7, 12, 10, 11]  )







      def       __lowerCAmelCase					(      self      :						int  )    ->Any:




       """simple docstring"""


       a		  =      BasicTokenizer()

       self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''  ) ,					['''ah''', '''\u535A''', '''\u63A8''', '''zz''']  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->int:




       """simple docstring"""


       a		  =      BasicTokenizer(do_lower_case=__UpperCAmelCase  )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  ) ,					['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  ) ,					['''hello''']  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Optional[int]:




       """simple docstring"""


       a		  =      BasicTokenizer(do_lower_case=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase  )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  ) ,					['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  ) ,					['''h\u00E9llo''']  )







      def       __lowerCAmelCase					(      self      :						Dict  )    ->str:




       """simple docstring"""


       a		  =      BasicTokenizer(do_lower_case=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase  )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  ) ,					['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  ) ,					['''hello''']  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->Dict:




       """simple docstring"""


       a		  =      BasicTokenizer(do_lower_case=__UpperCAmelCase  )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  ) ,					['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  ) ,					['''hello''']  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      BasicTokenizer(do_lower_case=__UpperCAmelCase  )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  ) ,					['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Tuple:




       """simple docstring"""


       a		  =      BasicTokenizer(do_lower_case=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase  )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  ) ,					['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )







      def       __lowerCAmelCase					(      self      :						int  )    ->Optional[int]:




       """simple docstring"""


       a		  =      BasicTokenizer(do_lower_case=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase  )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  ) ,					['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->int:




       """simple docstring"""


       a		  =      BasicTokenizer(do_lower_case=__UpperCAmelCase ,					never_split=['''[UNK]''']  )

       self.assertListEqual(
           tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]'''  ) ,					['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->int:




       """simple docstring"""


       a		  =      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

       a		  =      {}
       for i, token in enumerate(__UpperCAmelCase  ):
        a		  =      i
       a		  =      WordpieceTokenizer(vocab=__UpperCAmelCase ,					unk_token='''[UNK]'''  )

       self.assertListEqual(tokenizer.tokenize(''''''  ) ,					[]  )

       self.assertListEqual(tokenizer.tokenize('''unwanted running'''  ) ,					['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']  )

       self.assertListEqual(tokenizer.tokenize('''unwantedX running'''  ) ,					['''[UNK]''', '''runn''', '''##ing''']  )







      @require_torch
      def       __lowerCAmelCase					(      self      :						int  )    ->int:




       """simple docstring"""


       a		  =      self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''  )

       a		  =      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       a		  =      [1_037, 2_146, 20_423, 2_005, 7_680, 7_849, 3_989, 1_012, 102]
       a		  =      tokenizer(__UpperCAmelCase ,					padding=__UpperCAmelCase ,					return_tensors='''pt'''  )
       self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase  )
       a		  =      list(batch.input_ids.numpy()[0]  )
       self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       self.assertEqual((2, 9) ,					batch.input_ids.shape  )
       self.assertEqual((2, 9) ,					batch.attention_mask.shape  )







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->List[str]:




       """simple docstring"""


       self.assertTrue(_is_whitespace(''' '''  )  )
       self.assertTrue(_is_whitespace('''\t'''  )  )
       self.assertTrue(_is_whitespace('''\r'''  )  )
       self.assertTrue(_is_whitespace('''\n'''  )  )
       self.assertTrue(_is_whitespace('''\u00A0'''  )  )

       self.assertFalse(_is_whitespace('''A'''  )  )
       self.assertFalse(_is_whitespace('''-'''  )  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->List[str]:




       """simple docstring"""


       self.assertTrue(_is_control('''\u0005'''  )  )

       self.assertFalse(_is_control('''A'''  )  )
       self.assertFalse(_is_control(''' '''  )  )
       self.assertFalse(_is_control('''\t'''  )  )
       self.assertFalse(_is_control('''\r'''  )  )







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       self.assertTrue(_is_punctuation('''-'''  )  )
       self.assertTrue(_is_punctuation('''$'''  )  )
       self.assertTrue(_is_punctuation('''`'''  )  )
       self.assertTrue(_is_punctuation('''.'''  )  )

       self.assertFalse(_is_punctuation('''A'''  )  )
       self.assertFalse(_is_punctuation(''' '''  )  )







      @slow
      def       __lowerCAmelCase					(      self      :						List[str]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''  )

       a		  =      tokenizer.encode('''sequence builders''' ,					add_special_tokens=__UpperCAmelCase  )
       a		  =      tokenizer.encode('''multi-sequence build''' ,					add_special_tokens=__UpperCAmelCase  )

       a		  =      tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase  )
       a		  =      tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase ,					__UpperCAmelCase  )

       assert encoded_sentence == text + [102]
       assert encoded_pair == text + [102] + text_a + [102]










import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def 						_a    (      a :List[Any] )							->			Optional[int]:
 a		  =      []
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
         F"""stage{idx}.patch_embed.proj.weight""",
     ) )
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
         F"""stage{idx}.patch_embed.proj.bias""",
     ) )
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
         F"""stage{idx}.patch_embed.norm.weight""",
     ) )
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
         F"""stage{idx}.patch_embed.norm.bias""",
     ) )
 return embed
def 						_a    (      a :List[Any]	,							a :Optional[int] )							->			Dict:
 a		  =      []
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
     ) )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""") )
 return attention_weights
def 						_a    (      a :Any )							->			List[Any]:
 a		  =      []
 token.append((F"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''') )
 return token
def 						_a    (      )							->			Optional[int]:
 a		  =      []
 head.append(('''layernorm.weight''', '''norm.weight''') )
 head.append(('''layernorm.bias''', '''norm.bias''') )
 head.append(('''classifier.weight''', '''head.weight''') )
 head.append(('''classifier.bias''', '''head.bias''') )
 return head
def 						_a    (      a :Tuple	,							a :Optional[int]	,							a :List[Any]	,							a :Union[str, Any] )							->			Optional[int]:
 a		  =      '''imagenet-1k-id2label.json'''
 a		  =      1_000

 a		  =      '''huggingface/label-files'''
 a		  =      num_labels
 a		  =      json.load(open(cached_download(hf_hub_url(a	,							a	,							repo_type='''dataset''' ) )	,							'''r''' ) )
 a		  =      {int(a ): v for k, v in idalabel.items()}

 a		  =      idalabel
 a		  =      {v: k for k, v in idalabel.items()}

 a		  =      a		  =      CvtConfig(num_labels=a	,							idalabel=a	,							labelaid=a )

 # For depth size 13 (13 = 1+2+10)
 if cvt_model.rsplit('''/'''	,							1 )[-1][4:6] == "13":
  a		  =      [1, 2, 10]

 # For depth size 21 (21 = 1+4+16)
 elif cvt_model.rsplit('''/'''	,							1 )[-1][4:6] == "21":
  a		  =      [1, 4, 16]

 # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
 else:
  a		  =      [2, 2, 20]
  a		  =      [3, 12, 16]
  a		  =      [192, 768, 1_024]

 a		  =      CvtForImageClassification(a )
 a		  =      AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
 a		  =      image_size
 a		  =      torch.load(a	,							map_location=torch.device('''cpu''' ) )

 a		  =      OrderedDict()
 a		  =      []

 for idx in range(len(config.depth ) ):
  if config.cls_token[idx]:
   a		  =      list_of_state_dict + cls_token(a )
  a		  =      list_of_state_dict + embeddings(a )
  for cnt in range(config.depth[idx] ):
   a		  =      list_of_state_dict + attention(a	,							a )

 a		  =      list_of_state_dict + final()
 for gg in list_of_state_dict:
  print(a )
 for i in range(len(a ) ):
  a		  =      original_weights[list_of_state_dict[i][1]]

 model.load_state_dict(a )
 model.save_pretrained(a )
 image_processor.save_pretrained(a )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
     UpperCAmelCase__				   =		argparse.ArgumentParser()
     parser.add_argument(
         "--cvt_model",
         default="cvt-w24",
         type=str,
         help="Name of the cvt model you'd like to convert.",
     )
     parser.add_argument(
         "--image_size",
         default=384,
         type=int,
         help="Input Image Size",
     )
     parser.add_argument(
         "--cvt_file_name",
         default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
         type=str,
         help="Input Image Size",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
     )

     UpperCAmelCase__				   =		parser.parse_args()
     convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


