





'''simple docstring'''







import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 			_lowercase						(       __a				,		unittest.TestCase					):
     _UpperCAmelCase										=					CodeGenTokenizer
     _UpperCAmelCase										=					CodeGenTokenizerFast
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					{'''add_prefix_space''': True}
     _UpperCAmelCase										=					False







     def     UpperCamelCase			( self    ) ->	Tuple:
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          snake_case				=			[
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
              '''<|endoftext|>''',
          ]
          snake_case				=			dict(zip(A__    ,   range(len(A__    )    )    )    )
          snake_case				=			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          snake_case				=			{'''unk_token''': '''<unk>'''}

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







     def     UpperCamelCase			( self    ,   **A__    ) ->	Union[str, Any]:
          kwargs.update(self.special_tokens_map    )
          return CodeGenTokenizer.from_pretrained(self.tmpdirname    ,   **A__    )







     def     UpperCamelCase			( self    ,   **A__    ) ->	Union[str, Any]:
          kwargs.update(self.special_tokens_map    )
          return CodeGenTokenizerFast.from_pretrained(self.tmpdirname    ,   **A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Tuple:
          snake_case				=			'''lower newer'''
          snake_case				=			'''lower newer'''
          return input_text, output_text







     def     UpperCamelCase			( self    ) ->	List[Any]:
          snake_case				=			CodeGenTokenizer(self.vocab_file    ,   self.merges_file    ,   **self.special_tokens_map    )
          snake_case				=			'''lower newer'''
          snake_case				=			['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
          snake_case				=			tokenizer.tokenize(A__    ,   add_prefix_space=A__    )
          self.assertListEqual(A__    ,   A__    )

          snake_case				=			tokens + [tokenizer.unk_token]
          snake_case				=			[14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A__    )    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Optional[int]:
          if not self.test_rust_tokenizer:
               return

          snake_case				=			self.get_tokenizer()
          snake_case				=			self.get_rust_tokenizer(add_prefix_space=A__    )

          snake_case				=			'''lower newer'''

          # Testing tokenization
          snake_case				=			tokenizer.tokenize(A__    ,   add_prefix_space=A__    )
          snake_case				=			rust_tokenizer.tokenize(A__    )
          self.assertListEqual(A__    ,   A__    )

          # Testing conversion to ids without special tokens
          snake_case				=			tokenizer.encode(A__    ,   add_special_tokens=A__    ,   add_prefix_space=A__    )
          snake_case				=			rust_tokenizer.encode(A__    ,   add_special_tokens=A__    )
          self.assertListEqual(A__    ,   A__    )

          # Testing conversion to ids with special tokens
          snake_case				=			self.get_rust_tokenizer(add_prefix_space=A__    )
          snake_case				=			tokenizer.encode(A__    ,   add_prefix_space=A__    )
          snake_case				=			rust_tokenizer.encode(A__    )
          self.assertListEqual(A__    ,   A__    )

          # Testing the unknown token
          snake_case				=			tokens + [rust_tokenizer.unk_token]
          snake_case				=			[14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A__    )    ,   A__    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	List[str]:
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass







     def     UpperCamelCase			( self    ,   A__=15    ) ->	Tuple:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
                    snake_case				=			self.rust_tokenizer_class.from_pretrained(A__    ,   **A__    )

                    # Simple input
                    snake_case				=			'''This is a simple input'''
                    snake_case				=			['''This is a simple input 1''', '''This is a simple input 2''']
                    snake_case				=			('''This is a simple input''', '''This is a pair''')
                    snake_case				=			[
                        ('''This is a simple input 1''', '''This is a simple input 2'''),
                        ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                    ]

                    # Simple input tests
                    self.assertRaises(A__    ,   tokenizer_r.encode    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

                    # Simple input
                    self.assertRaises(A__    ,   tokenizer_r.encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

                    # Simple input
                    self.assertRaises(
                        A__    ,   tokenizer_r.batch_encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    ,   )

                    # Pair input
                    self.assertRaises(A__    ,   tokenizer_r.encode    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

                    # Pair input
                    self.assertRaises(A__    ,   tokenizer_r.encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

                    # Pair input
                    self.assertRaises(
                        A__    ,   tokenizer_r.batch_encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    ,   )







     def     UpperCamelCase			( self    ) ->	Tuple:
          snake_case				=			CodeGenTokenizer.from_pretrained(self.tmpdirname    ,   pad_token='''<pad>'''    )

          # Simple input
          snake_case				=			'''This is a simple input'''
          snake_case				=			['''This is a simple input looooooooong''', '''This is a simple input''']
          snake_case				=			('''This is a simple input''', '''This is a pair''')
          snake_case				=			[
              ('''This is a simple input loooooong''', '''This is a simple input'''),
              ('''This is a simple pair loooooong''', '''This is a simple pair'''),
          ]

          snake_case				=			tokenizer.pad_token_id

          snake_case				=			tokenizer(A__    ,   padding='''max_length'''    ,   max_length=30    ,   return_tensors='''np'''    )
          snake_case				=			tokenizer(A__    ,   padding=A__    ,   truncate=A__    ,   return_tensors='''np'''    )
          snake_case				=			tokenizer(*A__    ,   padding='''max_length'''    ,   max_length=60    ,   return_tensors='''np'''    )
          snake_case				=			tokenizer(A__    ,   padding=A__    ,   truncate=A__    ,   return_tensors='''np'''    )

          # s
          # test single string max_length padding
          self.assertEqual(out_s['''input_ids'''].shape[-1]    ,   30    )
          self.assertTrue(pad_token_id in out_s['''input_ids''']    )
          self.assertTrue(0 in out_s['''attention_mask''']    )

          # s2
          # test automatic padding
          self.assertEqual(out_sa['''input_ids'''].shape[-1]    ,   33    )
          # long slice doesn't have padding
          self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]    )
          self.assertFalse(0 in out_sa['''attention_mask'''][0]    )
          # short slice does have padding
          self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]    )
          self.assertTrue(0 in out_sa['''attention_mask'''][1]    )

          # p
          # test single pair max_length padding
          self.assertEqual(out_p['''input_ids'''].shape[-1]    ,   60    )
          self.assertTrue(pad_token_id in out_p['''input_ids''']    )
          self.assertTrue(0 in out_p['''attention_mask''']    )

          # p2
          # test automatic padding pair
          self.assertEqual(out_pa['''input_ids'''].shape[-1]    ,   52    )
          # long slice pair doesn't have padding
          self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]    )
          self.assertFalse(0 in out_pa['''attention_mask'''][0]    )
          # short slice pair does have padding
          self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]    )
          self.assertTrue(0 in out_pa['''attention_mask'''][1]    )







     def     UpperCamelCase			( self    ) ->	str:
          snake_case				=			'''$$$'''
          snake_case				=			CodeGenTokenizer.from_pretrained(self.tmpdirname    ,   bos_token=A__    ,   add_bos_token=A__    )

          snake_case				=			'''This is a simple input'''
          snake_case				=			['''This is a simple input 1''', '''This is a simple input 2''']

          snake_case				=			tokenizer.bos_token_id

          snake_case				=			tokenizer(A__    )
          snake_case				=			tokenizer(A__    )

          self.assertEqual(out_s.input_ids[0]    ,   A__    )
          self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids    )    )

          snake_case				=			tokenizer.decode(out_s.input_ids    )
          snake_case				=			tokenizer.batch_decode(out_sa.input_ids    )

          self.assertEqual(decode_s.split()[0]    ,   A__    )
          self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa    )    )







     @slow
     def     UpperCamelCase			( self    ) ->	Any:
          snake_case				=			CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono'''    )

          snake_case				=			'''\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'''
          snake_case				=			'''\nif len_a > len_b:      result = a\nelse:      result = b'''

          snake_case				=			tokenizer.encode(A__    )
          snake_case				=			['''^#''', re.escape('''<|endoftext|>'''    ), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
          snake_case				=			tokenizer.decode(A__    ,   truncate_before_pattern=A__    )
          self.assertEqual(A__    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
          pass








'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def    __UpperCamelCase	(	a    : Optional[int]     ) ->Dict:
 snake_case				=			[
     '''encoder.version''',
     '''decoder.version''',
     '''model.encoder.version''',
     '''model.decoder.version''',
     '''decoder.output_projection.weight''',
     '''_float_tensor''',
     '''encoder.embed_positions._float_tensor''',
     '''decoder.embed_positions._float_tensor''',
 ]
 for k in ignore_keys:
  state_dict.pop(a					,   a     )

def    __UpperCamelCase	(	a    : Optional[Any]     ) ->int:
 snake_case				=			list(s_dict.keys()     )
 for key in keys:
  if "transformer_layers" in key:
   snake_case				=			s_dict.pop(a     )
  elif "subsample" in key:
   snake_case				=			s_dict.pop(a     )

def    __UpperCamelCase	(	a    : Optional[int]     ) ->Optional[int]:
 snake_case				,		snake_case				=			emb.weight.shape
 snake_case				=			nn.Linear(a					,   a					,   bias=a     )
 snake_case				=			emb.weight.data
 return lin_layer

def    __UpperCamelCase	(	a    : Any					,   a    : Tuple     ) ->Tuple:
 snake_case				=			torch.load(a					,   map_location='''cpu'''     )
 snake_case				=			mam_aaa['''args''']
 snake_case				=			mam_aaa['''model''']
 snake_case				=			state_dict['''decoder.output_projection.weight''']

 remove_ignore_keys_(a     )
 rename_keys(a     )

 snake_case				=			state_dict['''decoder.embed_tokens.weight'''].shape[0]

 snake_case				=			args.share_decoder_input_output_embed

 snake_case				=			[int(a     ) for i in args.conv_kernel_sizes.split(''','''     )]
 snake_case				=			SpeechaTextConfig(
     vocab_size=a					,   max_source_positions=args.max_source_positions					,   max_target_positions=args.max_target_positions					,   encoder_layers=args.encoder_layers					,   decoder_layers=args.decoder_layers					,   encoder_attention_heads=args.encoder_attention_heads					,   decoder_attention_heads=args.decoder_attention_heads					,   encoder_ffn_dim=args.encoder_ffn_embed_dim					,   decoder_ffn_dim=args.decoder_ffn_embed_dim					,   d_model=args.encoder_embed_dim					,   dropout=args.dropout					,   attention_dropout=args.attention_dropout					,   activation_dropout=args.activation_dropout					,   activation_function='''relu'''					,   num_conv_layers=len(a     )					,   conv_channels=args.conv_channels					,   conv_kernel_sizes=a					,   input_feat_per_channel=args.input_feat_per_channel					,   input_channels=args.input_channels					,   tie_word_embeddings=a					,   num_beams=5					,   max_length=200					,   use_cache=a					,   decoder_start_token_id=2					,   early_stopping=a					,   )

 snake_case				=			SpeechaTextForConditionalGeneration(a     )
 snake_case				,		snake_case				=			model.model.load_state_dict(a					,   strict=a     )
 if len(a     ) > 0 and not set(a     ) <= {
     "encoder.embed_positions.weights",
     "decoder.embed_positions.weights",
 }:
  raise ValueError(
      '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
      f""" but all the following weights are missing {missing}"""     )

 if tie_embeds:
  snake_case				=			make_linear_from_emb(model.model.decoder.embed_tokens     )
 else:
  snake_case				=			lm_head_weights

 model.save_pretrained(a     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   _lowercase       						=			parser.parse_args()
   convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

