


'''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  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :Optional[Any]												=CodeGenTokenizer
  a_       :str												=CodeGenTokenizerFast
  a_       :Dict												=True
  a_       :Tuple												={"""add_prefix_space""": True}
  a_       :Optional[Any]												=False

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       __a       =   [
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
           """<|endoftext|>""",
       ]
       __a       =   dict(zip(SCREAMING_SNAKE_CASE__				,							range(len(SCREAMING_SNAKE_CASE__						)						)						)						)
       __a       =   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       __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(SCREAMING_SNAKE_CASE__						) + """\n"""						)
       with open(self.merges_file				,							"""w"""				,							encoding="""utf-8"""						) as fp:
            fp.write("""\n""".join(SCREAMING_SNAKE_CASE__						)						)

  def __a						(					self     :							Union[str, Any]				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizer.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[Any]				,							**SCREAMING_SNAKE_CASE__     :							Dict						):





       '''simple docstring'''

       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizerFast.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       __a       =   """lower newer"""
       __a       =   """lower newer"""
       return input_text, output_text

  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   CodeGenTokenizer(self.vocab_file				,							self.merges_file				,							**self.special_tokens_map						)
       __a       =   """lower newer"""
       __a       =   ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
       __a       =   tokenizer.tokenize(SCREAMING_SNAKE_CASE__				,							add_prefix_space=SCREAMING_SNAKE_CASE__						)
       self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

       __a       =   tokens + [tokenizer.unk_token]
       __a       =   [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__						)				,							SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       if not self.test_rust_tokenizer:
            return

       __a       =   self.get_tokenizer()
       __a       =   self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE__						)

       __a       =   """lower newer"""

       # Testing tokenization
       __a       =   tokenizer.tokenize(SCREAMING_SNAKE_CASE__				,							add_prefix_space=SCREAMING_SNAKE_CASE__						)
       __a       =   rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__						)
       self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

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

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

       # Testing the unknown token
       __a       =   tokens + [rust_tokenizer.unk_token]
       __a       =   [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__						)				,							SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[str]				,							*SCREAMING_SNAKE_CASE__     :							List[str]				,							**SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       pass

  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Dict=1_5						):





       '''simple docstring'''

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

                 # Simple input
                 __a       =   """This is a simple input"""
                 __a       =   ["""This is a simple input 1""", """This is a simple input 2"""]
                 __a       =   ("""This is a simple input""", """This is a pair""")
                 __a       =   [
                     ("""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(SCREAMING_SNAKE_CASE__				,							tokenizer_r.encode				,							SCREAMING_SNAKE_CASE__				,							max_length=SCREAMING_SNAKE_CASE__				,							padding="""max_length"""						)

                 # Simple input
                 self.assertRaises(SCREAMING_SNAKE_CASE__				,							tokenizer_r.encode_plus				,							SCREAMING_SNAKE_CASE__				,							max_length=SCREAMING_SNAKE_CASE__				,							padding="""max_length"""						)

                 # Simple input
                 self.assertRaises(
                     SCREAMING_SNAKE_CASE__				,							tokenizer_r.batch_encode_plus				,							SCREAMING_SNAKE_CASE__				,							max_length=SCREAMING_SNAKE_CASE__				,							padding="""max_length"""				,							)

                 # Pair input
                 self.assertRaises(SCREAMING_SNAKE_CASE__				,							tokenizer_r.encode				,							SCREAMING_SNAKE_CASE__				,							max_length=SCREAMING_SNAKE_CASE__				,							padding="""max_length"""						)

                 # Pair input
                 self.assertRaises(SCREAMING_SNAKE_CASE__				,							tokenizer_r.encode_plus				,							SCREAMING_SNAKE_CASE__				,							max_length=SCREAMING_SNAKE_CASE__				,							padding="""max_length"""						)

                 # Pair input
                 self.assertRaises(
                     SCREAMING_SNAKE_CASE__				,							tokenizer_r.batch_encode_plus				,							SCREAMING_SNAKE_CASE__				,							max_length=SCREAMING_SNAKE_CASE__				,							padding="""max_length"""				,							)

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   CodeGenTokenizer.from_pretrained(self.tmpdirname				,							pad_token="""<pad>"""						)

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

       __a       =   tokenizer.pad_token_id

       __a       =   tokenizer(SCREAMING_SNAKE_CASE__				,							padding="""max_length"""				,							max_length=3_0				,							return_tensors="""np"""						)
       __a       =   tokenizer(SCREAMING_SNAKE_CASE__				,							padding=SCREAMING_SNAKE_CASE__				,							truncate=SCREAMING_SNAKE_CASE__				,							return_tensors="""np"""						)
       __a       =   tokenizer(*SCREAMING_SNAKE_CASE__				,							padding="""max_length"""				,							max_length=6_0				,							return_tensors="""np"""						)
       __a       =   tokenizer(SCREAMING_SNAKE_CASE__				,							padding=SCREAMING_SNAKE_CASE__				,							truncate=SCREAMING_SNAKE_CASE__				,							return_tensors="""np"""						)

       # s
       # test single string max_length padding
       self.assertEqual(out_s["""input_ids"""].shape[-1]				,							3_0						)
       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]				,							3_3						)
       # 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]				,							6_0						)
       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]				,							5_2						)
       # 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 __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   """$$$"""
       __a       =   CodeGenTokenizer.from_pretrained(self.tmpdirname				,							bos_token=SCREAMING_SNAKE_CASE__				,							add_bos_token=SCREAMING_SNAKE_CASE__						)

       __a       =   """This is a simple input"""
       __a       =   ["""This is a simple input 1""", """This is a simple input 2"""]

       __a       =   tokenizer.bos_token_id

       __a       =   tokenizer(SCREAMING_SNAKE_CASE__						)
       __a       =   tokenizer(SCREAMING_SNAKE_CASE__						)

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

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

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

  @slow
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""						)

       __a       =   """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
       __a       =   """\nif len_a > len_b:      result = a\nelse:      result = b"""

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


  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       pass










'''simple docstring'''







import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


SCREAMING_SNAKE_CASE_      =			[
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
SCREAMING_SNAKE_CASE_      =			[
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

SCREAMING_SNAKE_CASE_      =			(
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

SCREAMING_SNAKE_CASE_      =			(
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

SCREAMING_SNAKE_CASE_      =			[
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




     for tf_name, hf_name in patterns:
          __a       =   k.replace(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
     return k




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					BigBirdPegasusForConditionalGeneration:
     """simple docstring"""




     __a       =   BigBirdPegasusConfig(**__SCREAMING_SNAKE_CASE		)
     __a       =   BigBirdPegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE		)
     __a       =   torch_model.state_dict()
     __a       =   {}

     # separating decoder weights
     __a       =   {k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""		)}
     __a       =   {k: tf_weights[k] for k in tf_weights if not k.startswith("""pegasus/decoder"""		)}

     for k, v in tqdm(decoder_weights.items()				,  """tf -> hf conversion"""		):
          __a       =   [k.endswith(__SCREAMING_SNAKE_CASE		) for ending in KEYS_TO_IGNORE]
          if any(__SCREAMING_SNAKE_CASE		):
               continue
          __a       =   DECODER_PATTERNS
          __a       =   rename_state_dict_key(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          if new_k not in state_dict:
               raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''		)
          if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]		):
               __a       =   v.T
          __a       =   torch.from_numpy(__SCREAMING_SNAKE_CASE		)
          assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

     for k, v in tqdm(remaining_weights.items()				,  """tf -> hf conversion"""		):
          __a       =   [k.endswith(__SCREAMING_SNAKE_CASE		) for ending in KEYS_TO_IGNORE]
          if any(__SCREAMING_SNAKE_CASE		):
               continue
          __a       =   REMAINING_PATTERNS
          __a       =   rename_state_dict_key(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
               raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''		)
          if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]		):
               __a       =   v.T
          __a       =   torch.from_numpy(__SCREAMING_SNAKE_CASE		)
          if k != "pegasus/embeddings/position_embeddings":
               assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

     __a       =   mapping["""model.embed_positions.weight"""]
     __a       =   mapping.pop("""model.embed_positions.weight"""		)
     __a ,						__a       =   torch_model.load_state_dict(__SCREAMING_SNAKE_CASE				,  strict=__SCREAMING_SNAKE_CASE		)
     __a       =   [
         k
         for k in missing
         if k
         not in [
             """final_logits_bias""",
             """model.encoder.embed_tokens.weight""",
             """model.decoder.embed_tokens.weight""",
             """lm_head.weight""",
         ]
     ]
     assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
     assert extra == [], F'''no matches found for the following tf keys {extra}'''
     return torch_model




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   tf.train.list_variables(__SCREAMING_SNAKE_CASE		)
     __a       =   {}
     __a       =   ["""global_step"""]
     for name, shape in tqdm(__SCREAMING_SNAKE_CASE				,  desc="""converting tf checkpoint to dict"""		):
          __a       =   any(pat in name for pat in ignore_name		)
          if skip_key:
               continue
          __a       =   tf.train.load_variable(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          __a       =   array
     return tf_weights




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




     __a       =   get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE		)
     __a       =   convert_bigbird_pegasus(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
     torch_model.save_pretrained(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			argparse.ArgumentParser()
 parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
 parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
 SCREAMING_SNAKE_CASE_      =			parser.parse_args()
 SCREAMING_SNAKE_CASE_      =			{}
 convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






