


import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase    =					get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_		(				__UpperCAmelCase      ,		unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						GPTSwaTokenizer
 __snake_case				=						False
 __snake_case				=						True
 __snake_case				=						False



 def 		UpperCamelCase__  (			self      ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       snake_case_ =   GPTSwaTokenizer(__SCREAMING_SNAKE_CASE	,   eos_token='''<unk>'''	,   bos_token='''<unk>'''	,   pad_token='''<unk>'''      )

       tokenizer.save_pretrained(self.tmpdirname      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   '''This is a test'''
       snake_case_ =   '''This is a test'''
       return input_text, output_text



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   '''<s>'''
       snake_case_ =   1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE      )	,   __SCREAMING_SNAKE_CASE      )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE      )	,   __SCREAMING_SNAKE_CASE      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   list(self.get_tokenizer().get_vocab().keys()      )

       self.assertEqual(vocab_keys[0]	,   '''<unk>'''      )
       self.assertEqual(vocab_keys[1]	,   '''<s>'''      )
       self.assertEqual(vocab_keys[-1]	,   '''j'''      )
       self.assertEqual(len(__SCREAMING_SNAKE_CASE      )	,   20_00      )



 def 		UpperCamelCase__  (			self      ):
       self.assertEqual(self.get_tokenizer().vocab_size	,   20_00      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   GPTSwaTokenizer(__SCREAMING_SNAKE_CASE      )

       snake_case_ =   tokenizer.tokenize('''This is a test'''      )
       self.assertListEqual(__SCREAMING_SNAKE_CASE	,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

       self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )	,   [4_65, 2_87, 2_65, 6_31, 8_42]      )

       snake_case_ =   tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
       # fmt: off
       self.assertListEqual(
           __SCREAMING_SNAKE_CASE	,   ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']	,   )
       # fmt: on

       snake_case_ =   tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )
       self.assertListEqual(
           __SCREAMING_SNAKE_CASE	,   [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]	,   )

       snake_case_ =   tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE      )
       # fmt: off
       self.assertListEqual(
           __SCREAMING_SNAKE_CASE	,   ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']      )
       # fmt: on



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   GPTSwaTokenizer(__SCREAMING_SNAKE_CASE      )
       snake_case_ =   ['''This is a test''', '''I was born in 92000, and this is falsé.''']
       snake_case_ =   [
           [4_65, 2_87, 2_65, 6_31, 8_42],
           [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
       ]

       # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
       for text, expected_ids in zip(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE      ):
             self.assertListEqual(tokenizer.encode_fast(__SCREAMING_SNAKE_CASE      )	,   __SCREAMING_SNAKE_CASE      )

       # Test that decode_fast returns the input text
       for text, token_ids in zip(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE      ):
             self.assertEqual(tokenizer.decode_fast(__SCREAMING_SNAKE_CASE      )	,   __SCREAMING_SNAKE_CASE      )





 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   [
           '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
           '''Hey there, how are you doing this fine day?''',
           '''This is a text with a trailing spaces followed by a dot     .''',
           '''Häj sväjs lillebrör! =)''',
           '''Det är inget fel på Mr. Cool''',
       ]

       # fmt: off
       snake_case_ =   {'''input_ids''': [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
       # fmt: on
       self.tokenizer_integration_test_util(
           expected_encoding=__SCREAMING_SNAKE_CASE	,   model_name='''AI-Sweden/gpt-sw3-126m'''	,   sequences=__SCREAMING_SNAKE_CASE	,   )



import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      )->						Any:




      """simple docstring"""

      assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
      snake_case_ =   nn.Parameter(SCREAMING_SNAKE_CASE      )
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
            snake_case_ =   nn.Parameter(SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   np.asarray(weights[0]      )
      snake_case_ =   np.asarray(weights[1]      )
      snake_case_ =   np.asarray(weights[2]      )

      set_param(
          torch_layer.self_attention.query_key      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.self_attention.value      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.output.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).view(-1      ,      SCREAMING_SNAKE_CASE      ).contiguous().transpose(0      ,      1      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   np.asarray(weights[0]      )
      snake_case_ =   np.asarray(weights[1]      )
      snake_case_ =   np.asarray(weights[2]      )
      snake_case_ =   np.asarray(weights[3]      )

      set_param(
          torch_layer.self_attention.query      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.self_attention.key      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.self_attention.value      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.output.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).view(-1      ,      SCREAMING_SNAKE_CASE      ).contiguous().transpose(0      ,      1      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   weights[0][0][0]
      snake_case_ =   np.asarray(layer_norm_a[0]      )
      snake_case_ =   np.asarray(layer_norm_a[1]      )
      set_param(
          torch_block.attention.layer_norm      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # lsh weights + output
      snake_case_ =   weights[0][1]
      if len(SCREAMING_SNAKE_CASE      ) < 4:
            set_layer_weights_in_torch_lsh(SCREAMING_SNAKE_CASE      ,      torch_block.attention      ,      SCREAMING_SNAKE_CASE      )
      else:
            set_layer_weights_in_torch_local(SCREAMING_SNAKE_CASE      ,      torch_block.attention      ,      SCREAMING_SNAKE_CASE      )

      # intermediate weighs
      snake_case_ =   weights[2][0][1][2]

      # Chunked Feed Forward
      if len(SCREAMING_SNAKE_CASE      ) == 4:
            snake_case_ =   intermediate_weights[2]

      # layernorm 2
      snake_case_ =   np.asarray(intermediate_weights[0][0]      )
      snake_case_ =   np.asarray(intermediate_weights[0][1]      )
      set_param(
          torch_block.feed_forward.layer_norm      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # intermediate dense
      snake_case_ =   np.asarray(intermediate_weights[1][0]      )
      snake_case_ =   np.asarray(intermediate_weights[1][1]      )
      set_param(
          torch_block.feed_forward.dense.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(0      ,      1      ).contiguous()      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # intermediate out
      snake_case_ =   np.asarray(intermediate_weights[4][0]      )
      snake_case_ =   np.asarray(intermediate_weights[4][1]      )
      set_param(
          torch_block.feed_forward.output.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(0      ,      1      ).contiguous()      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   torch_model.reformer

      # word embeds
      snake_case_ =   np.asarray(weights[1]      )
      set_param(
          torch_model_reformer.embeddings.word_embeddings      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      if isinstance(weights[3]      ,      SCREAMING_SNAKE_CASE      ):
            snake_case_ =   torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights      )      ):
                  snake_case_ =   np.asarray(weights[3][emb_idx][0]      )
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), f'''{position_embeddings[emb_idx]} emb does not match'''
                  snake_case_ =   nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE      )      )

      snake_case_ =   weights[5]
      assert len(torch_model_reformer.encoder.layers      ) * 4 == len(
          SCREAMING_SNAKE_CASE      ), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers      ):
            snake_case_ =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

      # output layer norm
      snake_case_ =   np.asarray(weights[7][0]      )
      snake_case_ =   np.asarray(weights[7][1]      )
      set_param(
          torch_model_reformer.encoder.layer_norm      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # output embeddings
      snake_case_ =   np.asarray(weights[9][0]      )
      snake_case_ =   np.asarray(weights[9][1]      )
      set_param(
          torch_model.lm_head.decoder      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(0      ,      1      ).contiguous()      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE      )
      print(f'''Building PyTorch model from configuration: {config}'''      )
      snake_case_ =   ReformerModelWithLMHead(SCREAMING_SNAKE_CASE      )

      with open(SCREAMING_SNAKE_CASE      ,      '''rb'''      ) as f:
            snake_case_ =   pickle.load(SCREAMING_SNAKE_CASE      )['''weights''']

      set_model_weights_in_torch(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      config.hidden_size      )

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''      )
      torch.save(model.state_dict()      ,      SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   UpperCAmelCase    =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained Reformer model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   UpperCAmelCase    =					parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)