'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						LEDTokenizer
     SCREAMING_SNAKE_CASE__		   =						LEDTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True







     def lowerCAmelCase     (		self    ):
         super().setUp()
         __UpperCamelCase	:	Dict		    =  [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         __UpperCamelCase	:	Dict		    =  dict(zip(_lowerCamelCase     ,			range(len(_lowerCamelCase    )    )    )    )
         __UpperCamelCase	:	str		    =  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         __UpperCamelCase	:	List[Any]		    =  {'unk_token': '<unk>'}

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







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         kwargs.update(self.special_tokens_map    )
         return self.tokenizer_class.from_pretrained(self.tmpdirname     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         kwargs.update(self.special_tokens_map    )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return "lower newer", "lower newer"







     @cached_property
     def lowerCAmelCase     (		self    ):
         return LEDTokenizer.from_pretrained('allenai/led-base-16384'    )







     @cached_property
     def lowerCAmelCase     (		self    ):
         return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
         __UpperCamelCase	:	Optional[int]		    =  [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	List[Any]		    =  tokenizer(_lowerCamelCase     ,			max_length=len(_lowerCamelCase    )     ,			padding=_lowerCamelCase     ,			return_tensors='pt'    )
             self.assertIsInstance(_lowerCamelCase     ,			_lowerCamelCase    )

             self.assertEqual((2, 9)     ,			batch.input_ids.shape    )
             self.assertEqual((2, 9)     ,			batch.attention_mask.shape    )
             __UpperCamelCase	:	Union[str, Any]		    =  batch.input_ids.tolist()[0]
             self.assertListEqual(_lowerCamelCase     ,			_lowerCamelCase    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	Optional[Any]		    =  tokenizer(_lowerCamelCase     ,			padding=_lowerCamelCase     ,			return_tensors='pt'    )
             self.assertIn('input_ids'     ,			_lowerCamelCase    )
             self.assertIn('attention_mask'     ,			_lowerCamelCase    )
             self.assertNotIn('labels'     ,			_lowerCamelCase    )
             self.assertNotIn('decoder_attention_mask'     ,			_lowerCamelCase    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  [
             'Summary of the text.',
             'Another summary.',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	int		    =  tokenizer(text_target=_lowerCamelCase     ,			max_length=3_2     ,			padding='max_length'     ,			return_tensors='pt'    )
             self.assertEqual(3_2     ,			targets['input_ids'].shape[1]    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	Tuple		    =  tokenizer(
                 ['I am a small frog' * 1_0_2_4, 'I am a small frog']     ,			padding=_lowerCamelCase     ,			truncation=_lowerCamelCase     ,			return_tensors='pt'    )
             self.assertIsInstance(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertEqual(batch.input_ids.shape     ,			(2, 5_1_2_2)    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  ['A long paragraph for summarization.']
         __UpperCamelCase	:	str		    =  [
             'Summary of the text.',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	Tuple		    =  tokenizer(_lowerCamelCase     ,			return_tensors='pt'    )
             __UpperCamelCase	:	Dict		    =  tokenizer(text_target=_lowerCamelCase     ,			return_tensors='pt'    )
             __UpperCamelCase	:	Dict		    =  inputs['input_ids']
             __UpperCamelCase	:	Any		    =  targets['input_ids']
             self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
             self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
             self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
             self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	Optional[int]		    =  ['Summary of the text.', 'Another summary.']
             __UpperCamelCase	:	str		    =  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

             __UpperCamelCase	:	Optional[int]		    =  tokenizer(_lowerCamelCase     ,			padding=_lowerCamelCase    )
             __UpperCamelCase	:	Any		    =  [[0] * len(_lowerCamelCase    ) for x in encoded_output['input_ids']]
             __UpperCamelCase	:	Any		    =  tokenizer.pad(_lowerCamelCase    )
             self.assertSequenceEqual(outputs['global_attention_mask']     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         pass







     def lowerCAmelCase     (		self    ):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
                 __UpperCamelCase	:	Dict		    =  self.rust_tokenizer_class.from_pretrained(_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	List[Any]		    =  self.tokenizer_class.from_pretrained(_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	List[str]		    =  'A, <mask> AllenNLP sentence.'
                 __UpperCamelCase	:	Union[str, Any]		    =  tokenizer_r.encode_plus(_lowerCamelCase     ,			add_special_tokens=_lowerCamelCase     ,			return_token_type_ids=_lowerCamelCase    )
                 __UpperCamelCase	:	Any		    =  tokenizer_p.encode_plus(_lowerCamelCase     ,			add_special_tokens=_lowerCamelCase     ,			return_token_type_ids=_lowerCamelCase    )
                 self.assertEqual(sum(tokens_r['token_type_ids']    )     ,			sum(tokens_p['token_type_ids']    )    )
                 self.assertEqual(
                     sum(tokens_r['attention_mask']    ) / len(tokens_r['attention_mask']    )     ,			sum(tokens_p['attention_mask']    ) / len(tokens_p['attention_mask']    )     ,			)

                 __UpperCamelCase	:	Any		    =  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']    )
                 __UpperCamelCase	:	Optional[int]		    =  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']    )
                 self.assertSequenceEqual(tokens_p['input_ids']     ,			[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )
                 self.assertSequenceEqual(tokens_r['input_ids']     ,			[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )

                 self.assertSequenceEqual(
                     _lowerCamelCase     ,			['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )
                 self.assertSequenceEqual(
                     _lowerCamelCase     ,			['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )




'''simple docstring'''
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  _UpperCamelCase (     _a	:	int				,     _a	:	Tuple				,     _a	:	Dict=None			):




    """simple docstring"""



    assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
    __UpperCamelCase	:	List[Any]		    =  nn.Parameter(_a			)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
        __UpperCamelCase	:	Dict		    =  nn.Parameter(_a			)







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	str				,     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	str		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[2]			)

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







def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	Any				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[2]			)
    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[3]			)

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







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	str				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  weights[0][0][0]
    __UpperCamelCase	:	Tuple		    =  np.asarray(layer_norm_a[0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(layer_norm_a[1]			)
    set_param(
        torch_block.attention.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

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

    # intermediate weighs
    __UpperCamelCase	:	int		    =  weights[2][0][1][2]

    # Chunked Feed Forward
    if len(_a			) == 4:
        __UpperCamelCase	:	Optional[Any]		    =  intermediate_weights[2]

    # layernorm 2
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[0][0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(intermediate_weights[0][1]			)
    set_param(
        torch_block.feed_forward.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # intermediate dense
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(intermediate_weights[1][0]			)
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[1][1]			)
    set_param(
        torch_block.feed_forward.dense.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )

    # intermediate out
    __UpperCamelCase	:	Dict		    =  np.asarray(intermediate_weights[4][0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(intermediate_weights[4][1]			)
    set_param(
        torch_block.feed_forward.output.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	int				,     _a	:	str				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  torch_model.reformer

    # word embeds
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    set_param(
        torch_model_reformer.embeddings.word_embeddings				,     torch.tensor(_a			)				,     )

    if isinstance(weights[3]				,     _a			):
        __UpperCamelCase	:	Dict		    =  torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights			)			):
            __UpperCamelCase	:	str		    =  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"""
            __UpperCamelCase	:	Tuple		    =  nn.Parameter(torch.tensor(_a			)			)

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

    # output layer norm
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(weights[7][0]			)
    __UpperCamelCase	:	Any		    =  np.asarray(weights[7][1]			)
    set_param(
        torch_model_reformer.encoder.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # output embeddings
    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[9][0]			)
    __UpperCamelCase	:	Union[str, Any]		    =  np.asarray(weights[9][1]			)
    set_param(
        torch_model.lm_head.decoder				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	List[Any]				,     _a	:	Optional[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  ReformerConfig.from_json_file(_a			)
    print(f"""Building PyTorch model from configuration: {config}"""			)
    __UpperCamelCase	:	List[str]		    =  ReformerModelWithLMHead(_a			)

    with open(_a				,     'rb'			) as f:
        __UpperCamelCase	:	Tuple		    =  pickle.load(_a			)['weights']

    set_model_weights_in_torch(_a				,     _a				,     config.hidden_size			)

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


if __name__ == "__main__":
  a=  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.'''
  )
  a=  parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



