


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

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors




@require_tokenizers
class      lowerCamelCase_  (      __a	,			unittest.TestCase ):
      lowerCAmelCase__							=	MvpTokenizer
      lowerCAmelCase__							=	MvpTokenizerFast
      lowerCAmelCase__							=	True
      lowerCAmelCase__							=	filter_roberta_detectors






      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


          super().setUp()
          UpperCAmelCase__     :						Optional[int]						=							[
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          UpperCAmelCase__     :						Tuple						=							dict(zip(UpperCamelCase_		,		range(len(UpperCamelCase_					)					)					)					)
          UpperCAmelCase__     :						Any						=							['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          UpperCAmelCase__     :						Union[str, Any]						=							{'''unk_token''': '''<unk>'''}

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






      def lowercase_				(   self							:       Dict		,		**_A							:       Dict					):


          '''simple docstring'''


          kwargs.update(self.special_tokens_map					)
          return self.tokenizer_class.from_pretrained(self.tmpdirname		,		**UpperCamelCase_					)






      def lowercase_				(   self							:       List[str]		,		**_A							:       List[str]					):


          '''simple docstring'''


          kwargs.update(self.special_tokens_map					)
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,		**UpperCamelCase_					)






      def lowercase_				(   self							:       Union[str, Any]		,		_A							:       Tuple					):


          '''simple docstring'''


          return "lower newer", "lower newer"






      @cached_property
      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''					)






      @cached_property
      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''					)






      @require_torch
      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          UpperCAmelCase__     :						Union[str, Any]						=							[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

              self.assertEqual((2, 9)		,		batch.input_ids.shape					)
              self.assertEqual((2, 9)		,		batch.attention_mask.shape					)
              UpperCAmelCase__     :						Any						=							batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCamelCase_		,		UpperCamelCase_					)
              # Test that special tokens are reset






      @require_torch
      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__     :						Dict						=							tokenizer(UpperCamelCase_		,		padding=UpperCamelCase_		,		return_tensors='''pt'''					)
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''		,		UpperCamelCase_					)
              self.assertIn('''attention_mask'''		,		UpperCamelCase_					)
              self.assertNotIn('''labels'''		,		UpperCamelCase_					)
              self.assertNotIn('''decoder_attention_mask'''		,		UpperCamelCase_					)






      @require_torch
      def lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          UpperCAmelCase__     :						str						=							[
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__     :						int						=							tokenizer(text_target=UpperCamelCase_		,		max_length=32		,		padding='''max_length'''		,		return_tensors='''pt'''					)
              self.assertEqual(32		,		targets['''input_ids'''].shape[1]					)






      @require_torch
      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__     :						int						=							tokenizer(
                  ['''I am a small frog''' * 1_024, '''I am a small frog''']		,		padding=UpperCamelCase_		,		truncation=UpperCamelCase_		,		return_tensors='''pt'''					)
              self.assertIsInstance(UpperCamelCase_		,		UpperCamelCase_					)
              self.assertEqual(batch.input_ids.shape		,		(2, 1_024)					)






      @require_torch
      def lowercase_				(   self							:       Union[str, Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							['''A long paragraph for summarization.''']
          UpperCAmelCase__     :						Union[str, Any]						=							[
              '''Summary of the text.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__     :						List[Any]						=							tokenizer(UpperCamelCase_		,		text_target=UpperCamelCase_		,		return_tensors='''pt'''					)
              UpperCAmelCase__     :						Optional[int]						=							inputs['''input_ids''']
              UpperCAmelCase__     :						Union[str, Any]						=							inputs['''labels''']
              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()					)






      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          pass






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
                  UpperCAmelCase__     :						List[str]						=							self.rust_tokenizer_class.from_pretrained(UpperCamelCase_		,		**UpperCamelCase_					)
                  UpperCAmelCase__     :						Any						=							self.tokenizer_class.from_pretrained(UpperCamelCase_		,		**UpperCamelCase_					)
                  UpperCAmelCase__     :						List[str]						=							'''A, <mask> AllenNLP sentence.'''
                  UpperCAmelCase__     :						Optional[int]						=							tokenizer_r.encode_plus(UpperCamelCase_		,		add_special_tokens=UpperCamelCase_		,		return_token_type_ids=UpperCamelCase_					)
                  UpperCAmelCase__     :						Optional[int]						=							tokenizer_p.encode_plus(UpperCamelCase_		,		add_special_tokens=UpperCamelCase_		,		return_token_type_ids=UpperCamelCase_					)

                  # token_type_ids should put 0 everywhere
                  self.assertEqual(sum(tokens_r['''token_type_ids''']					)		,		sum(tokens_p['''token_type_ids''']					)					)

                  # attention_mask should put 1 everywhere, so sum over length should be 1
                  self.assertEqual(
                      sum(tokens_r['''attention_mask''']					) / len(tokens_r['''attention_mask''']					)		,		sum(tokens_p['''attention_mask''']					) / len(tokens_p['''attention_mask''']					)		,		)

                  UpperCAmelCase__     :						Union[str, Any]						=							tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']					)
                  UpperCAmelCase__     :						List[str]						=							tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']					)

                  # Rust correctly handles the space before the mask while python doesnt
                  self.assertSequenceEqual(tokens_p['''input_ids''']		,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]					)
                  self.assertSequenceEqual(tokens_r['''input_ids''']		,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]					)

                  self.assertSequenceEqual(
                      UpperCamelCase_		,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)
                  self.assertSequenceEqual(
                      UpperCamelCase_		,		['''<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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=None							)   ->List[str]:
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
	snake_case__	       =   nn.Parameter(UpperCAmelCase_							)
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
		snake_case__	       =   nn.Parameter(UpperCAmelCase_							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	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(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.value					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.output.dense					,				torch.tensor(UpperCAmelCase_							).view(-1					,				UpperCAmelCase_							).contiguous().transpose(0					,				1							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	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(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.key					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.value					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.output.dense					,				torch.tensor(UpperCAmelCase_							).view(-1					,				UpperCAmelCase_							).contiguous().transpose(0					,				1							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	# layernorm 1
	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(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

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

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

	# Chunked Feed Forward
	if len(UpperCAmelCase_							) == 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(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# 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(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)

	# 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(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	# reformer model
	snake_case__	       =   torch_model.reformer

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

	if isinstance(weights[3]					,				UpperCAmelCase_							):
		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(UpperCAmelCase_							)							)

	snake_case__	       =   weights[5]
	assert len(torch_model_reformer.encoder.layers							) * 4 == len(
	    UpperCAmelCase_							), "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(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

	# 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(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# 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(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# Initialise PyTorch model
	snake_case__	       =   ReformerConfig.from_json_file(UpperCAmelCase_							)
	print(f'''Building PyTorch model from configuration: {config}'''							)
	snake_case__	       =   ReformerModelWithLMHead(UpperCAmelCase_							)

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

	set_model_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				config.hidden_size							)

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


if __name__ == "__main__":
			a__					:			Optional[int]          =     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__					:			Dict          =     parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



