





"""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      __A   (      a_    :Optional[int] ,							a_    :str ,							a_    :Union[str, Any]=None)		->		Union[str, Any]:
				# set parameter of one layer
				assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
				__a			:    Optional[Any]      							=	nn.Parameter(lowerCAmelCase_)
				if bias is not None:
								assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
								__a			:    List[Any]      							=	nn.Parameter(lowerCAmelCase_)

def      __A   (      a_    :Any ,							a_    :Optional[int] ,							a_    :Union[str, Any])		->		Any:
				# set torch weights for 1-to-1 comparison
				__a			:    Optional[Any]      							=	np.asarray(weights[0])
				__a			:    Any      							=	np.asarray(weights[1])
				__a			:    Optional[int]      							=	np.asarray(weights[2])

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

def      __A   (      a_    :Dict ,							a_    :str ,							a_    :Optional[Any])		->		Optional[Any]:
				# set torch weights for 1-to-1 comparison
				__a			:    List[Any]      							=	np.asarray(weights[0])
				__a			:    List[str]      							=	np.asarray(weights[1])
				__a			:    List[Any]      							=	np.asarray(weights[2])
				__a			:    int      							=	np.asarray(weights[3])

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

def      __A   (      a_    :str ,							a_    :Dict ,							a_    :Tuple)		->		List[str]:
				# layernorm 1
				__a			:    str      							=	weights[0][0][0]
				__a			:    int      							=	np.asarray(layer_norm_a[0])
				__a			:    Tuple      							=	np.asarray(layer_norm_a[1])
				set_param(
				    torch_block.attention.layer_norm ,							torch.tensor(lowerCAmelCase_) ,							torch.tensor(lowerCAmelCase_) ,							)

				# lsh weights + output
				__a			:    Union[str, Any]      							=	weights[0][1]
				if len(lowerCAmelCase_) < 4:
								set_layer_weights_in_torch_lsh(lowerCAmelCase_ ,							torch_block.attention ,							lowerCAmelCase_)
				else:
								set_layer_weights_in_torch_local(lowerCAmelCase_ ,							torch_block.attention ,							lowerCAmelCase_)

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

				# Chunked Feed Forward
				if len(lowerCAmelCase_) == 4:
								__a			:    Dict      							=	intermediate_weights[2]

				# layernorm 2
				__a			:    Dict      							=	np.asarray(intermediate_weights[0][0])
				__a			:    Union[str, Any]      							=	np.asarray(intermediate_weights[0][1])
				set_param(
				    torch_block.feed_forward.layer_norm ,							torch.tensor(lowerCAmelCase_) ,							torch.tensor(lowerCAmelCase_) ,							)

				# intermediate dense
				__a			:    List[str]      							=	np.asarray(intermediate_weights[1][0])
				__a			:    Tuple      							=	np.asarray(intermediate_weights[1][1])
				set_param(
				    torch_block.feed_forward.dense.dense ,							torch.tensor(lowerCAmelCase_).transpose(0 ,							1).contiguous() ,							torch.tensor(lowerCAmelCase_) ,							)

				# intermediate out
				__a			:    int      							=	np.asarray(intermediate_weights[4][0])
				__a			:    Optional[Any]      							=	np.asarray(intermediate_weights[4][1])
				set_param(
				    torch_block.feed_forward.output.dense ,							torch.tensor(lowerCAmelCase_).transpose(0 ,							1).contiguous() ,							torch.tensor(lowerCAmelCase_) ,							)

def      __A   (      a_    :int ,							a_    :Any ,							a_    :Any)		->		Dict:
				# reformer model
				__a			:    List[Any]      							=	torch_model.reformer

				# word embeds
				__a			:    Dict      							=	np.asarray(weights[1])
				set_param(
				    torch_model_reformer.embeddings.word_embeddings ,							torch.tensor(lowerCAmelCase_) ,							)

				if isinstance(weights[3] ,							lowerCAmelCase_):
								__a			:    List[str]      							=	torch_model_reformer.embeddings.position_embeddings
								for emb_idx in range(len(position_embeddings.weights)):
												__a			:    Optional[int]      							=	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"""
												__a			:    Optional[int]      							=	nn.Parameter(torch.tensor(lowerCAmelCase_))

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

				# output layer norm
				__a			:    str      							=	np.asarray(weights[7][0])
				__a			:    Tuple      							=	np.asarray(weights[7][1])
				set_param(
				    torch_model_reformer.encoder.layer_norm ,							torch.tensor(lowerCAmelCase_) ,							torch.tensor(lowerCAmelCase_) ,							)

				# output embeddings
				__a			:    List[Any]      							=	np.asarray(weights[9][0])
				__a			:    int      							=	np.asarray(weights[9][1])
				set_param(
				    torch_model.lm_head.decoder ,							torch.tensor(lowerCAmelCase_).transpose(0 ,							1).contiguous() ,							torch.tensor(lowerCAmelCase_) ,							)

def      __A   (      a_    :Tuple ,							a_    :int ,							a_    :int)		->		str:
				# Initialise PyTorch model
				__a			:    Any      							=	ReformerConfig.from_json_file(lowerCAmelCase_)
				print(F"""Building PyTorch model from configuration: {config}""")
				__a			:    Tuple      							=	ReformerModelWithLMHead(lowerCAmelCase_)

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

				set_model_weights_in_torch(lowerCAmelCase_ ,							lowerCAmelCase_ ,							config.hidden_size)

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


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)






import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	BioGptTokenizer
    lowerCamelCase__    :		List[Any]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE__											=			[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']     )
        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file''']     )
        with open(self.vocab_file   ,				'''w'''     ) as fp:
            fp.write(json.dumps(A_     )     )
        with open(self.merges_file   ,				'''w'''     ) as fp:
            fp.write('''\n'''.join(A_     )     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        return input_text, output_text



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer(self.vocab_file   ,				self.merges_file     )

        SCREAMING_SNAKE_CASE__											=			'''lower'''
        SCREAMING_SNAKE_CASE__											=			['''low''', '''er</w>''']
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + ['''<unk>''']
        SCREAMING_SNAKE_CASE__											=			[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''sequence builders'''   ,				add_special_tokens=A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''multi-sequence build'''   ,				add_special_tokens=A_     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_   ,				A_     )

        self.assertTrue(encoded_sentence == [2] + text     )
        self.assertTrue(encoded_pair == [2] + text + [2] + text_a     )
