






import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a__       =     get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
class    _lowerCAmelCase			(		lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  XLMProphetNetTokenizer
				_lowercase				:   Optional[Any]      =  False
				_lowercase				:   Optional[Any]      =  True



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											super().setUp()

											# We have a SentencePiece fixture for testing
											snake_case__	  =       XLMProphetNetTokenizer(UpperCamelCase__			,		keep_accents=UpperCamelCase__)
											tokenizer.save_pretrained(self.tmpdirname)



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       """[PAD]"""
											snake_case__	  =       0

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



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											snake_case__	  =       list(self.get_tokenizer().get_vocab().keys())

											self.assertEqual(vocab_keys[0]			,		"""[PAD]""")
											self.assertEqual(vocab_keys[1]			,		"""[CLS]""")
											self.assertEqual(vocab_keys[-1]			,		"""j""")
											self.assertEqual(len(UpperCamelCase__)			,		1_0_1_2)



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											self.assertEqual(self.get_tokenizer().vocab_size			,		1_0_1_2)



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											snake_case__	  =       XLMProphetNetTokenizer(UpperCamelCase__			,		keep_accents=UpperCamelCase__)

											snake_case__	  =       tokenizer.tokenize("""This is a test""")
											self.assertListEqual(UpperCamelCase__			,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(UpperCamelCase__)			,		[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]			,		)

											snake_case__	  =       tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
											self.assertListEqual(
											    UpperCamelCase__			,		[
											        SPIECE_UNDERLINE + """I""",
											        SPIECE_UNDERLINE + """was""",
											        SPIECE_UNDERLINE + """b""",
											        """or""",
											        """n""",
											        SPIECE_UNDERLINE + """in""",
											        SPIECE_UNDERLINE + """""",
											        """9""",
											        """2""",
											        """0""",
											        """0""",
											        """0""",
											        """,""",
											        SPIECE_UNDERLINE + """and""",
											        SPIECE_UNDERLINE + """this""",
											        SPIECE_UNDERLINE + """is""",
											        SPIECE_UNDERLINE + """f""",
											        """al""",
											        """s""",
											        """é""",
											        """.""",
											    ]			,		)
											snake_case__	  =       tokenizer.convert_tokens_to_ids(UpperCamelCase__)
											self.assertListEqual(
											    UpperCamelCase__			,		[
											        value + tokenizer.fairseq_offset
											        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
											    ]			,		)

											snake_case__	  =       tokenizer.convert_ids_to_tokens(UpperCamelCase__)
											self.assertListEqual(
											    UpperCamelCase__			,		[
											        SPIECE_UNDERLINE + """I""",
											        SPIECE_UNDERLINE + """was""",
											        SPIECE_UNDERLINE + """b""",
											        """or""",
											        """n""",
											        SPIECE_UNDERLINE + """in""",
											        SPIECE_UNDERLINE + """""",
											        """[UNK]""",
											        """2""",
											        """0""",
											        """0""",
											        """0""",
											        """,""",
											        SPIECE_UNDERLINE + """and""",
											        SPIECE_UNDERLINE + """this""",
											        SPIECE_UNDERLINE + """is""",
											        SPIECE_UNDERLINE + """f""",
											        """al""",
											        """s""",
											        """[UNK]""",
											        """.""",
											    ]			,		)



				@cached_property
				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







											return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""")



				@slow
				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       """Hello World!"""
											snake_case__	  =       [3_5_3_8_9, 6_6_7_2, 4_9, 2]
											self.assertListEqual(UpperCamelCase__			,		self.big_tokenizer.encode(UpperCamelCase__))



				@slow
				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       {"""input_ids""": [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 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], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 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, 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, 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, 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, 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], [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, 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]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=UpperCamelCase__			,		model_name="""microsoft/xprophetnet-large-wiki100-cased"""			,		revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""			,		)








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						:	Optional[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any]=None ):
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
							snake_case__	  =       nn.Parameter(a )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
														snake_case__	  =       nn.Parameter(a )

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	List[str]  ,					a						:	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(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						:	Optional[int]  ,					a						:	Dict  ,					a						:	Optional[int] ):
							# 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(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						:	List[Any]  ,					a						:	Tuple  ,					a						:	List[Any] ):
							# 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(a )  ,					torch.tensor(a )  ,					)

							# lsh weights + output
							snake_case__	  =       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
							snake_case__	  =       weights[2][0][1][2]

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

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

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

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Dict  ,					a						:	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(a )  ,					)

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

							snake_case__	  =       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 ):
														snake_case__	  =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(a  ,					a  ,					a )

							# 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(a )  ,					torch.tensor(a )  ,					)

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

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str  ,					a						:	str ):
							# Initialise PyTorch model
							snake_case__	  =       ReformerConfig.from_json_file(a )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case__	  =       ReformerModelWithLMHead(a )

							with open(a  ,					"""rb""" ) as f:
														snake_case__	  =       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)
