






import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__       =     logging.get_logger(__name__)

a__       =     {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

a__       =     {
    """vocab_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"""},
    """merges_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"""},
}

a__       =     {
    """ctrl""": 2_5_6,
}

a__       =     {
    """Pregnancy""": 1_6_8_6_2_9,
    """Christianity""": 7_6_7_5,
    """Explain""": 1_0_6_4_2_3,
    """Fitness""": 6_3_4_4_0,
    """Saving""": 6_3_1_6_3,
    """Ask""": 2_7_1_7_1,
    """Ass""": 9_5_9_8_5,
    """Joke""": 1_6_3_5_0_9,
    """Questions""": 4_5_6_2_2,
    """Thoughts""": 4_9_6_0_5,
    """Retail""": 5_2_3_4_2,
    """Feminism""": 1_6_4_3_3_8,
    """Writing""": 1_1_9_9_2,
    """Atheism""": 1_9_2_2_6_3,
    """Netflix""": 4_8_6_1_6,
    """Computing""": 3_9_6_3_9,
    """Opinion""": 4_3_2_1_3,
    """Alone""": 4_4_9_6_7,
    """Funny""": 5_8_9_1_7,
    """Gaming""": 4_0_3_5_8,
    """Human""": 4_0_8_8,
    """India""": 1_3_3_1,
    """Joker""": 7_7_1_3_8,
    """Diet""": 3_6_2_0_6,
    """Legal""": 1_1_8_5_9,
    """Norman""": 4_9_3_9,
    """Tip""": 7_2_6_8_9,
    """Weight""": 5_2_3_4_3,
    """Movies""": 4_6_2_7_3,
    """Running""": 2_3_4_2_5,
    """Science""": 2_0_9_0,
    """Horror""": 3_7_7_9_3,
    """Confession""": 6_0_5_7_2,
    """Finance""": 1_2_2_5_0,
    """Politics""": 1_6_3_6_0,
    """Scary""": 1_9_1_9_8_5,
    """Support""": 1_2_6_5_4,
    """Technologies""": 3_2_5_1_6,
    """Teenage""": 6_6_1_6_0,
    """Event""": 3_2_7_6_9,
    """Learned""": 6_7_4_6_0,
    """Notion""": 1_8_2_7_7_0,
    """Wikipedia""": 3_7_5_8_3,
    """Books""": 6_6_6_5,
    """Extract""": 7_6_0_5_0,
    """Confessions""": 1_0_2_7_0_1,
    """Conspiracy""": 7_5_9_3_2,
    """Links""": 6_3_6_7_4,
    """Narcissus""": 1_5_0_4_2_5,
    """Relationship""": 5_4_7_6_6,
    """Relationships""": 1_3_4_7_9_6,
    """Reviews""": 4_1_6_7_1,
    """News""": 4_2_5_6,
    """Translation""": 2_6_8_2_0,
    """multilingual""": 1_2_8_4_0_6,
}

def _UpperCAmelCase   (	a						:	Optional[Any] ):
							snake_case__	  =       set()
							snake_case__	  =       word[0]
							for char in word[1:]:
														pairs.add((prev_char, char) )
														snake_case__	  =       char

							snake_case__	  =       set(a )
							return pairs



class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  VOCAB_FILES_NAMES
				_lowercase				:   Optional[int]      =  PRETRAINED_VOCAB_FILES_MAP
				_lowercase				:   Optional[Any]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase				:   Dict      =  CONTROL_CODES



				def __init__(      self    :    Optional[int]			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Tuple="<unk>"			,		**UpperCamelCase__    :    int):






											'''simple docstring'''







											super().__init__(unk_token=UpperCamelCase__			,		**UpperCamelCase__)

											with open(UpperCamelCase__			,		encoding="""utf-8""") as vocab_handle:
																		snake_case__	  =       json.load(UpperCamelCase__)
											snake_case__	  =       {v: k for k, v in self.encoder.items()}
											with open(UpperCamelCase__			,		encoding="""utf-8""") as merges_handle:
																		snake_case__	  =       merges_handle.read().split("""\n""")[1:-1]
											snake_case__	  =       [tuple(merge.split()) for merge in merges]
											snake_case__	  =       dict(zip(UpperCamelCase__			,		range(len(UpperCamelCase__))))
											snake_case__	  =       {}



				@property
				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											return len(self.encoder)



				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											return dict(self.encoder			,		**self.added_tokens_encoder)



				def 					__magic_name__							(      self    :    Tuple			,		UpperCamelCase__    :    str):






											'''simple docstring'''







											if token in self.cache:
																		return self.cache[token]
											snake_case__	  =       tuple(UpperCamelCase__)
											snake_case__	  =       tuple(list(word[:-1]) + [word[-1] + """</w>"""])
											snake_case__	  =       get_pairs(UpperCamelCase__)

											if not pairs:
																		return token

											while True:
																		snake_case__	  =       min(UpperCamelCase__			,		key=lambda UpperCamelCase__: self.bpe_ranks.get(UpperCamelCase__			,		float("""inf""")))
																		if bigram not in self.bpe_ranks:
																									break
																		snake_case__  , snake_case__	  =       bigram
																		snake_case__	  =       []
																		snake_case__	  =       0
																		while i < len(UpperCamelCase__):
																									try:
																																snake_case__	  =       word.index(UpperCamelCase__			,		UpperCamelCase__)
																									except ValueError:
																																new_word.extend(word[i:])
																																break
																									else:
																																new_word.extend(word[i:j])
																																snake_case__	  =       j

																									if word[i] == first and i < len(UpperCamelCase__) - 1 and word[i + 1] == second:
																																new_word.append(first + second)
																																i += 2
																									else:
																																new_word.append(word[i])
																																i += 1
																		snake_case__	  =       tuple(UpperCamelCase__)
																		snake_case__	  =       new_word
																		if len(UpperCamelCase__) == 1:
																									break
																		else:
																									snake_case__	  =       get_pairs(UpperCamelCase__)
											snake_case__	  =       """@@ """.join(UpperCamelCase__)
											snake_case__	  =       word[:-4]
											snake_case__	  =       word
											return word



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






											'''simple docstring'''







											snake_case__	  =       []

											snake_case__	  =       re.findall(R"""\S+\n?"""			,		UpperCamelCase__)

											for token in words:
																		split_tokens.extend(list(self.bpe(UpperCamelCase__).split(""" """)))
											return split_tokens



				def 					__magic_name__							(      self    :    Dict			,		UpperCamelCase__    :    Any):






											'''simple docstring'''







											return self.encoder.get(UpperCamelCase__			,		self.encoder.get(self.unk_token))



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Any):






											'''simple docstring'''







											return self.decoder.get(UpperCamelCase__			,		self.unk_token)



				def 					__magic_name__							(      self    :    int			,		UpperCamelCase__    :    int):






											'''simple docstring'''







											snake_case__	  =       """ """.join(UpperCamelCase__).replace("""@@ """			,		"""""").strip()
											return out_string



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Optional[str] = None):






											'''simple docstring'''







											if not os.path.isdir(UpperCamelCase__):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
																		return
											snake_case__	  =       os.path.join(
											    UpperCamelCase__			,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
											snake_case__	  =       os.path.join(
											    UpperCamelCase__			,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""])

											with open(UpperCamelCase__			,		"""w"""			,		encoding="""utf-8""") as f:
																		f.write(json.dumps(self.encoder			,		indent=2			,		sort_keys=UpperCamelCase__			,		ensure_ascii=UpperCamelCase__) + """\n""")

											snake_case__	  =       0
											with open(UpperCamelCase__			,		"""w"""			,		encoding="""utf-8""") as writer:
																		writer.write("""#version: 0.2\n""")
																		for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,		key=lambda UpperCamelCase__: kv[1]):
																									if index != token_index:
																																logger.warning(
																																    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																																    """ Please check that the tokenizer is not corrupted!""")
																																snake_case__	  =       token_index
																									writer.write(""" """.join(UpperCamelCase__) + """\n""")
																									index += 1

											return vocab_file, merge_file

				# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
				#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
				#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
				#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
				#     return ''.join(tokens_generated_so_far)








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)
