"""simple docstring"""




import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	int			=    ProphetNetTokenizer
					UpperCamelCase_ :	Optional[int]			=    False




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							lowerCAmelCase__        =   [
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """[PAD]""",
							    """[MASK]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowerCAmelCase__        =   os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["""vocab_file"""]     )
							with open(self.vocab_file  ,						"""w"""  ,						encoding="""utf-8"""     ) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Any     ):
							lowerCAmelCase__        =   """UNwant\u00E9d,running"""
							lowerCAmelCase__        =   """unwanted, running"""
							return input_text, output_text




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   self.tokenizer_class(self.vocab_file     )

							lowerCAmelCase__        =   tokenizer.tokenize("""UNwant\u00E9d,running"""     )
							self.assertListEqual(__a  ,						["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]     )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__a     )  ,						[9, 6, 7, 12, 10, 11]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   BasicTokenizer()

							self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""     )  ,						["""ah""", """\u535A""", """\u63A8""", """zz"""]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   BasicTokenizer(do_lower_case=__a     )

							self.assertListEqual(
							    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """     )  ,						["""hello""", """!""", """how""", """are""", """you""", """?"""]     )
							self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )  ,						["""hello"""]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   BasicTokenizer(do_lower_case=__a  ,						strip_accents=__a     )

							self.assertListEqual(
							    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )  ,						["""hällo""", """!""", """how""", """are""", """you""", """?"""]     )
							self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )  ,						["""h\u00E9llo"""]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   BasicTokenizer(do_lower_case=__a  ,						strip_accents=__a     )

							self.assertListEqual(
							    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )  ,						["""hallo""", """!""", """how""", """are""", """you""", """?"""]     )
							self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )  ,						["""hello"""]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   BasicTokenizer(do_lower_case=__a     )

							self.assertListEqual(
							    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )  ,						["""hallo""", """!""", """how""", """are""", """you""", """?"""]     )
							self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )  ,						["""hello"""]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   BasicTokenizer(do_lower_case=__a     )

							self.assertListEqual(
							    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """     )  ,						["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   BasicTokenizer(do_lower_case=__a  ,						strip_accents=__a     )

							self.assertListEqual(
							    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )  ,						["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   BasicTokenizer(do_lower_case=__a  ,						strip_accents=__a     )

							self.assertListEqual(
							    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )  ,						["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   BasicTokenizer(do_lower_case=__a  ,						never_split=["""[UNK]"""]     )

							self.assertListEqual(
							    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""     )  ,						["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

							lowerCAmelCase__        =   {}
							for i, token in enumerate(__a     ):
									lowerCAmelCase__        =   i
							lowerCAmelCase__        =   WordpieceTokenizer(vocab=__a  ,						unk_token="""[UNK]"""     )

							self.assertListEqual(tokenizer.tokenize(""""""     )  ,						[]     )

							self.assertListEqual(tokenizer.tokenize("""unwanted running"""     )  ,						["""un""", """##want""", """##ed""", """runn""", """##ing"""]     )

							self.assertListEqual(tokenizer.tokenize("""unwantedX running"""     )  ,						["""[UNK]""", """runn""", """##ing"""]     )




					@require_torch
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""     )

							lowerCAmelCase__        =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							lowerCAmelCase__        =   [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
							lowerCAmelCase__        =   tokenizer(__a  ,						padding=__a  ,						return_tensors="""pt"""     )
							self.assertIsInstance(__a  ,						__a     )
							lowerCAmelCase__        =   list(batch.input_ids.numpy()[0]     )
							self.assertListEqual(__a  ,						__a     )

							self.assertEqual((2, 9)  ,						batch.input_ids.shape     )
							self.assertEqual((2, 9)  ,						batch.attention_mask.shape     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							self.assertTrue(_is_whitespace(""" """     )     )
							self.assertTrue(_is_whitespace("""\t"""     )     )
							self.assertTrue(_is_whitespace("""\r"""     )     )
							self.assertTrue(_is_whitespace("""\n"""     )     )
							self.assertTrue(_is_whitespace("""\u00A0"""     )     )

							self.assertFalse(_is_whitespace("""A"""     )     )
							self.assertFalse(_is_whitespace("""-"""     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							self.assertTrue(_is_control("""\u0005"""     )     )

							self.assertFalse(_is_control("""A"""     )     )
							self.assertFalse(_is_control(""" """     )     )
							self.assertFalse(_is_control("""\t"""     )     )
							self.assertFalse(_is_control("""\r"""     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							self.assertTrue(_is_punctuation("""-"""     )     )
							self.assertTrue(_is_punctuation("""$"""     )     )
							self.assertTrue(_is_punctuation("""`"""     )     )
							self.assertTrue(_is_punctuation("""."""     )     )

							self.assertFalse(_is_punctuation("""A"""     )     )
							self.assertFalse(_is_punctuation(""" """     )     )




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""     )

							lowerCAmelCase__        =   tokenizer.encode("""sequence builders"""  ,						add_special_tokens=__a     )
							lowerCAmelCase__        =   tokenizer.encode("""multi-sequence build"""  ,						add_special_tokens=__a     )

							lowerCAmelCase__        =   tokenizer.build_inputs_with_special_tokens(__a     )
							lowerCAmelCase__        =   tokenizer.build_inputs_with_special_tokens(__a  ,						__a     )

							assert encoded_sentence == text + [102]
							assert encoded_pair == text + [102] + text_a + [102]







"""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		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None     ):





		"""simple docstring"""




		assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
		lowerCAmelCase__        =   nn.Parameter(lowerCamelCase__     )
		if bias is not None:
				assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
				lowerCAmelCase__        =   nn.Parameter(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.asarray(weights[0]     )
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		lowerCAmelCase__        =   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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.asarray(weights[0]     )
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		lowerCAmelCase__        =   np.asarray(weights[2]     )
		lowerCAmelCase__        =   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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   weights[0][0][0]
		lowerCAmelCase__        =   np.asarray(layer_norm_a[0]     )
		lowerCAmelCase__        =   np.asarray(layer_norm_a[1]     )
		set_param(
		    torch_block.attention.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

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

		# Chunked Feed Forward
		if len(lowerCamelCase__     ) == 4:
				lowerCAmelCase__        =   intermediate_weights[2]

		# layernorm 2
		lowerCAmelCase__        =   np.asarray(intermediate_weights[0][0]     )
		lowerCAmelCase__        =   np.asarray(intermediate_weights[0][1]     )
		set_param(
		    torch_block.feed_forward.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

		# intermediate dense
		lowerCAmelCase__        =   np.asarray(intermediate_weights[1][0]     )
		lowerCAmelCase__        =   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
		lowerCAmelCase__        =   np.asarray(intermediate_weights[4][0]     )
		lowerCAmelCase__        =   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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   torch_model.reformer

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

		if isinstance(weights[3]			,  lowerCamelCase__     ):
				lowerCAmelCase__        =   torch_model_reformer.embeddings.position_embeddings
				for emb_idx in range(len(position_embeddings.weights     )     ):
						lowerCAmelCase__        =   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"""
						lowerCAmelCase__        =   nn.Parameter(torch.tensor(lowerCamelCase__     )     )

		lowerCAmelCase__        =   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     ):
				lowerCAmelCase__        =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
				set_block_weights_in_torch(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

		# output layer norm
		lowerCAmelCase__        =   np.asarray(weights[7][0]     )
		lowerCAmelCase__        =   np.asarray(weights[7][1]     )
		set_param(
		    torch_model_reformer.encoder.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

		# output embeddings
		lowerCAmelCase__        =   np.asarray(weights[9][0]     )
		lowerCAmelCase__        =   np.asarray(weights[9][1]     )
		set_param(
		    torch_model.lm_head.decoder			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   ReformerConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   ReformerModelWithLMHead(lowerCamelCase__     )

		with open(lowerCamelCase__			,  """rb"""     ) as f:
				lowerCAmelCase__        =   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__":
			__lowerCAmelCase    :							List[str]													=						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."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




