





"""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        __UpperCamelCase				(						a__		,       unittest.TestCase						):
							lowerCamelCase   :					int      					=ProphetNetTokenizer
							lowerCamelCase   :					int      					=False






							def     __a   (   self      )	-> Optional[Any]:
								super().setUp()

								a      :		Union[str, Any]	=				[
								    "[UNK]",
								    "[CLS]",
								    "[SEP]",
								    "[PAD]",
								    "[MASK]",
								    "want",
								    "##want",
								    "##ed",
								    "wa",
								    "un",
								    "runn",
								    "##ing",
								    ",",
								    "low",
								    "lowest",
								]
								a      :		int	=				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     __a   (   self      ,					lowerCAmelCase__      )	-> Optional[int]:
								a      :		int	=				"UNwant\u00E9d,running"
								a      :		str	=				"unwanted, running"
								return input_text, output_text






							def     __a   (   self      )	-> Union[str, Any]:
								a      :		Any	=				self.tokenizer_class(self.vocab_file      )

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






							def     __a   (   self      )	-> Union[str, Any]:
								a      :		Any	=				BasicTokenizer()

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






							def     __a   (   self      )	-> str:
								a      :		str	=				BasicTokenizer(do_lower_case=lowerCAmelCase__      )

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






							def     __a   (   self      )	-> Optional[int]:
								a      :		Union[str, Any]	=				BasicTokenizer(do_lower_case=lowerCAmelCase__      ,					strip_accents=lowerCAmelCase__      )

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






							def     __a   (   self      )	-> Dict:
								a      :		List[Any]	=				BasicTokenizer(do_lower_case=lowerCAmelCase__      ,					strip_accents=lowerCAmelCase__      )

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






							def     __a   (   self      )	-> str:
								a      :		List[str]	=				BasicTokenizer(do_lower_case=lowerCAmelCase__      )

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






							def     __a   (   self      )	-> Tuple:
								a      :		Any	=				BasicTokenizer(do_lower_case=lowerCAmelCase__      )

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






							def     __a   (   self      )	-> Tuple:
								a      :		int	=				BasicTokenizer(do_lower_case=lowerCAmelCase__      ,					strip_accents=lowerCAmelCase__      )

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






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

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






							def     __a   (   self      )	-> Optional[Any]:
								a      :		Dict	=				BasicTokenizer(do_lower_case=lowerCAmelCase__      ,					never_split=["[UNK]"]      )

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






							def     __a   (   self      )	-> List[Any]:
								a      :		Tuple	=				["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

								a      :		Optional[Any]	=				{}
								for i, token in enumerate(lowerCAmelCase__      ):
									a      :		Dict	=				i
								a      :		List[str]	=				WordpieceTokenizer(vocab=lowerCAmelCase__      ,					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     __a   (   self      )	-> Union[str, Any]:
								a      :		List[Any]	=				self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased"      )

								a      :		Optional[int]	=				["A long paragraph for summarization.", "Another paragraph for summarization."]
								a      :		Any	=				[1037, 2146, 2_0423, 2005, 7680, 7849, 3989, 1012, 102]
								a      :		Optional[int]	=				tokenizer(lowerCAmelCase__      ,					padding=lowerCAmelCase__      ,					return_tensors="pt"      )
								self.assertIsInstance(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Union[str, Any]	=				list(batch.input_ids.numpy()[0]      )
								self.assertListEqual(lowerCAmelCase__      ,					lowerCAmelCase__      )

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






							def     __a   (   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     __a   (   self      )	-> Tuple:
								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     __a   (   self      )	-> List[str]:
								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     __a   (   self      )	-> List[str]:
								a      :		Any	=				self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased"      )

								a      :		Tuple	=				tokenizer.encode("sequence builders"      ,					add_special_tokens=lowerCAmelCase__      )
								a      :		Union[str, Any]	=				tokenizer.encode("multi-sequence build"      ,					add_special_tokens=lowerCAmelCase__      )

								a      :		int	=				tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__      )
								a      :		Optional[int]	=				tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__      ,					lowerCAmelCase__      )

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









"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]					) ->str:






	'''simple docstring'''






	a      :		Union[str, Any]	=				os.path.join(args.tf_model_dir    ,       "parameters.json"					)
	a      :		str	=				json.loads(open(_lowercase					).read()					)
	if not params:
		raise ValueError(
		    F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""					)
	if not args.output.endswith(".pt"					):
		a      :		str	=				args.output + ".pt"
	a      :		Dict	=				OrderedDict()
	with tf.device("/CPU:0"					):
		a      :		Optional[int]	=				tf.train.load_checkpoint(args.tf_model_dir					)
		a      :		Optional[Any]	=				reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			a      :		Dict	=				reader.get_tensor(_lowercase					).astype(np.floataa					)
			if key_name.endswith("/adam_m"					) or key_name.endswith("/adam_v"					):
				continue
			if key_name.startswith("pasts/"					):
				if key_name.startswith("pasts/mlp"					):
					a      :		Union[str, Any]	=				int(key_name[9]					)
				elif key_name.startswith("pasts/out"					):
					a      :		Optional[int]	=				8
				a      :		Dict	=				"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				a      :		int	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/moe"					):
				a      :		List[str]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/switch_gating/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[str]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/softmlp/kernel"					):
					a      :		Optional[int]	=				"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/wo/kernel"					) or key_name.endswith("/wi/kernel"					):
					a      :		Any	=				key_name[-9:-7]
					for i in range(16					):
						a      :		List[Any]	=				"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
						a      :		str	=				(
						    vnp[i].transpose([1, 0]					).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/mlp"					):
				a      :		Union[str, Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/p1/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.weight" % player
					a      :		Dict	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p1/bias"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.bias" % player
					a      :		List[Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/kernel"					):
					a      :		Union[str, Any]	=				"model.blocks.%d.feed_forward.mlp.wo.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/bias"					):
					a      :		Dict	=				"model.blocks.%d.feed_forward.mlp.wo.bias" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		str	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/ln"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.bias" % player
					a      :		Tuple	=				vnp.copy()  # same because it is one dimensional
					a      :		int	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.weight" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/att"					):
				a      :		Optional[Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/qkv/kernel"					):
					a      :		Union[str, Any]	=				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					a      :		List[str]	=				state[:, 0, :, :]
					a      :		Dict	=				state[:, 1, :, :]
					a      :		Union[str, Any]	=				state[:, 2, :, :]
					a      :		str	=				(
					    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[str]	=				(
					    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		Dict	=				(
					    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
					a      :		Union[str, Any]	=				torch.tensor(_lowercase					)
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
					a      :		List[str]	=				torch.tensor(_lowercase					)
					a      :		Optional[Any]	=				"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
					a      :		Optional[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/o/kernel"					):
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
					a      :		Optional[int]	=				(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]					).transpose([1, 0]					).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/an"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[int]	=				"model.blocks.%d.self_attn.norm.bias" % player
					a      :		Union[str, Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Any	=				"model.blocks.%d.self_attn.norm.weight" % player
					a      :		str	=				vnp.copy()  # same because it is one dimensional
					a      :		Any	=				torch.tensor(_lowercase					)
			elif (
			    key_name.startswith("model/wte"					)
			    or key_name.startswith("model/wpe"					)
			    or key_name.startswith("model/ete"					)
			):
				a      :		Optional[int]	=				{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
				    key_name[-3:]
				]
				a      :		Tuple	=				"model.%s.weight" % nlayer
				a      :		Any	=				vnp.copy()  # same in embedded
				a      :		Tuple	=				torch.tensor(_lowercase					)
				if key_name.startswith("model/wte"					):
					a      :		Optional[int]	=				"lm_head.weight"
					a      :		Optional[int]	=				vnp.copy()  # same in embedded
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/wob"					):
				a      :		Optional[int]	=				"final_logits_bias"
				a      :		Optional[Any]	=				vnp.copy()  # same in embedded
				a      :		Optional[int]	=				state.reshape((1, -1)					)
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense/kernel":
				a      :		Optional[int]	=				"model.last_project.weight"
				a      :		Union[str, Any]	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense_1/bias":
				a      :		Dict	=				"model.last_project.bias"
				a      :		Optional[Any]	=				vnp.copy()  # same because it is one dimensional
				a      :		Any	=				torch.tensor(_lowercase					)
	torch.save(_lowercase    ,       args.output					)


if __name__ == "__main__":
		a						: Optional[int]     	=			argparse.ArgumentParser(
		    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
		parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
		a						: Tuple     	=			parser.parse_args()
		convert_tf_gptsan_to_pt(args)


