



import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Optional[Any]     		=CanineTokenizer
				UpperCamelCase__				:					int     		=False




				def 							A__      (						self					:						Any						):
							super().setUp()
							lowercase__					  =				CanineTokenizer()
							tokenizer.save_pretrained(self.tmpdirname						)




				@cached_property
				def 							A__      (						self					:						Dict						):
							return CanineTokenizer.from_pretrained("google/canine-s"						)




				def 							A__      (						self					:						int,					**__lowercase					:						int						):
							lowercase__					  =				self.tokenizer_class.from_pretrained(self.tmpdirname,					**__lowercase						)
							lowercase__					  =				1024
							return tokenizer




				@require_torch
				def 							A__      (						self					:						Dict						):
							lowercase__					  =				self.canine_tokenizer
							lowercase__					  =				["Life is like a box of chocolates.", "You never know what you're gonna get."]
							# fmt: off
							lowercase__					  =				[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
							# fmt: on
							lowercase__					  =				tokenizer(__lowercase,					padding=__lowercase,					return_tensors="pt"						)
							self.assertIsInstance(__lowercase,					__lowercase						)

							lowercase__					  =				list(batch.input_ids.numpy()[0]						)

							self.assertListEqual(__lowercase,					__lowercase						)

							self.assertEqual((2, 39),					batch.input_ids.shape						)
							self.assertEqual((2, 39),					batch.attention_mask.shape						)




				@require_torch
				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				self.canine_tokenizer
							lowercase__					  =				["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
							lowercase__					  =				tokenizer(__lowercase,					padding=__lowercase,					return_tensors="pt"						)
							# check if input_ids, attention_mask and token_type_ids are returned
							self.assertIn("input_ids",					__lowercase						)
							self.assertIn("attention_mask",					__lowercase						)
							self.assertIn("token_type_ids",					__lowercase						)




				@require_torch
				def 							A__      (						self					:						List[Any]						):
							lowercase__					  =				self.canine_tokenizer
							lowercase__					  =				[
							    "What's the weater?",
							    "It's about 25 degrees.",
							]
							lowercase__					  =				tokenizer(
							    text_target=__lowercase,					max_length=32,					padding="max_length",					truncation=__lowercase,					return_tensors="pt"						)
							self.assertEqual(32,					targets["input_ids"].shape[1]						)




				def 							A__      (						self					:						List[Any]						):
							# safety check on max_len default value so we are sure the test works
							lowercase__					  =				self.get_tokenizers()
							for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
													self.assertNotEqual(tokenizer.model_max_length,					42						)

        # Now let's start the test
							lowercase__					  =				self.get_tokenizers()
							for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
													# Isolate this from the other tests because we save additional tokens/etc
													lowercase__					  =				tempfile.mkdtemp()

													lowercase__					  =				" He is very happy, UNwant\u00E9d,running"
													lowercase__					  =				tokenizer.encode(__lowercase,					add_special_tokens=__lowercase						)
													tokenizer.save_pretrained(__lowercase						)

													lowercase__					  =				tokenizer.__class__.from_pretrained(__lowercase						)
													lowercase__					  =				after_tokenizer.encode(__lowercase,					add_special_tokens=__lowercase						)
													self.assertListEqual(__lowercase,					__lowercase						)

													shutil.rmtree(__lowercase						)

							lowercase__					  =				self.get_tokenizers(model_max_length=42						)
							for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
													# Isolate this from the other tests because we save additional tokens/etc
													lowercase__					  =				tempfile.mkdtemp()

													lowercase__					  =				" He is very happy, UNwant\u00E9d,running"

													lowercase__					  =				tokenizer.additional_special_tokens

													# We can add a new special token for Canine as follows:
													lowercase__					  =				chr(0xe_0_0_7						)
													additional_special_tokens.append(__lowercase						)
													tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}						)
													lowercase__					  =				tokenizer.encode(__lowercase,					add_special_tokens=__lowercase						)
													tokenizer.save_pretrained(__lowercase						)

													lowercase__					  =				tokenizer.__class__.from_pretrained(__lowercase						)
													lowercase__					  =				after_tokenizer.encode(__lowercase,					add_special_tokens=__lowercase						)
													self.assertListEqual(__lowercase,					__lowercase						)
													self.assertIn(__lowercase,					after_tokenizer.additional_special_tokens						)
													self.assertEqual(after_tokenizer.model_max_length,					42						)

													lowercase__					  =				tokenizer.__class__.from_pretrained(__lowercase,					model_max_length=43						)
													self.assertEqual(tokenizer.model_max_length,					43						)

													shutil.rmtree(__lowercase						)




				def 							A__      (						self					:						Dict						):
							lowercase__					  =				self.get_tokenizers(do_lower_case=__lowercase						)
							for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
													lowercase__  ,       lowercase__					  =				self.get_clean_sequence(__lowercase						)

													# a special token for Canine can be defined as follows:
													lowercase__					  =				0xe_0_0_5
													lowercase__					  =				chr(__lowercase						)

													tokenizer.add_special_tokens({"cls_token": special_token}						)
													lowercase__					  =				tokenizer.encode(__lowercase,					add_special_tokens=__lowercase						)
													self.assertEqual(len(__lowercase						),					1						)

													lowercase__					  =				tokenizer.decode(ids + encoded_special_token,					clean_up_tokenization_spaces=__lowercase						)
													lowercase__					  =				tokenizer.encode(__lowercase,					add_special_tokens=__lowercase						)

													lowercase__					  =				tokenizer.encode(__lowercase,					add_special_tokens=__lowercase						)
													lowercase__					  =				tokenizer.encode(__lowercase,					add_special_tokens=__lowercase						)
													self.assertEqual(__lowercase,					input_encoded + special_token_id						)

													lowercase__					  =				tokenizer.decode(__lowercase,					skip_special_tokens=__lowercase						)
													self.assertTrue(special_token not in decoded						)




				def 							A__      (						self					:						int						):
							lowercase__					  =				self.get_tokenizers(do_lower_case=__lowercase						)
							for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
													lowercase__					  =				chr(0xe_0_0_5						)
													lowercase__					  =				chr(0xe_0_0_6						)

													# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
													tokenizer.add_tokens([SPECIAL_TOKEN_1],					special_tokens=__lowercase						)
													# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
													# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
													tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]}						)

													lowercase__					  =				tokenizer.tokenize(__lowercase						)
													lowercase__					  =				tokenizer.tokenize(__lowercase						)

													self.assertEqual(len(__lowercase						),					1						)
													self.assertEqual(len(__lowercase						),					1						)
													self.assertEqual(token_a[0],					__lowercase						)
													self.assertEqual(token_a[0],					__lowercase						)




				@require_tokenizers
				def 							A__      (						self					:						Dict						):
							lowercase__					  =				self.get_tokenizers(do_lower_case=__lowercase						)
							for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
													# a special token for Canine can be defined as follows:
													lowercase__					  =				0xe_0_0_6
													lowercase__					  =				chr(__lowercase						)

													lowercase__					  =				AddedToken(__lowercase,					lstrip=__lowercase						)
													tokenizer.add_special_tokens({"additional_special_tokens": [new_token]}						)

													with tempfile.TemporaryDirectory() as tmp_dir_name:
																tokenizer.save_pretrained(__lowercase						)
																tokenizer.from_pretrained(__lowercase						)




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				[]
							if self.test_slow_tokenizer:
										tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

							if self.test_rust_tokenizer:
										tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

							for tokenizer_class, tokenizer_utils in tokenizer_list:
										with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer_utils.save_pretrained(__lowercase						)

													with open(os.path.join(__lowercase,					"special_tokens_map.json"						),					encoding="utf-8"						) as json_file:
																lowercase__					  =				json.load(__lowercase						)

													with open(os.path.join(__lowercase,					"tokenizer_config.json"						),					encoding="utf-8"						) as json_file:
																lowercase__					  =				json.load(__lowercase						)

													# a special token for Canine can be defined as follows:
													lowercase__					  =				0xe_0_0_6
													lowercase__					  =				chr(__lowercase						)

													lowercase__					  =				[new_token_a]
													lowercase__					  =				[new_token_a]

													with open(os.path.join(__lowercase,					"special_tokens_map.json"						),					"w",					encoding="utf-8"						) as outfile:
																json.dump(__lowercase,					__lowercase						)
													with open(os.path.join(__lowercase,					"tokenizer_config.json"						),					"w",					encoding="utf-8"						) as outfile:
																json.dump(__lowercase,					__lowercase						)

													# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
													# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
													# "special_tokens_map.json" files
													lowercase__					  =				tokenizer_class.from_pretrained(__lowercase,					extra_ids=0						)
													self.assertIn(__lowercase,					tokenizer_without_change_in_init.additional_special_tokens						)
													# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
													self.assertEqual(
													    [new_token_a],					tokenizer_without_change_in_init.convert_ids_to_tokens(
													        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]						)						),					)

													lowercase__					  =				0xe_0_0_7
													lowercase__					  =				chr(__lowercase						)
													# Now we test that we can change the value of additional_special_tokens in the from_pretrained
													lowercase__					  =				[AddedToken(__lowercase,					lstrip=__lowercase						)]
													lowercase__					  =				tokenizer_class.from_pretrained(
													    __lowercase,					additional_special_tokens=__lowercase,					extra_ids=0						)

													self.assertIn(__lowercase,					tokenizer.additional_special_tokens						)
													# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
													self.assertEqual(
													    [new_token_a],					tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]						)						)						)




				@require_tokenizers
				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				self.get_tokenizers(do_lower_case=__lowercase						)
							for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
													lowercase__					  =				"hello world"
													if self.space_between_special_tokens:
																lowercase__					  =				"[CLS] hello world [SEP]"
													else:
																lowercase__					  =				input
													lowercase__					  =				tokenizer.encode(__lowercase,					add_special_tokens=__lowercase						)
													lowercase__					  =				tokenizer.decode(__lowercase,					spaces_between_special_tokens=self.space_between_special_tokens						)
													self.assertIn(__lowercase,					[output, output.lower()]						)




				def 							A__      (						self					:						List[Any]						):
							lowercase__					  =				self.get_tokenizers()
							for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
													lowercase__					  =				[
													    "bos_token",
													    "eos_token",
													    "unk_token",
													    "sep_token",
													    "pad_token",
													    "cls_token",
													    "mask_token",
													]

							lowercase__					  =				"a"
							lowercase__					  =				ord(__lowercase						)

							for attr in attributes_list:
										setattr(__lowercase,					attr + "_id",					__lowercase						)
										self.assertEqual(getattr(__lowercase,					__lowercase						),					__lowercase						)
										self.assertEqual(getattr(__lowercase,					attr + "_id"						),					__lowercase						)

										setattr(__lowercase,					attr + "_id",					__lowercase						)
										self.assertEqual(getattr(__lowercase,					__lowercase						),					__lowercase						)
										self.assertEqual(getattr(__lowercase,					attr + "_id"						),					__lowercase						)

							setattr(__lowercase,					"additional_special_tokens_ids",					[]						)
							self.assertListEqual(getattr(__lowercase,					"additional_special_tokens"						),					[]						)
							self.assertListEqual(getattr(__lowercase,					"additional_special_tokens_ids"						),					[]						)

							lowercase__					  =				0xe_0_0_6
							lowercase__					  =				chr(__lowercase						)
							setattr(__lowercase,					"additional_special_tokens_ids",					[additional_special_token_id]						)
							self.assertListEqual(getattr(__lowercase,					"additional_special_tokens"						),					[additional_special_token]						)
							self.assertListEqual(getattr(__lowercase,					"additional_special_tokens_ids"						),					[additional_special_token_id]						)




				def 							A__      (						self					:						Dict						):
							pass




				def 							A__      (						self					:						Tuple						):
							pass




				def 							A__      (						self					:						Tuple						):
							pass




				def 							A__      (						self					:						List[Any]						):
							pass




				def 							A__      (						self					:						Dict						):
							pass




				def 							A__      (						self					:						Union[str, Any]						):
							pass




				def 							A__      (						self					:						List[str]						):
							pass




				def 							A__      (						self					:						Tuple						):
							pass









import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

			lowercase__					  =				(
			    ("layer.", "layer_"),
			    ("word_embeddings.weight", "word_embeddings"),
			    ("position_embeddings.weight", "position_embeddings"),
			    ("token_type_embeddings.weight", "token_type_embeddings"),
			    (".", "/"),
			    ("LayerNorm/weight", "LayerNorm/gamma"),
			    ("LayerNorm/bias", "LayerNorm/beta"),
			    ("weight", "kernel"),
			)

			if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
						os.makedirs(SCREAMING_SNAKE_CASE_  )

			lowercase__					  =				model.state_dict()

			def to_tf_var_name(SCREAMING_SNAKE_CASE_  ):
						for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
									lowercase__					  =				name.replace(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
						return f'''bert/{name}'''

			def create_tf_var(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
						lowercase__					  =				tf.dtypes.as_dtype(tensor.dtype  )
						lowercase__					  =				tf.get_variable(dtype=SCREAMING_SNAKE_CASE_						,  shape=tensor.shape						,  name=SCREAMING_SNAKE_CASE_						,  initializer=tf.zeros_initializer()  )
						session.run(tf.variables_initializer([tf_var]  )  )
						session.run(SCREAMING_SNAKE_CASE_  )
						return tf_var

			tf.reset_default_graph()
			with tf.Session() as session:
						for var_name in state_dict:
									lowercase__					  =				to_tf_var_name(SCREAMING_SNAKE_CASE_  )
									lowercase__					  =				state_dict[var_name].numpy()
									if any(x in var_name for x in tensors_to_transpose  ):
												lowercase__					  =				torch_tensor.T
									lowercase__					  =				create_tf_var(tensor=SCREAMING_SNAKE_CASE_						,  name=SCREAMING_SNAKE_CASE_						,  session=SCREAMING_SNAKE_CASE_  )
									tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
									lowercase__					  =				session.run(SCREAMING_SNAKE_CASE_  )
									print(f'''Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )}'''  )

						lowercase__					  =				tf.train.Saver(tf.trainable_variables()  )
						saver.save(SCREAMING_SNAKE_CASE_						,  os.path.join(SCREAMING_SNAKE_CASE_						,  model_name.replace("-"						,  "_"  ) + ".ckpt"  )  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_=None  ):
			lowercase__					  =				argparse.ArgumentParser()
			parser.add_argument("--model_name"						,  type=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="model name e.g. bert-base-uncased"  )
			parser.add_argument(
			    "--cache_dir"						,  type=SCREAMING_SNAKE_CASE_						,  default=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="Directory containing pytorch model"  )
			parser.add_argument("--pytorch_model_path"						,  type=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="/path/to/<pytorch-model-name>.bin"  )
			parser.add_argument("--tf_cache_dir"						,  type=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="Directory in which to save tensorflow model"  )
			lowercase__					  =				parser.parse_args(SCREAMING_SNAKE_CASE_  )

			lowercase__					  =				BertModel.from_pretrained(
			    pretrained_model_name_or_path=args.model_name						,  state_dict=torch.load(args.pytorch_model_path  )						,  cache_dir=args.cache_dir						,  )

			convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_						,  ckpt_dir=args.tf_cache_dir						,  model_name=args.model_name  )


if __name__ == "__main__":
							main()




