






"""simple docstring"""

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
			lowercase    			=       '''pt'''
elif is_tf_available():
			lowercase    			=       '''tf'''
else:
			lowercase    			=       '''jax'''

class 						A_							(      snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				ByTaTokenizer
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						Tuple	)				->					List[str]:
										super().setUp()
										__magic_name__            =     ByTaTokenizer()
										tokenizer.save_pretrained(self.tmpdirname	)







				@cached_property
				def     _snake_case					(							self							:						int	)				->					Any:
										return ByTaTokenizer.from_pretrained("google/byt5-small"	)







				def     _snake_case					(							self							:						Tuple						,    **__lowerCamelCase							:						int	)				->					ByTaTokenizer:
										return self.tokenizer_class.from_pretrained(self.tmpdirname						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Optional[int]=False						,    __lowerCamelCase							:						int=2_0						,    __lowerCamelCase							:						Optional[Any]=5	)				->					Tuple[str, list]:
										# XXX The default common tokenizer tests assume that every ID is decodable on its own.
										# This assumption is invalid for ByT5 because single bytes might not be
										# valid utf-8 (byte 128 for instance).
										# Here we're overriding the smallest possible method to provide
										# a clean sequence without making the same assumption.

										__magic_name__            =     []
										for i in range(len(__lowerCamelCase	)	):
																try:
																						__magic_name__            =     tokenizer.decode([i]						,    clean_up_tokenization_spaces=__lowerCamelCase	)
																except UnicodeDecodeError:
																						pass
																toks.append((i, tok)	)

										__magic_name__            =     list(filter(lambda __lowerCamelCase	: re.match(R"^[ a-zA-Z]+$"						,    t[1]	)						,    __lowerCamelCase	)	)
										__magic_name__            =     list(filter(lambda __lowerCamelCase	: [t[0]] == tokenizer.encode(t[1]						,    add_special_tokens=__lowerCamelCase	)						,    __lowerCamelCase	)	)
										if max_length is not None and len(__lowerCamelCase	) > max_length:
																__magic_name__            =     toks[:max_length]
										if min_length is not None and len(__lowerCamelCase	) < min_length and len(__lowerCamelCase	) > 0:
																while len(__lowerCamelCase	) < min_length:
																						__magic_name__            =     toks + toks
        # toks_str = [t[1] for t in toks]
										__magic_name__            =     [t[0] for t in toks]

										# Ensure consistency
										__magic_name__            =     tokenizer.decode(__lowerCamelCase						,    clean_up_tokenization_spaces=__lowerCamelCase	)
										if " " not in output_txt and len(__lowerCamelCase	) > 1:
																__magic_name__            =     (
																    tokenizer.decode([toks_ids[0]]						,    clean_up_tokenization_spaces=__lowerCamelCase	)
																    + " "
																    + tokenizer.decode(toks_ids[1:]						,    clean_up_tokenization_spaces=__lowerCamelCase	)
																)
										if with_prefix_space:
																__magic_name__            =     " " + output_txt
										__magic_name__            =     tokenizer.encode(__lowerCamelCase						,    add_special_tokens=__lowerCamelCase	)
										return output_txt, output_ids







				def     _snake_case					(							self							:						List[str]	)				->					Optional[int]:
										__magic_name__            =     self.ta_base_tokenizer
										__magic_name__            =     tokenizer(["hi</s>", "I went to the gym</s>", "</s>"]	)
										__magic_name__            =     tokenizer(["hi", "I went to the gym", ""]	)
										self.assertListEqual(batch_with_eos_added["input_ids"]						,    batch_without_eos_added["input_ids"]	)







				def     _snake_case					(							self							:						List[str]	)				->					Union[str, Any]:
										__magic_name__            =     self.ta_base_tokenizer
										__magic_name__            =     "Unicode €."
										__magic_name__            =     tokenizer(__lowerCamelCase	)
										__magic_name__            =     [8_8, 1_1_3, 1_0_8, 1_0_2, 1_1_4, 1_0_3, 1_0_4, 3_5, 2_2_9, 1_3_3, 1_7_5, 4_9, 1]
										self.assertEqual(encoded["input_ids"]						,    __lowerCamelCase	)

										# decoding
										__magic_name__            =     tokenizer.decode(__lowerCamelCase	)
										self.assertEqual(__lowerCamelCase						,    "Unicode €.</s>"	)

										__magic_name__            =     tokenizer("e è é ê ë"	)
										__magic_name__            =     [1_0_4, 3_5, 1_9_8, 1_7_1, 3_5, 1_9_8, 1_7_2, 3_5, 1_9_8, 1_7_3, 3_5, 1_9_8, 1_7_4, 1]
										self.assertEqual(encoded["input_ids"]						,    __lowerCamelCase	)
										# decoding
										__magic_name__            =     tokenizer.decode(__lowerCamelCase	)
										self.assertEqual(__lowerCamelCase						,    "e è é ê ë</s>"	)

										# encode/decode, but with `encode` instead of `__call__`
										self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"	)	)						,    "e è é ê ë</s>"	)







				def     _snake_case					(							self							:						str	)				->					Tuple:
										__magic_name__            =     self.ta_base_tokenizer
										__magic_name__            =     ["A long paragraph for summarization.", "Another paragraph for summarization."]
										# fmt: off
										__magic_name__            =     [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 1, 0]
										# fmt: on
										__magic_name__            =     tokenizer(__lowerCamelCase						,    padding=__lowerCamelCase						,    return_tensors=__lowerCamelCase	)
										self.assertIsInstance(__lowerCamelCase						,    __lowerCamelCase	)

										if FRAMEWORK != "jax":
																__magic_name__            =     list(batch.input_ids.numpy()[0]	)
										else:
																__magic_name__            =     list(batch.input_ids.tolist()[0]	)

										self.assertListEqual(__lowerCamelCase						,    __lowerCamelCase	)

										self.assertEqual((2, 3_7)						,    batch.input_ids.shape	)
										self.assertEqual((2, 3_7)						,    batch.attention_mask.shape	)







				def     _snake_case					(							self							:						List[str]	)				->					Union[str, Any]:
										__magic_name__            =     self.ta_base_tokenizer
										__magic_name__            =     ["A long paragraph for summarization.", "Another paragraph for summarization."]
										__magic_name__            =     tokenizer(__lowerCamelCase						,    padding=__lowerCamelCase						,    return_tensors=__lowerCamelCase	)
										# check if input_ids are returned and no decoder_input_ids
										self.assertIn("input_ids"						,    __lowerCamelCase	)
										self.assertIn("attention_mask"						,    __lowerCamelCase	)
										self.assertNotIn("decoder_input_ids"						,    __lowerCamelCase	)
										self.assertNotIn("decoder_attention_mask"						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[Any]:
										__magic_name__            =     self.ta_base_tokenizer
										__magic_name__            =     [
										    "Summary of the text.",
										    "Another summary.",
										]
										__magic_name__            =     tokenizer(
										    text_target=__lowerCamelCase						,    max_length=3_2						,    padding="max_length"						,    truncation=__lowerCamelCase						,    return_tensors=__lowerCamelCase	)
										self.assertEqual(3_2						,    targets["input_ids"].shape[1]	)







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										__magic_name__            =     self.ta_base_tokenizer
										__magic_name__            =     ["A long paragraph for summarization. </s>"]
										__magic_name__            =     ["Summary of the text. </s>"]
										# fmt: off
										__magic_name__            =     [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 3_5, 1]
										__magic_name__            =     [8_6, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_2_4, 3_5, 1_1_4, 1_0_5, 3_5, 1_1_9, 1_0_7, 1_0_4, 3_5, 1_1_9, 1_0_4, 1_2_3, 1_1_9, 4_9, 3_5, 1]
										# fmt: on

										__magic_name__            =     tokenizer(__lowerCamelCase						,    text_target=__lowerCamelCase	)

										self.assertEqual(__lowerCamelCase						,    batch["input_ids"][0]	)
										self.assertEqual(__lowerCamelCase						,    batch["labels"][0]	)







				def     _snake_case					(							self							:						str	)				->					List[Any]:
										# safety check on max_len default value so we are sure the test works
										__magic_name__            =     self.get_tokenizers()
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
																						self.assertNotEqual(tokenizer.model_max_length						,    4_2	)

        # Now let's start the test
										__magic_name__            =     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
																						__magic_name__            =     tempfile.mkdtemp()

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

																						__magic_name__            =     tokenizer.__class__.from_pretrained(__lowerCamelCase	)
																						__magic_name__            =     after_tokenizer.encode(__lowerCamelCase						,    add_special_tokens=__lowerCamelCase	)
																						self.assertListEqual(__lowerCamelCase						,    __lowerCamelCase	)

																						shutil.rmtree(__lowerCamelCase	)

										__magic_name__            =     self.get_tokenizers(model_max_length=4_2	)
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
																						# Isolate this from the other tests because we save additional tokens/etc
																						__magic_name__            =     tempfile.mkdtemp()

																						__magic_name__            =     " He is very happy, UNwant\u00E9d,running"
																						tokenizer.add_tokens(["bim", "bambam"]	)
																						__magic_name__            =     tokenizer.additional_special_tokens
																						additional_special_tokens.append("new_additional_special_token"	)
																						tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}	)
																						__magic_name__            =     tokenizer.encode(__lowerCamelCase						,    add_special_tokens=__lowerCamelCase	)
																						tokenizer.save_pretrained(__lowerCamelCase	)

																						__magic_name__            =     tokenizer.__class__.from_pretrained(__lowerCamelCase	)
																						__magic_name__            =     after_tokenizer.encode(__lowerCamelCase						,    add_special_tokens=__lowerCamelCase	)
																						self.assertListEqual(__lowerCamelCase						,    __lowerCamelCase	)
																						self.assertIn("new_additional_special_token"						,    after_tokenizer.additional_special_tokens	)
																						self.assertEqual(after_tokenizer.model_max_length						,    4_2	)

																						__magic_name__            =     tokenizer.__class__.from_pretrained(__lowerCamelCase						,    model_max_length=4_3	)
																						self.assertEqual(tokenizer.model_max_length						,    4_3	)

																						shutil.rmtree(__lowerCamelCase	)







				def     _snake_case					(							self							:						Any	)				->					Any:
										__magic_name__            =     []
										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(__lowerCamelCase	)

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

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

																						__magic_name__            =     [f'''<extra_id_{i}>''' for i in range(1_2_5	)]

																						__magic_name__            =     added_tokens_extra_ids + [
																						    "an_additional_special_token"
																						]
																						__magic_name__            =     added_tokens_extra_ids + [
																						    "an_additional_special_token"
																						]

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

																						# 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
																						__magic_name__            =     tokenizer_class.from_pretrained(
																						    __lowerCamelCase						,    )
																						self.assertIn(
																						    "an_additional_special_token"						,    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(
																						    ["an_additional_special_token"]						,    tokenizer_without_change_in_init.convert_ids_to_tokens(
																						        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]	)	)						,    )

																						# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																						__magic_name__            =     added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"						,    lstrip=__lowerCamelCase	)]
																						__magic_name__            =     tokenizer_class.from_pretrained(
																						    __lowerCamelCase						,    additional_special_tokens=__lowerCamelCase						,    )

																						self.assertIn("a_new_additional_special_token"						,    tokenizer.additional_special_tokens	)
																						self.assertEqual(
																						    ["a_new_additional_special_token"]						,    tokenizer.convert_ids_to_tokens(
																						        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]	)	)						,    )







				def     _snake_case					(							self							:						Dict	)				->					List[Any]:
										__magic_name__            =     []
										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(__lowerCamelCase	)

																						__magic_name__            =     tokenizer_class.from_pretrained(__lowerCamelCase	)

																						self.assertTrue(tokenizer.decode([2_5_5]	) == ""	)







				def     _snake_case					(							self							:						Dict	)				->					int:
										pass







				def     _snake_case					(							self							:						Optional[int]	)				->					int:
										pass







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Optional[Any]:
										pass







				def     _snake_case					(							self							:						int	)				->					List[Any]:
										pass







				def     _snake_case					(							self							:						Union[str, Any]	)				->					int:
										# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
										# and special added tokens as tokens
										__magic_name__            =     self.get_tokenizers(fast=__lowerCamelCase						,    do_lower_case=__lowerCamelCase	)
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
																						__magic_name__            =     ["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
																						__magic_name__            =     tokenizer.convert_tokens_to_string(__lowerCamelCase	)

																						self.assertIsInstance(__lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Union[str, Any]:
										__magic_name__            =     self.get_tokenizers()
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
																						__magic_name__            =     [
																						    "bos_token",
																						    "eos_token",
																						    "unk_token",
																						    "sep_token",
																						    "pad_token",
																						    "cls_token",
																						    "mask_token",
																						]

																						__magic_name__            =     0
																						__magic_name__            =     tokenizer.convert_ids_to_tokens(
																						    __lowerCamelCase						,    skip_special_tokens=__lowerCamelCase	)

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

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

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

																						setattr(__lowerCamelCase						,    "additional_special_tokens_ids"						,    [token_id_to_test_setters]	)
																						self.assertListEqual(getattr(__lowerCamelCase						,    "additional_special_tokens"	)						,    [token_to_test_setters]	)
																						self.assertListEqual(getattr(__lowerCamelCase						,    "additional_special_tokens_ids"	)						,    [token_id_to_test_setters]	)











"""simple docstring"""

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
			# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
			# setup instructions, if using on-demand hardware

			# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
			# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
			# Throw an error if user passes both BYO and on-demand cluster args
			# Otherwise, use default values
			lowercase    			=       argparse.ArgumentParser()
			parser.add_argument('''--user''', type=str, default='''ubuntu''')
			parser.add_argument('''--host''', type=str, default='''localhost''')
			parser.add_argument('''--key_path''', type=str, default=None)
			parser.add_argument('''--instance''', type=str, default='''V100:1''')
			parser.add_argument('''--provider''', type=str, default='''cheapest''')
			parser.add_argument('''--use_spot''', type=bool, default=False)
			parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
			lowercase			,      lowercase    			=       parser.parse_known_args()
			if args.host != "localhost":
						if args.instance != "V100:1" or args.provider != "cheapest":
									raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowercase    			=       args.example.rsplit('''/''', 1)[0]

			# Set up remote environment
			cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
			# Note transformers is copied into the home directory on the remote machine, so we can install from there
			cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
			cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

			# Run example. You can bypass the CLI wrapper and paste your own code here.
			cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

			# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
			# from my_script... import train
			# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
			# launch_train_gpu = rh.function(fn=train,
			#                                system=gpu,
			#                                reqs=reqs,
			#                                name='train_bert_glue')
			#
			# We can pass in arguments just like we would to a function:
			# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
			#                  stream_logs=True)



