






"""simple docstring"""

import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A_							(      snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				CLIPTokenizer
				UpperCAmelCase__        =				CLIPTokenizerFast
				UpperCAmelCase__        =				True
				UpperCAmelCase__        =				{}
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						List[str]	)				->					Any:
										super().setUp()

										# fmt: off
										__magic_name__            =     ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
										# fmt: on
										__magic_name__            =     dict(zip(__lowerCamelCase						,    range(len(__lowerCamelCase	)	)	)	)
										__magic_name__            =     ["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
										__magic_name__            =     {"unk_token": "<unk>"}

										__magic_name__            =     os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES["vocab_file"]	)
										__magic_name__            =     os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES["merges_file"]	)
										with open(self.vocab_file						,    "w"						,    encoding="utf-8"	) as fp:
																fp.write(json.dumps(__lowerCamelCase	) + "\n"	)
										with open(self.merges_file						,    "w"						,    encoding="utf-8"	) as fp:
																fp.write("\n".join(__lowerCamelCase	)	)







				def     _snake_case					(							self							:						List[str]						,    **__lowerCamelCase							:						Tuple	)				->					Optional[int]:
										kwargs.update(self.special_tokens_map	)
										return CLIPTokenizer.from_pretrained(self.tmpdirname						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]						,    **__lowerCamelCase							:						Any	)				->					List[Any]:
										kwargs.update(self.special_tokens_map	)
										return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						int	)				->					Tuple:
										__magic_name__            =     "lower newer"
										__magic_name__            =     "lower newer"
										return input_text, output_text







				def     _snake_case					(							self							:						Union[str, Any]	)				->					int:
										__magic_name__            =     CLIPTokenizer(self.vocab_file						,    self.merges_file						,    **self.special_tokens_map	)
										__magic_name__            =     "lower newer"
										__magic_name__            =     ["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
										__magic_name__            =     tokenizer.tokenize(__lowerCamelCase	)
										self.assertListEqual(__lowerCamelCase						,    __lowerCamelCase	)

										__magic_name__            =     tokens + [tokenizer.unk_token]
										__magic_name__            =     [1_0, 2, 1_6, 9, 3, 2, 1_6, 2_0]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase	)						,    __lowerCamelCase	)







				@require_ftfy
				def     _snake_case					(							self							:						Union[str, Any]	)				->					Optional[Any]:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																						__magic_name__            =     self.tokenizer_class.from_pretrained(__lowerCamelCase						,    **__lowerCamelCase	)
																						__magic_name__            =     self.rust_tokenizer_class.from_pretrained(__lowerCamelCase						,    **__lowerCamelCase	)

																						__magic_name__            =     "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
																						__magic_name__            =     tokenizer_s.tokenize(__lowerCamelCase	)
																						__magic_name__            =     tokenizer_r.tokenize(__lowerCamelCase	)

																						self.assertListEqual(__lowerCamelCase						,    __lowerCamelCase	)

																						# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																						# with Tilde) encoded in 2 different ways
																						__magic_name__            =     "xa\u0303y" + " " + "x\xe3y"
																						__magic_name__            =     tokenizer_s.tokenize(__lowerCamelCase	)
																						__magic_name__            =     tokenizer_r.tokenize(__lowerCamelCase	)

																						self.assertListEqual(__lowerCamelCase						,    __lowerCamelCase	)

																						# Test that the tokenization is identical on unicode of space type
																						__magic_name__            =     [
																						    "\u0009",  # (horizontal tab, '\t')
																						    "\u000B",  # (vertical tab)
																						    "\u000C",  # (form feed)
																						    "\u0020",  # (space, ' ')
																						    "\u200E",  # (left-to-right mark):w
																						    "\u200F",  # (right-to-left mark)
																						]
																						for unicode_seq in spaces_unicodes:
																												__magic_name__            =     tokenizer_s.tokenize(__lowerCamelCase	)
																												__magic_name__            =     tokenizer_r.tokenize(__lowerCamelCase	)

																												self.assertListEqual(__lowerCamelCase						,    __lowerCamelCase	)

																						# Test that the tokenization is identical on unicode of line break type
																						__magic_name__            =     [
																						    "\u000A",  # (line feed, '\n')
																						    "\r\n",  # (carriage return and line feed, '\r\n')
																						    "\u000D",  # (carriage return, '\r')
																						    "\r",  # (carriage return, '\r')
																						    "\u000D",  # (carriage return, '\r')
																						    "\u2028",  # (line separator)
																						    "\u2029",  # (paragraph separator)
																						    # "\u0085", # (next line)
																						]

																						# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																						# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																						# space (and thus into an empty list).

																						for unicode_seq in line_break_unicodes:
																												__magic_name__            =     tokenizer_s.tokenize(__lowerCamelCase	)
																												__magic_name__            =     tokenizer_r.tokenize(__lowerCamelCase	)

																												self.assertListEqual(__lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]	)				->					Optional[int]:
										# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																						__magic_name__            =     "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
																						__magic_name__            =     f'''{text_of_1_token} {text_of_1_token}'''

																						__magic_name__            =     self.rust_tokenizer_class.from_pretrained(
																						    __lowerCamelCase						,    use_fast=__lowerCamelCase						,    )
																						__magic_name__            =     tokenizer_r(__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase	)
																						self.assertEqual(encoding.offset_mapping[0]						,    (0, len(__lowerCamelCase	))	)
																						self.assertEqual(
																						    encoding.offset_mapping[1]						,    (len(__lowerCamelCase	) + 1, len(__lowerCamelCase	) + 1 + len(__lowerCamelCase	))						,    )

																						__magic_name__            =     f''' {text}'''

																						__magic_name__            =     self.rust_tokenizer_class.from_pretrained(
																						    __lowerCamelCase						,    use_fast=__lowerCamelCase						,    )
																						__magic_name__            =     tokenizer_r(__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase	)
																						self.assertEqual(encoding.offset_mapping[0]						,    (1, 1 + len(__lowerCamelCase	))	)
																						self.assertEqual(
																						    encoding.offset_mapping[1]						,    (1 + len(__lowerCamelCase	) + 1, 1 + len(__lowerCamelCase	) + 1 + len(__lowerCamelCase	))						,    )







				def     _snake_case					(							self							:						Optional[int]	)				->					Dict:
										# Test related to the breaking change introduced in transformers v4.17.0
										# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
										with self.assertRaises(__lowerCamelCase	) as context:
																self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"	)

										self.assertTrue(
										    context.exception.args[0].startswith(
										        "The `backend_tokenizer` provided does not match the expected format."	)	)







				@require_ftfy
				def     _snake_case					(							self							:						Tuple	)				->					Any:
										super().test_tokenization_python_rust_equals()







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										# CLIP always lower cases letters
										pass











"""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)



