





"""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  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      str         =CLIPTokenizer
							lowerCamelCase     :      Tuple         =CLIPTokenizerFast
							lowerCamelCase     :      List[str]         =True
							lowerCamelCase     :      int         ={}
							lowerCamelCase     :      int         =False






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

								# fmt: off
								a   :	Dict			=       ["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
								a   :	Any			=       dict(zip(lowerCAmelCase__   ,      range(len(lowerCAmelCase__   )   )   )   )
								a   :	Optional[int]			=       ["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
								a   :	List[str]			=       {"unk_token": "<unk>"}

								a   :	Dict			=       os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES["vocab_file"]   )
								a   :	List[str]			=       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 					__a     (     self   ,      **lowerCAmelCase__   )			->				List[Any]:
								kwargs.update(self.special_tokens_map   )
								return CLIPTokenizer.from_pretrained(self.tmpdirname   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      **lowerCAmelCase__   )			->				int:
								kwargs.update(self.special_tokens_map   )
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								a   :	List[Any]			=       "lower newer"
								a   :	int			=       "lower newer"
								return input_text, output_text






							def 					__a     (     self   )			->				str:
								a   :	Optional[Any]			=       CLIPTokenizer(self.vocab_file   ,      self.merges_file   ,      **self.special_tokens_map   )
								a   :	List[Any]			=       "lower newer"
								a   :	Optional[int]			=       ["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
								a   :	List[str]			=       tokenizer.tokenize(lowerCAmelCase__   )
								self.assertListEqual(lowerCAmelCase__   ,      lowerCAmelCase__   )

								a   :	List[Any]			=       tokens + [tokenizer.unk_token]
								a   :	List[Any]			=       [10, 2, 16, 9, 3, 2, 16, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__   )   ,      lowerCAmelCase__   )






							@require_ftfy
							def 					__a     (     self   )			->				Dict:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
										a   :	List[str]			=       self.tokenizer_class.from_pretrained(lowerCAmelCase__   ,      **lowerCAmelCase__   )
										a   :	List[Any]			=       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__   ,      **lowerCAmelCase__   )

										a   :	Any			=       "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
										a   :	str			=       tokenizer_s.tokenize(lowerCAmelCase__   )
										a   :	Union[str, Any]			=       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
										a   :	List[Any]			=       "xa\u0303y" + " " + "x\xe3y"
										a   :	str			=       tokenizer_s.tokenize(lowerCAmelCase__   )
										a   :	Optional[int]			=       tokenizer_r.tokenize(lowerCAmelCase__   )

										self.assertListEqual(lowerCAmelCase__   ,      lowerCAmelCase__   )

										# Test that the tokenization is identical on unicode of space type
										a   :	Any			=       [
										    "\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:
											a   :	Any			=       tokenizer_s.tokenize(lowerCAmelCase__   )
											a   :	Dict			=       tokenizer_r.tokenize(lowerCAmelCase__   )

											self.assertListEqual(lowerCAmelCase__   ,      lowerCAmelCase__   )

										# Test that the tokenization is identical on unicode of line break type
										a   :	Any			=       [
										    "\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:
											a   :	List[str]			=       tokenizer_s.tokenize(lowerCAmelCase__   )
											a   :	Tuple			=       tokenizer_r.tokenize(lowerCAmelCase__   )

											self.assertListEqual(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[str]:
								# 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})"""   ):
										a   :	Tuple			=       "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
										a   :	Tuple			=       f"""{text_of_1_token} {text_of_1_token}"""

										a   :	Union[str, Any]			=       self.rust_tokenizer_class.from_pretrained(
										    lowerCAmelCase__   ,      use_fast=lowerCAmelCase__   ,      )
										a   :	str			=       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__   ))   ,      )

										a   :	Optional[Any]			=       f""" {text}"""

										a   :	Optional[int]			=       self.rust_tokenizer_class.from_pretrained(
										    lowerCAmelCase__   ,      use_fast=lowerCAmelCase__   ,      )
										a   :	Any			=       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 					__a     (     self   )			->				Union[str, Any]:
								# 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 					__a     (     self   )			->				Union[str, Any]:
								super().test_tokenization_python_rust_equals()






							def 					__a     (     self   )			->				List[str]:
								# CLIP always lower cases letters
								pass









"""simple docstring"""




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers.generation import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)


@require_torch
class  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								a   :	str			=       3
								a   :	str			=       250

								a   :	List[Any]			=       ids_tensor((batch_size, length)   ,      lowerCAmelCase__   )
								a   :	Optional[Any]			=       torch.ones((batch_size, length)   ,      device=lowerCAmelCase__   ,      dtype=torch.float   ) / length
								return input_ids, scores






							def 					__a     (     self   )			->				List[Any]:
								a, a   :	str			=       self._get_tensors(5   )

								a   :	Any			=       StoppingCriteriaList(
								    [
								        MaxLengthCriteria(max_length=10   ),
								        MaxTimeCriteria(max_time=0.1   ),
								    ]   )

								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	str			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[Any]:
								a   :	Optional[Any]			=       MaxLengthCriteria(max_length=10   )

								a, a   :	int			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       MaxNewTokensCriteria(start_length=5   ,      max_new_tokens=5   )

								a, a   :	str			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	List[Any]			=       StoppingCriteriaList([criteria]   )
								self.assertEqual(criteria_list.max_length   ,      10   )






							def 					__a     (     self   )			->				str:
								a, a   :	Tuple			=       self._get_tensors(5   )

								a   :	str			=       MaxTimeCriteria(max_time=0.1   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	Optional[int]			=       MaxTimeCriteria(max_time=0.1   ,      initial_timestamp=time.time() - 0.2   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				str:
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      10   )

								with self.assertWarns(lowerCAmelCase__   ):
									validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      11   )

								a   :	Optional[int]			=       validate_stopping_criteria(StoppingCriteriaList()   ,      11   )

								self.assertEqual(len(lowerCAmelCase__   )   ,      1   )


