

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_ta import TaTokenizer
else:
						lowerCAmelCase							:  List[Any]   	=						None


lowerCAmelCase							:  str   	=						logging.get_logger(__name__)

lowerCAmelCase							:  Union[str, Any]   	=						{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase							:  Optional[Any]   	=						{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase							:  str   	=						{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Any										=     VOCAB_FILES_NAMES
			_UpperCAmelCase   :							Dict										=     PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase   :							int										=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase   :							str										=     ['''input_ids''', '''attention_mask''']
			_UpperCAmelCase   :							Optional[int]										=     TaTokenizer

			_UpperCAmelCase   :							List[int]										=     []



			def __init__( self  :     Any   ,						lowerCAmelCase__  :     Optional[int]=None   ,						lowerCAmelCase__  :     Dict=None   ,						lowerCAmelCase__  :     int="</s>"   ,						lowerCAmelCase__  :     List[Any]="<unk>"   ,						lowerCAmelCase__  :     str="<pad>"   ,						lowerCAmelCase__  :     List[Any]=100   ,						lowerCAmelCase__  :     List[Any]=None   ,						**lowerCAmelCase__  :     Tuple   ,						):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       [F"<extra_id_{i}>" for i in range(lowerCAmelCase__)]
									elif extra_ids > 0 and additional_special_tokens is not None:
															# Check that we have the right number of extra special tokens
															SCREAMING_SNAKE_CASE_: List[str]  		=       len(set(filter(lambda lowerCAmelCase__: bool("extra_id_" in str(lowerCAmelCase__))   ,						lowerCAmelCase__)))
															if extra_tokens != extra_ids:
																					raise ValueError(
																					    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																					    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
																					    " tokens")

									super().__init__(
									    lowerCAmelCase__   ,						tokenizer_file=lowerCAmelCase__   ,						eos_token=lowerCAmelCase__   ,						unk_token=lowerCAmelCase__   ,						pad_token=lowerCAmelCase__   ,						extra_ids=lowerCAmelCase__   ,						additional_special_tokens=lowerCAmelCase__   ,						**lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: Dict  		=       vocab_file
									SCREAMING_SNAKE_CASE_: Dict  		=       False if not self.vocab_file else True
									SCREAMING_SNAKE_CASE_: List[str]  		=       extra_ids



			@staticmethod
			def        _SCREAMING_SNAKE_CASE			( lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Any):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
															SCREAMING_SNAKE_CASE_: List[str]  		=       TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
															if init_max_model_length is not None and init_max_model_length != max_model_length:
																					return init_max_model_length
															elif init_max_model_length is None:
																					warnings.warn(
																					    "This tokenizer was incorrectly instantiated with a model max length of"
																					    F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
																					    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
																					    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
																					    F" {pretrained_model_name_or_path} automatically truncating your input to"
																					    F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
																					    F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
																					    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
																					    " instantiate this tokenizer with `model_max_length` set to your preferred value."   ,						lowerCAmelCase__   ,						)

									return max_model_length



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[str] = None):
									if not self.can_save_slow_tokenizer:
															raise ValueError(
															    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
															    "tokenizer.")

									if not os.path.isdir(lowerCAmelCase__):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory")
															return
									SCREAMING_SNAKE_CASE_: Tuple  		=       os.path.join(
									    lowerCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

									if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__):
															copyfile(self.vocab_file   ,						lowerCAmelCase__)
															logger.info(F"Copy vocab file to {out_vocab_file}")

									return (out_vocab_file,)



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
															return self.prefix_tokens + token_ids_a
									else:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       token_ids_a + [self.eos_token_id]
															return self.prefix_tokens + token_ids_a + token_ids_a



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       [self.eos_token_id]

									if token_ids_a is None:
															return len(token_ids_a + eos) * [0]
									return len(token_ids_a + eos + token_ids_a + eos) * [0]



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									return list(
									    set(filter(lambda lowerCAmelCase__: bool(re.search(R"<extra_id_\d+>"   ,						lowerCAmelCase__)) is not None   ,						self.additional_special_tokens)))




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									return [self.convert_tokens_to_ids(lowerCAmelCase__) for token in self.get_sentinel_tokens()]










"""simple docstring"""







import os
import sys
import unittest


__lowercase    					=      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowercase    					=      os.path.join(git_repo_path, """src""", """diffusers""")






class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    Any):
      a						:		List[Any]      							=			find_backend("    if not is_torch_available():")
      self.assertEqual(__UpperCAmelCase				, "torch")

      # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
      # self.assertEqual(backend_with_underscore, "tensorflow_text")

      a						:		Dict      							=			find_backend("    if not (is_torch_available() and is_transformers_available()):")
      self.assertEqual(__UpperCAmelCase				, "torch_and_transformers")

      # double_backend_with_underscore = find_backend(
      #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
      # )
      # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

      a						:		int      							=			find_backend(
          "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):")
      self.assertEqual(__UpperCAmelCase				, "torch_and_transformers_and_onnx")






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Dict      							=			read_init()
      # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
      self.assertIn("torch"				, __UpperCAmelCase)
      self.assertIn("torch_and_transformers"				, __UpperCAmelCase)
      self.assertIn("flax_and_transformers"				, __UpperCAmelCase)
      self.assertIn("torch_and_transformers_and_onnx"				, __UpperCAmelCase)

      # Likewise, we can't assert on the exact content of a key
      self.assertIn("UNet2DModel"				, objects["torch"])
      self.assertIn("FlaxUNet2DConditionModel"				, objects["flax"])
      self.assertIn("StableDiffusionPipeline"				, objects["torch_and_transformers"])
      self.assertIn("FlaxStableDiffusionPipeline"				, objects["flax_and_transformers"])
      self.assertIn("LMSDiscreteScheduler"				, objects["torch_and_scipy"])
      self.assertIn("OnnxStableDiffusionPipeline"				, objects["torch_and_transformers_and_onnx"])






    def 	__snake_case							(					self				:    Tuple):
      a						:		Optional[int]      							=			create_dummy_object("CONSTANT"				, "'torch'")
      self.assertEqual(__UpperCAmelCase				, "\nCONSTANT = None\n")

      a						:		Dict      							=			create_dummy_object("function"				, "'torch'")
      self.assertEqual(
          __UpperCAmelCase				, "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n")

      a						:		Optional[Any]      							=			"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
      a						:		int      							=			create_dummy_object("FakeClass"				, "'torch'")
      self.assertEqual(__UpperCAmelCase				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[str]):
      a						:		List[str]      							=			"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
      a						:		Tuple      							=			create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
      self.assertEqual(dummy_files["torch"]				, __UpperCAmelCase)






