





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
      import onnxruntime as ort


lowerCAmelCase_     =     logging.get_logger(__name__)

lowerCAmelCase_     =     {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}



class 							_lowerCAmelCase     :


  '''simple docstring'''







  def __init__(   self   :       int   ,					UpperCamelCase   :       List[Any]=None   ,					**UpperCamelCase   :       List[Any]				):


    '''simple docstring'''





    logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'				)
    _snake_case :					Dict								=						model
    _snake_case :					List[str]								=						kwargs.get('model_save_dir'   ,					snake_case__				)
    _snake_case :					List[Any]								=						kwargs.get('latest_model_name'   ,					snake_case__				)







  def __call__(   self   :       int   ,					**UpperCamelCase   :       List[Any]				):


    '''simple docstring'''





    _snake_case :					List[Any]								=						{k: np.array(snake_case__				) for k, v in kwargs.items()}
    return self.model.run(snake_case__   ,					snake_case__				)







  @staticmethod
  def UpperCamelCase_			(   UpperCamelCase   :       Union[str, Path]   ,					UpperCamelCase   :       List[Any]=None   ,					UpperCamelCase   :       int=None				):


    '''simple docstring'''





    if provider is None:
      logger.info('No onnxruntime provider specified, using CPUExecutionProvider'				)
      _snake_case :					List[Any]								=						'CPUExecutionProvider'

    return ort.InferenceSession(snake_case__   ,					providers=[provider]   ,					sess_options=snake_case__				)







  def UpperCamelCase_			(   self   :       Tuple   ,					UpperCamelCase   :       Union[str, Path]   ,					UpperCamelCase   :       Optional[str] = None   ,					**UpperCamelCase   :       Union[str, Any]				):


    '''simple docstring'''





    _snake_case :					Tuple								=						file_name if file_name is not None else ONNX_WEIGHTS_NAME

    _snake_case :					List[Any]								=						self.model_save_dir.joinpath(self.latest_model_name				)
    _snake_case :					Dict								=						Path(snake_case__				).joinpath(snake_case__				)
    try:
      shutil.copyfile(snake_case__   ,					snake_case__				)
    except shutil.SameFileError:
      pass

    # copy external weights (for models >2GB)
    _snake_case :					Optional[int]								=						self.model_save_dir.joinpath(snake_case__				)
    if src_path.exists():
      _snake_case :					Union[str, Any]								=						Path(snake_case__				).joinpath(snake_case__				)
      try:
        shutil.copyfile(snake_case__   ,					snake_case__				)
      except shutil.SameFileError:
        pass







  def UpperCamelCase_			(   self   :       int   ,					UpperCamelCase   :       Union[str, os.PathLike]   ,					**UpperCamelCase   :       Optional[Any]   ,					):


    '''simple docstring'''





    if os.path.isfile(snake_case__				):
      logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""				)
      return

    os.makedirs(snake_case__   ,					exist_ok=snake_case__				)

    # saving model weights/files
    self._save_pretrained(snake_case__   ,					**snake_case__				)







  @classmethod
  def UpperCamelCase_			(   cls   :       int   ,					UpperCamelCase   :       Union[str, Path]   ,					UpperCamelCase   :       Optional[Union[bool, str, None]] = None   ,					UpperCamelCase   :       Optional[Union[str, None]] = None   ,					UpperCamelCase   :       bool = False   ,					UpperCamelCase   :       Optional[str] = None   ,					UpperCamelCase   :       Optional[str] = None   ,					UpperCamelCase   :       Optional[str] = None   ,					UpperCamelCase   :       Optional["ort.SessionOptions"] = None   ,					**UpperCamelCase   :       Union[str, Any]   ,					):


    '''simple docstring'''





    _snake_case :					Optional[int]								=						file_name if file_name is not None else ONNX_WEIGHTS_NAME
    # load model from local directory
    if os.path.isdir(snake_case__				):
      _snake_case :					Union[str, Any]								=						OnnxRuntimeModel.load_model(
          os.path.join(snake_case__   ,					snake_case__				)   ,					provider=snake_case__   ,					sess_options=snake_case__				)
      _snake_case :					List[Any]								=						Path(snake_case__				)
    # load model from hub
    else:
      # download model
      _snake_case :					Dict								=						hf_hub_download(
          repo_id=snake_case__   ,					filename=snake_case__   ,					use_auth_token=snake_case__   ,					revision=snake_case__   ,					cache_dir=snake_case__   ,					force_download=snake_case__   ,					)
      _snake_case :					int								=						Path(snake_case__				).parent
      _snake_case :					int								=						Path(snake_case__				).name
      _snake_case :					Optional[int]								=						OnnxRuntimeModel.load_model(snake_case__   ,					provider=snake_case__   ,					sess_options=snake_case__				)
    return cls(model=snake_case__   ,					**snake_case__				)







  @classmethod
  def UpperCamelCase_			(   cls   :       int   ,					UpperCamelCase   :       Union[str, Path]   ,					UpperCamelCase   :       bool = True   ,					UpperCamelCase   :       Optional[str] = None   ,					UpperCamelCase   :       Optional[str] = None   ,					**UpperCamelCase   :       Optional[Any]   ,					):


    '''simple docstring'''





    _snake_case :					int								=						None
    if len(str(snake_case__				).split('@'				)				) == 2:
      _snake_case				,       _snake_case :					Union[str, Any]								=						model_id.split('@'				)

    return cls._from_pretrained(
        model_id=snake_case__   ,					revision=snake_case__   ,					cache_dir=snake_case__   ,					force_download=snake_case__   ,					use_auth_token=snake_case__   ,					**snake_case__   ,					)





"""simple docstring"""




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Optional[int]      =     MgpstrTokenizer
					UpperCamelCase_			:						Optional[int]      =     False
					UpperCamelCase_			:						Tuple      =     {}
					UpperCamelCase_			:						List[str]      =     False




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							super().setUp()

							# fmt: off
							lowerCAmelCase__   = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
							# fmt: on
							lowerCAmelCase__   = dict(zip(snake_case__	,    range(len(snake_case__  )  )  )  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    **snake_case__    :  Tuple  ):
							return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  Dict  ):
							lowerCAmelCase__   = """tester"""
							lowerCAmelCase__   = """tester"""
							return input_text, output_text




					@unittest.skip("""MGP-STR always lower cases letters."""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							pass




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = self.get_tokenizers(do_lower_case=snake_case__  )
							for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
											lowerCAmelCase__   = """[SPECIAL_TOKEN]"""

											tokenizer.add_special_tokens({"""cls_token""": special_token}  )
											lowerCAmelCase__   = tokenizer.encode([special_token]	,    add_special_tokens=snake_case__  )
											self.assertEqual(len(snake_case__  )	,    1  )

											lowerCAmelCase__   = tokenizer.decode(snake_case__	,    skip_special_tokens=snake_case__  )
											self.assertTrue(special_token not in decoded  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.get_tokenizers()
							for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
											lowerCAmelCase__ ,       lowerCAmelCase__   = self.get_input_output_texts(snake_case__  )

											lowerCAmelCase__   = tokenizer.tokenize(snake_case__  )
											lowerCAmelCase__   = tokenizer.convert_tokens_to_ids(snake_case__  )
											lowerCAmelCase__   = tokenizer.encode(snake_case__	,    add_special_tokens=snake_case__  )
											self.assertListEqual(snake_case__	,    snake_case__  )

											lowerCAmelCase__   = tokenizer.convert_ids_to_tokens(snake_case__  )
											self.assertNotEqual(len(snake_case__  )	,    0  )
											lowerCAmelCase__   = tokenizer.decode(snake_case__  )
											self.assertIsInstance(snake_case__	,    snake_case__  )

											self.assertEqual(text_a.replace(""" """	,    """"""  )	,    snake_case__  )




					@unittest.skip("""MGP-STR tokenizer only handles one sequence."""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							pass




					@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							pass




