
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      tempfile.mkdtemp()

          snake_case       :   Tuple							=      BlipImageProcessor()
          snake_case       :   Optional[int]							=      GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )

          snake_case       :   Dict							=      BlipaProcessor(snake_case__    ,						snake_case__ )

          processor.save_pretrained(self.tmpdirname )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						**snake_case__		:					int )		->	Union[str, Any]:


          '''simple docstring'''


          return AutoProcessor.from_pretrained(self.tmpdirname    ,						**snake_case__ ).tokenizer



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						**snake_case__		:					Any )		->	Union[str, Any]:


          '''simple docstring'''


          return AutoProcessor.from_pretrained(self.tmpdirname    ,						**snake_case__ ).image_processor



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[Any]:


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      [np.random.randint(2_55    ,						size=(3, 30, 4_00)    ,						dtype=np.uinta )]

          snake_case       :   Tuple							=      [Image.fromarray(np.moveaxis(snake_case__    ,						0    ,						-1 ) ) for x in image_inputs]

          return image_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	str:


          '''simple docstring'''


          snake_case       :   List[Any]							=      BlipaProcessor(tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor() )
          processor.save_pretrained(self.tmpdirname )

          snake_case       :   Dict							=      self.get_tokenizer(bos_token="(BOS)"    ,						eos_token="(EOS)" )
          snake_case       :   Union[str, Any]							=      self.get_image_processor(do_normalize=snake_case__    ,						padding_value=1.0 )

          snake_case       :   Dict							=      BlipaProcessor.from_pretrained(
              self.tmpdirname    ,						bos_token="(BOS)"    ,						eos_token="(EOS)"    ,						do_normalize=snake_case__    ,						padding_value=1.0 )

          self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer_add_kwargs.get_vocab() )
          self.assertIsInstance(processor.tokenizer    ,						snake_case__ )

          self.assertEqual(processor.image_processor.to_json_string()    ,						image_processor_add_kwargs.to_json_string() )
          self.assertIsInstance(processor.image_processor    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Any:


          '''simple docstring'''


          snake_case       :   List[str]							=      self.get_image_processor()
          snake_case       :   str							=      self.get_tokenizer()

          snake_case       :   Optional[Any]							=      BlipaProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Optional[Any]							=      self.prepare_image_inputs()

          snake_case       :   Optional[int]							=      image_processor(snake_case__    ,						return_tensors="np" )
          snake_case       :   List[Any]							=      processor(images=snake_case__    ,						return_tensors="np" )

          for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()    ,						input_processor[key].sum()    ,						delta=1e-2 )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Tuple:


          '''simple docstring'''


          snake_case       :   int							=      self.get_image_processor()
          snake_case       :   Tuple							=      self.get_tokenizer()

          snake_case       :   List[str]							=      BlipaProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Union[str, Any]							=      "lower newer"

          snake_case       :   int							=      processor(text=snake_case__ )

          snake_case       :   List[str]							=      tokenizer(snake_case__    ,						return_token_type_ids=snake_case__ )

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]    ,						encoded_processor[key] )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   int							=      self.get_image_processor()
          snake_case       :   List[Any]							=      self.get_tokenizer()

          snake_case       :   List[str]							=      BlipaProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   int							=      "lower newer"
          snake_case       :   str							=      self.prepare_image_inputs()

          snake_case       :   Union[str, Any]							=      processor(text=snake_case__    ,						images=snake_case__ )

          self.assertListEqual(list(inputs.keys() )    ,						["pixel_values", "input_ids", "attention_mask"] )

          # test if it raises when no input is passed
          with pytest.raises(snake_case__ ):
                 processor()



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Any							=      self.get_image_processor()
          snake_case       :   List[str]							=      self.get_tokenizer()

          snake_case       :   Tuple							=      BlipaProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Optional[Any]							=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          snake_case       :   Dict							=      processor.batch_decode(snake_case__ )
          snake_case       :   str							=      tokenizer.batch_decode(snake_case__ )

          self.assertListEqual(snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	str:


          '''simple docstring'''


          snake_case       :   int							=      self.get_image_processor()
          snake_case       :   int							=      self.get_tokenizer()

          snake_case       :   Optional[int]							=      BlipaProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   List[str]							=      "lower newer"
          snake_case       :   int							=      self.prepare_image_inputs()

          snake_case       :   Dict							=      processor(text=snake_case__    ,						images=snake_case__ )

          # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
          self.assertListEqual(list(inputs.keys() )    ,						["pixel_values", "input_ids", "attention_mask"] )







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   UpperCAmelCase		:



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]=None    ,						**snake_case__		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


          logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future." )
          snake_case       :   Optional[Any]							=      model
          snake_case       :   Dict							=      kwargs.get("model_save_dir"    ,						snake_case__ )
          snake_case       :   int							=      kwargs.get("latest_model_name"    ,						snake_case__ )



   def __call__(self		:					Tuple    ,						**snake_case__		:					str )		->	List[str]:


          '''simple docstring'''


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



   @staticmethod
   def 				_SCREAMING_SNAKE_CASE   (snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[int]=None    ,						snake_case__		:					Optional[int]=None )		->	Any:


          '''simple docstring'''


          if provider is None:
                 logger.info("No onnxruntime provider specified, using CPUExecutionProvider" )
                 snake_case       :   Optional[int]							=      "CPUExecutionProvider"

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[str] = None    ,						**snake_case__		:					Any )		->	List[Any]:


          '''simple docstring'''


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

          snake_case       :   Any							=      self.model_save_dir.joinpath(self.latest_model_name )
          snake_case       :   str							=      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       :   List[str]							=      self.model_save_dir.joinpath(snake_case__ )
          if src_path.exists():
                 snake_case       :   Tuple							=      Path(snake_case__ ).joinpath(snake_case__ )
                 try:
                        shutil.copyfile(snake_case__    ,						snake_case__ )
                 except shutil.SameFileError:
                        pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, os.PathLike]    ,						**snake_case__		:					Optional[int]    ,						)		->	str:


          '''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 				_SCREAMING_SNAKE_CASE   (cls		:					Tuple    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[Union[bool, str, None]] = None    ,						snake_case__		:					Optional[Union[str, None]] = None    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional["ort.SessionOptions"] = None    ,						**snake_case__		:					Tuple    ,						)		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      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       :   Any							=      OnnxRuntimeModel.load_model(
                     os.path.join(snake_case__    ,						snake_case__ )    ,						provider=snake_case__    ,						sess_options=snake_case__ )
                 snake_case       :   Union[str, 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       :   List[Any]							=      Path(snake_case__ ).parent
                 snake_case       :   Union[str, Any]							=      Path(snake_case__ ).name
                 snake_case       :   Dict							=      OnnxRuntimeModel.load_model(snake_case__    ,						provider=snake_case__    ,						sess_options=snake_case__ )
          return cls(model=snake_case__    ,						**snake_case__ )



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Optional[Any]    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						**snake_case__		:					Dict    ,						)		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      None
          if len(str(snake_case__ ).split("@" ) ) == 2:
                 snake_case				,					snake_case       :   int							=      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__    ,						)





