
"""simple docstring"""







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  A__       (	unittest.TestCase):

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      tempfile.mkdtemp()

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

			__lowerCAmelCase			:						Dict									=      BlipaProcessor(snake_case__       ,		snake_case__ )

			processor.save_pretrained(self.tmpdirname )

		def 							__lowerCamelCase							(      self       ,		**_SCREAMING_SNAKE_CASE ):
			return AutoProcessor.from_pretrained(self.tmpdirname       ,		**snake_case__ ).tokenizer

		def 							__lowerCamelCase							(      self       ,		**_SCREAMING_SNAKE_CASE ):
			return AutoProcessor.from_pretrained(self.tmpdirname       ,		**snake_case__ ).image_processor

		def 							__lowerCamelCase							(      self ):
			shutil.rmtree(self.tmpdirname )

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

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

			return image_inputs

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

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

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

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

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

			__lowerCAmelCase			:						Optional[int]									=      image_processor(snake_case__       ,		return_tensors='np' )
			__lowerCAmelCase			:						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 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      self.get_image_processor()
			__lowerCAmelCase			:						Tuple									=      self.get_tokenizer()

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

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

			__lowerCAmelCase			:						int									=      processor(text=snake_case__ )

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

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

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

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

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

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

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

			self.assertListEqual(snake_case__       ,		snake_case__ )







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      self.get_image_processor()
			__lowerCAmelCase			:						int									=      self.get_tokenizer()

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

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

			__lowerCAmelCase			:						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__    ,						)





