


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


_snake_case     :		int						=				logging.get_logger(__name__)

_snake_case     :		Optional[int]						=				{
    "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 				a		:




							"""simple docstring"""


							def __init__(							self       :  List[str]    ,	lowerCamelCase       :  str=None    ,	**lowerCamelCase       :  Optional[int]				)	->    str:
														logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."				)
														__snake_case		:			Any							 =   model
														__snake_case		:			Dict							 =   kwargs.get("model_save_dir"    ,	__a				)
														__snake_case		:			Optional[int]							 =   kwargs.get("latest_model_name"    ,	__a				)

							def __call__(							self       :  Tuple    ,	**lowerCamelCase       :  Optional[int]				)	->    int:
														__snake_case		:			int							 =   {k: np.array(__a				) for k, v in kwargs.items()}
														return self.model.run(__a    ,	__a				)

							@staticmethod
							def     __snake_case					(							lowerCamelCase       :  Tuple    ,	lowerCamelCase       :  str=None    ,	lowerCamelCase       :  List[str]=None				)	->    str:
														if provider is None:
																					logger.info("No onnxruntime provider specified, using CPUExecutionProvider"				)
																					__snake_case		:			List[Any]							 =   "CPUExecutionProvider"

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

							def     __snake_case					(							self       :  Optional[Any]    ,	lowerCamelCase       :  List[Any]    ,	lowerCamelCase       :  str = None    ,	**lowerCamelCase       :  List[str]				)	->    Union[str, Any]:
														__snake_case		:			Union[str, Any]							 =   file_name if file_name is not None else ONNX_WEIGHTS_NAME

														__snake_case		:			int							 =   self.model_save_dir.joinpath(self.latest_model_name				)
														__snake_case		:			List[Any]							 =   Path(__a				).joinpath(__a				)
														try:
																					shutil.copyfile(__a    ,	__a				)
														except shutil.SameFileError:
																					pass

														# copy external weights (for models >2GB)
														__snake_case		:			List[str]							 =   self.model_save_dir.joinpath(__a				)
														if src_path.exists():
																					__snake_case		:			Tuple							 =   Path(__a				).joinpath(__a				)
																					try:
																												shutil.copyfile(__a    ,	__a				)
																					except shutil.SameFileError:
																												pass

							def     __snake_case					(							self       :  List[Any]    ,	lowerCamelCase       :  str    ,	**lowerCamelCase       :  Optional[Any]    ,	)	->    Dict:
														if os.path.isfile(__a				):
																					logger.error(F'Provided path ({save_directory}) should be a directory, not a file'				)
																					return

														os.makedirs(__a    ,	exist_ok=__a				)

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

							@classmethod
							def     __snake_case					(							cls       :  Dict    ,	lowerCamelCase       :  Dict    ,	lowerCamelCase       :  Dict = None    ,	lowerCamelCase       :  Union[str, Any] = None    ,	lowerCamelCase       :  Optional[Any] = False    ,	lowerCamelCase       :  List[Any] = None    ,	lowerCamelCase       :  Dict = None    ,	lowerCamelCase       :  Any = None    ,	lowerCamelCase       :  List[str] = None    ,	**lowerCamelCase       :  Optional[Any]    ,	)	->    Optional[int]:
														__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(__a				):
																					__snake_case		:			Tuple							 =   OnnxRuntimeModel.load_model(
																					    os.path.join(__a    ,	__a				)    ,	provider=__a    ,	sess_options=__a				)
																					__snake_case		:			Dict							 =   Path(__a				)
														# load model from hub
														else:
																					# download model
																					__snake_case		:			List[Any]							 =   hf_hub_download(
																					    repo_id=__a    ,	filename=__a    ,	use_auth_token=__a    ,	revision=__a    ,	cache_dir=__a    ,	force_download=__a    ,	)
																					__snake_case		:			Optional[Any]							 =   Path(__a				).parent
																					__snake_case		:			List[str]							 =   Path(__a				).name
																					__snake_case		:			Tuple							 =   OnnxRuntimeModel.load_model(__a    ,	provider=__a    ,	sess_options=__a				)
														return cls(model=__a    ,	**__a				)






							@classmethod
							def     __snake_case					(							cls       :  List[str]    ,	lowerCamelCase       :  Tuple    ,	lowerCamelCase       :  int = True    ,	lowerCamelCase       :  List[Any] = None    ,	lowerCamelCase       :  Tuple = None    ,	**lowerCamelCase       :  List[Any]    ,	)	->    Optional[Any]:
														__snake_case		:			str							 =   None
														if len(str(__a				).split("@"				)				) == 2:
																					__snake_case ,			__snake_case		:			List[Any]							 =   model_id.split("@"				)

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





"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
	import torch
@is_pipeline_test
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__UpperCAmelCase						:    Union[str, Any]				      =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			TextaTextGenerationPipeline(model=__a    ,   tokenizer=__a	)
										return generator, ["Something to write", "Something else"]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			generator("Something there"	)
										self.assertEqual(__a    ,   [{"generated_text": ANY(__a	)}]	)
										# These are encoder decoder, they don't just append to incoming string
										self.assertFalse(outputs[0]["generated_text"].startswith("Something there"	)	)

										__lowerCAmelCase				    =			generator(["This is great !", "Something else"]    ,   num_return_sequences=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										__lowerCAmelCase				    =			generator(
										    ["This is great !", "Something else"]    ,   num_return_sequences=2    ,   batch_size=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										with self.assertRaises(__a	):
													generator(4	)

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="pt"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)

										__lowerCAmelCase				    =			3
										__lowerCAmelCase				    =			generator(
										    "Something there"    ,   num_return_sequences=__a    ,   num_beams=__a    ,   )
										__lowerCAmelCase				    =			[
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": ""},
										]
										self.assertEqual(__a    ,   __a	)

										__lowerCAmelCase				    =			generator("This is a test"    ,   do_sample=__a    ,   num_return_sequences=2    ,   return_tensors=__a	)
										self.assertEqual(
										    __a    ,   [
										        {"generated_token_ids": ANY(torch.Tensor	)},
										        {"generated_token_ids": ANY(torch.Tensor	)},
										    ]    ,   )
										__lowerCAmelCase				    =			generator.model.config.eos_token_id
										__lowerCAmelCase				    =			"<pad>"
										__lowerCAmelCase				    =			generator(
										    ["This is a test", "This is a second test"]    ,   do_sample=__a    ,   num_return_sequences=2    ,   batch_size=2    ,   return_tensors=__a    ,   )
										self.assertEqual(
										    __a    ,   [
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										    ]    ,   )



							@require_tf
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="tf"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)




