




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    __lowerCAmelCase       (							unittest.TestCase):
       _a 					=						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
       _a 					=						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					Optional[Any]       ,					_lowerCAmelCase:					Optional[int]				):
         lowercase    :Tuple      	=						TextaTextGenerationPipeline(model=_lowerCAmelCase       ,					tokenizer=_lowerCAmelCase				)
         return generator, ["Something to write", "Something else"]






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					int       ,					_lowerCAmelCase:					Dict				):
         lowercase    :Optional[Any]      	=						generator("Something there"				)
         self.assertEqual(_lowerCAmelCase       ,					[{"generated_text": ANY(_lowerCAmelCase				)}]				)
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]["generated_text"].startswith("Something there"				)				)

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

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

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






       @require_torch
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         lowercase    :Union[str, Any]      	=						pipeline("text2text-generation"       ,					model="patrickvonplaten/t5-tiny-random"       ,					framework="pt"				)
         # do_sample=False necessary for reproducibility
         lowercase    :int      	=						generator("Something there"       ,					do_sample=_lowerCAmelCase				)
         self.assertEqual(_lowerCAmelCase       ,					[{"generated_text": ""}]				)

         lowercase    :Union[str, Any]      	=						3
         lowercase    :Dict      	=						generator(
             "Something there"       ,					num_return_sequences=_lowerCAmelCase       ,					num_beams=_lowerCAmelCase       ,					)
         lowercase    :Optional[Any]      	=						[
             {'''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(_lowerCAmelCase       ,					_lowerCAmelCase				)

         lowercase    :List[Any]      	=						generator("This is a test"       ,					do_sample=_lowerCAmelCase       ,					num_return_sequences=2       ,					return_tensors=_lowerCAmelCase				)
         self.assertEqual(
             _lowerCAmelCase       ,					[
                 {"generated_token_ids": ANY(torch.Tensor				)},
                 {"generated_token_ids": ANY(torch.Tensor				)},
             ]       ,					)
         lowercase    :Optional[Any]      	=						generator.model.config.eos_token_id
         lowercase    :Optional[Any]      	=						'''<pad>'''
         lowercase    :int      	=						generator(
             ["This is a test", "This is a second test"]       ,					do_sample=_lowerCAmelCase       ,					num_return_sequences=2       ,					batch_size=2       ,					return_tensors=_lowerCAmelCase       ,					)
         self.assertEqual(
             _lowerCAmelCase       ,					[
                 [
                     {"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 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :int      	=						pipeline("text2text-generation"       ,					model="patrickvonplaten/t5-tiny-random"       ,					framework="tf"				)
         # do_sample=False necessary for reproducibility
         lowercase    :str      	=						generator("Something there"       ,					do_sample=_lowerCAmelCase				)
         self.assertEqual(_lowerCAmelCase       ,					[{"generated_text": ""}]				)











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


_UpperCAmelCase	:						Dict			=     logging.get_logger(__name__)

_UpperCAmelCase	:						List[Any]			=     {
    "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       :






       def __init__(   self:					Dict       ,					_lowerCAmelCase:					Union[str, Any]=None       ,					**_lowerCAmelCase:					Any				):
         logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."				)
         lowercase    :str      	=						model
         lowercase    :Any      	=						kwargs.get("model_save_dir"       ,					_lowerCAmelCase				)
         lowercase    :Dict      	=						kwargs.get("latest_model_name"       ,					_lowerCAmelCase				)






       def __call__(   self:					Optional[Any]       ,					**_lowerCAmelCase:					List[Any]				):
         lowercase    :List[str]      	=						{k: np.array(_lowerCAmelCase				) for k, v in kwargs.items()}
         return self.model.run(_lowerCAmelCase       ,					_lowerCAmelCase				)






       @staticmethod
       def 					SCREAMING_SNAKE_CASE    (   _lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					List[Any]=None       ,					_lowerCAmelCase:					List[Any]=None				):
         if provider is None:
           logger.info("No onnxruntime provider specified, using CPUExecutionProvider"				)
           lowercase    :int      	=						"CPUExecutionProvider"

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






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					Optional[str] = None       ,					**_lowerCAmelCase:					Any				):
         lowercase    :Optional[int]      	=						file_name if file_name is not None else ONNX_WEIGHTS_NAME

         lowercase    :Tuple      	=						self.model_save_dir.joinpath(self.latest_model_name				)
         lowercase    :Optional[int]      	=						Path(_lowerCAmelCase				).joinpath(_lowerCAmelCase				)
         try:
           shutil.copyfile(_lowerCAmelCase       ,					_lowerCAmelCase				)
         except shutil.SameFileError:
           pass

         # copy external weights (for models >2GB)
         lowercase    :Tuple      	=						self.model_save_dir.joinpath(_lowerCAmelCase				)
         if src_path.exists():
           lowercase    :Dict      	=						Path(_lowerCAmelCase				).joinpath(_lowerCAmelCase				)
           try:
             shutil.copyfile(_lowerCAmelCase       ,					_lowerCAmelCase				)
           except shutil.SameFileError:
             pass






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					Union[str, os.PathLike]       ,					**_lowerCAmelCase:					int       ,					):
         if os.path.isfile(_lowerCAmelCase				):
           logger.error(F"Provided path ({save_directory}) should be a directory, not a file"				)
           return

         os.makedirs(_lowerCAmelCase       ,					exist_ok=_lowerCAmelCase				)

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






       @classmethod
       def 					SCREAMING_SNAKE_CASE    (   cls:					Dict       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					Optional[Union[bool, str, None]] = None       ,					_lowerCAmelCase:					Optional[Union[str, None]] = None       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional["ort.SessionOptions"] = None       ,					**_lowerCAmelCase:					Optional[int]       ,					):
         lowercase    :List[str]      	=						file_name if file_name is not None else ONNX_WEIGHTS_NAME
         # load model from local directory
         if os.path.isdir(_lowerCAmelCase				):
           lowercase    :Union[str, Any]      	=						OnnxRuntimeModel.load_model(
               os.path.join(_lowerCAmelCase       ,					_lowerCAmelCase				)       ,					provider=_lowerCAmelCase       ,					sess_options=_lowerCAmelCase				)
           lowercase    :Optional[Any]      	=						Path(_lowerCAmelCase				)
         # load model from hub
         else:
           # download model
           lowercase    :str      	=						hf_hub_download(
               repo_id=_lowerCAmelCase       ,					filename=_lowerCAmelCase       ,					use_auth_token=_lowerCAmelCase       ,					revision=_lowerCAmelCase       ,					cache_dir=_lowerCAmelCase       ,					force_download=_lowerCAmelCase       ,					)
           lowercase    :Optional[int]      	=						Path(_lowerCAmelCase				).parent
           lowercase    :Tuple      	=						Path(_lowerCAmelCase				).name
           lowercase    :Tuple      	=						OnnxRuntimeModel.load_model(_lowerCAmelCase       ,					provider=_lowerCAmelCase       ,					sess_options=_lowerCAmelCase				)
         return cls(model=_lowerCAmelCase       ,					**_lowerCAmelCase				)






       @classmethod
       def 					SCREAMING_SNAKE_CASE    (   cls:					str       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					bool = True       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					**_lowerCAmelCase:					Any       ,					):
         lowercase    :List[str]      	=						None
         if len(str(_lowerCAmelCase				).split("@"				)				) == 2:
           lowercase	,    lowercase    :Tuple      	=						model_id.split("@"				)

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



