




import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_UpperCAmelCase	:						str			=     [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-classification",
        "language-modeling",
        "summarization",
        "token-classification",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
     import run_clm_flax
     import run_flax_glue
     import run_flax_ner
     import run_mlm_flax
     import run_qa
     import run_summarization_flax
     import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

_UpperCAmelCase	:						Tuple			=     logging.getLogger()



def       UpperCAmelCase__  (     ):
  lowercase    :Dict      	=						argparse.ArgumentParser()
  parser.add_argument("-f"   )
  lowercase    :Union[str, Any]      	=						parser.parse_args()
  return args.f



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase="eval"   ):
  lowercase    :Any      	=						os.path.join(lowerCamelCase,			F"{split}_results.json"   )
  if os.path.exists(lowerCamelCase   ):
    with open(lowerCamelCase,			"r"   ) as f:
      return json.load(lowerCamelCase   )
  raise ValueError(F"can't find {path}"   )


_UpperCAmelCase	:						Tuple			=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class    __lowerCAmelCase       (							lowerCAmelCase):






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :Optional[Any]      	=						self.get_auto_remove_tmp_dir()
         lowercase    :Tuple      	=						F"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

         with patch.object(_lowerCAmelCase       ,					"argv"       ,					_lowerCAmelCase				):
           run_flax_glue.main()
           lowercase    :Dict      	=						get_results(_lowerCAmelCase				)
           self.assertGreaterEqual(result["eval_accuracy"]       ,					0.75				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :Optional[int]      	=						self.get_auto_remove_tmp_dir()
         lowercase    :List[str]      	=						F"\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

         with patch.object(_lowerCAmelCase       ,					"argv"       ,					_lowerCAmelCase				):
           run_clm_flax.main()
           lowercase    :Dict      	=						get_results(_lowerCAmelCase				)
           self.assertLess(result["eval_perplexity"]       ,					1_00				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :Dict      	=						self.get_auto_remove_tmp_dir()
         lowercase    :List[str]      	=						F"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        ".split()

         with patch.object(_lowerCAmelCase       ,					"argv"       ,					_lowerCAmelCase				):
           run_summarization_flax.main()
           lowercase    :str      	=						get_results(_lowerCAmelCase       ,					split="test"				)
           self.assertGreaterEqual(result["test_rouge1"]       ,					10				)
           self.assertGreaterEqual(result["test_rouge2"]       ,					2				)
           self.assertGreaterEqual(result["test_rougeL"]       ,					7				)
           self.assertGreaterEqual(result["test_rougeLsum"]       ,					7				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :Dict      	=						self.get_auto_remove_tmp_dir()
         lowercase    :Tuple      	=						F"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        ".split()

         with patch.object(_lowerCAmelCase       ,					"argv"       ,					_lowerCAmelCase				):
           run_mlm_flax.main()
           lowercase    :Optional[int]      	=						get_results(_lowerCAmelCase				)
           self.assertLess(result["eval_perplexity"]       ,					42				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :Any      	=						self.get_auto_remove_tmp_dir()
         lowercase    :Dict      	=						F"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

         with patch.object(_lowerCAmelCase       ,					"argv"       ,					_lowerCAmelCase				):
           run_ta_mlm_flax.main()
           lowercase    :int      	=						get_results(_lowerCAmelCase				)
           self.assertGreaterEqual(result["eval_accuracy"]       ,					0.42				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
         lowercase    :Optional[Any]      	=						7 if get_gpu_count() > 1 else 2

         lowercase    :int      	=						self.get_auto_remove_tmp_dir()
         lowercase    :List[str]      	=						F"\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        ".split()

         with patch.object(_lowerCAmelCase       ,					"argv"       ,					_lowerCAmelCase				):
           run_flax_ner.main()
           lowercase    :Union[str, Any]      	=						get_results(_lowerCAmelCase				)
           self.assertGreaterEqual(result["eval_accuracy"]       ,					0.75				)
           self.assertGreaterEqual(result["eval_f1"]       ,					0.3				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :List[str]      	=						self.get_auto_remove_tmp_dir()
         lowercase    :Optional[Any]      	=						F"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

         with patch.object(_lowerCAmelCase       ,					"argv"       ,					_lowerCAmelCase				):
           run_qa.main()
           lowercase    :List[Any]      	=						get_results(_lowerCAmelCase				)
           self.assertGreaterEqual(result["eval_f1"]       ,					30				)
           self.assertGreaterEqual(result["eval_exact"]       ,					30				)









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       ,					)



