'''simple docstring'''





import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow




class 	__A				:



    def  _lowercase						(self   :   Optional[int]       ,      __a   :   List[Any]       ,      __a   :   Optional[Any]       ,      __a   :   Tuple			):
         return None




class 	__A				:



    def  _lowercase						(self   :   Optional[int]       ,      __a   :   Tuple       ,      __a   :   str       ,      __a   :   Any       ,      __a   :   Union[str, Any]			):
         return None




class 	__A				(			unittest.TestCase     ):
    a__	:						Any	       =  [
        # (model_name, model_kwargs)
        ("""bert-base-cased""", {}),
        ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
    ]



    @require_tf
    @slow
    def  _lowercase						(self   :   str			):
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(__a       ,      "tf"       ,      12       ,      **__a			)



    @require_torch
    @slow
    def  _lowercase						(self   :   str			):
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(__a       ,      "pt"       ,      12       ,      **__a			)



    @require_torch
    @slow
    def  _lowercase						(self   :   Optional[int]			):
         from transformers import BertModel

         UpperCAmelCase_	   =       ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
         with NamedTemporaryFile(mode="w+t"			) as vocab_file:
              vocab_file.write("\n".join(__a			)			)
              vocab_file.flush()
              UpperCAmelCase_	   =       BertTokenizerFast(vocab_file.name			)

         with TemporaryDirectory() as bert_save_dir:
              UpperCAmelCase_	   =       BertModel(BertConfig(vocab_size=len(__a			)			)			)
              model.save_pretrained(__a			)
              self._test_export(__a       ,      "pt"       ,      12       ,      __a			)



    @require_tf
    @slow
    def  _lowercase						(self   :   Dict			):
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              UpperCAmelCase_	   =       self._test_export(__a       ,      "tf"       ,      12       ,      **__a			)
              UpperCAmelCase_	   =       quantize(Path(__a			)			)

              # Ensure the actual quantized model is not bigger than the original one
              if quantized_path.stat().st_size >= Path(__a			).stat().st_size:
                   self.fail("Quantized model is bigger than initial ONNX model"			)



    @require_torch
    @slow
    def  _lowercase						(self   :   Dict			):
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              UpperCAmelCase_	   =       self._test_export(__a       ,      "pt"       ,      12       ,      **__a			)
              UpperCAmelCase_	   =       quantize(__a			)

              # Ensure the actual quantized model is not bigger than the original one
              if quantized_path.stat().st_size >= Path(__a			).stat().st_size:
                   self.fail("Quantized model is bigger than initial ONNX model"			)



    def  _lowercase						(self   :   str       ,      __a   :   Optional[Any]       ,      __a   :   List[Any]       ,      __a   :   int       ,      __a   :   Tuple=None       ,      **__a   :   Any			):
         try:
              # Compute path
              with TemporaryDirectory() as tempdir:
                   UpperCAmelCase_	   =       Path(__a			).joinpath("model.onnx"			)

              # Remove folder if exists
              if path.parent.exists():
                   path.parent.rmdir()

              # Export
              convert(__a       ,      __a       ,      __a       ,      __a       ,      __a       ,      **__a			)

              return path
         except Exception as e:
              self.fail(__a			)



    @require_torch
    @require_tokenizers
    @slow
    def  _lowercase						(self   :   Optional[Any]			):
         from transformers import BertModel

         UpperCAmelCase_	   =       BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
         UpperCAmelCase_	   =       BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
         self._test_infer_dynamic_axis(__a       ,      __a       ,      "pt"			)



    @require_tf
    @require_tokenizers
    @slow
    def  _lowercase						(self   :   List[Any]			):
         from transformers import TFBertModel

         UpperCAmelCase_	   =       TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
         UpperCAmelCase_	   =       BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
         self._test_infer_dynamic_axis(__a       ,      __a       ,      "tf"			)



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Any       ,      __a   :   Optional[int]       ,      __a   :   Union[str, Any]			):
         UpperCAmelCase_	   =       FeatureExtractionPipeline(__a       ,      __a			)

         UpperCAmelCase_	   =       ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       infer_shapes(__a       ,      __a			)

         # Assert all variables are present
         self.assertEqual(len(__a			)       ,      len(__a			)			)
         self.assertTrue(all(var_name in shapes for var_name in variable_names			)			)
         self.assertSequenceEqual(variable_names[:3]       ,      __a			)
         self.assertSequenceEqual(variable_names[3:]       ,      __a			)

         # Assert inputs are {0: batch, 1: sequence}
         for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
              self.assertDictEqual(shapes[var_name]       ,      {0: "batch", 1: "sequence"}			)

         # Assert outputs are {0: batch, 1: sequence} and {0: batch}
         self.assertDictEqual(shapes["output_0"]       ,      {0: "batch", 1: "sequence"}			)
         self.assertDictEqual(shapes["output_1"]       ,      {0: "batch"}			)



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       ["input_ids", "attention_mask", "token_type_ids"]
         UpperCAmelCase_	   =       {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       ensure_valid_input(FuncContiguousArgs()       ,      __a       ,      __a			)

         # Should have exactly the same number of args (all are valid)
         self.assertEqual(len(__a			)       ,      3			)

         # Should have exactly the same input names
         self.assertEqual(set(__a			)       ,      set(__a			)			)

         # Parameter should be reordered according to their respective place in the function:
         # (input_ids, token_type_ids, attention_mask)
         self.assertEqual(__a       ,      (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])			)

         # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       ensure_valid_input(FuncNonContiguousArgs()       ,      __a       ,      __a			)

         # Should have exactly the one arg (all before the one not provided "some_other_args")
         self.assertEqual(len(__a			)       ,      1			)
         self.assertEqual(len(__a			)       ,      1			)

         # Should have only "input_ids"
         self.assertEqual(inputs_args[0]       ,      tokens["input_ids"]			)
         self.assertEqual(ordered_input_names[0]       ,      "input_ids"			)






    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       generate_identified_filename(Path("/home/something/my_fake_model.onnx"			)       ,      "-test"			)
         self.assertEqual("/home/something/my_fake_model-test.onnx"       ,      generated.as_posix()			)





'''simple docstring'''





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import jax
    import jaxlib

SCREAMING_SNAKE_CASE_:					List[str]  =get_logger()

SCREAMING_SNAKE_CASE_:					Optional[dict]  =None




class 	__A				(			TensorFormatter[Mapping, """jax.Array""", Mapping]     ):



    def __init__(self   :   List[Any]       ,      __a   :   Optional[int]=None       ,      __a   :   Any=None       ,      **__a   :   Dict			):
         super().__init__(features=__a			)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(__a       ,      __a			):
              raise ValueError(
                  f"""Expected {device} to be a `str` not {type(__a			)}, as `jaxlib.xla_extension.Device` """
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."			)
         UpperCAmelCase_	   =       device if isinstance(__a       ,      __a			) else str(jax.devices()[0]			)
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()			):
              logger.warning(
                  f"""Device with string identifier {self.device} not listed among the available """
                  f"""devices: {list(DEVICE_MAPPING.keys()			)}, so falling back to the default """
                  f"""device: {str(jax.devices()[0]			)}."""			)
              UpperCAmelCase_	   =       str(jax.devices()[0]			)
         UpperCAmelCase_	   =       jnp_array_kwargs



    @staticmethod
    def  _lowercase						():
         import jax

         return {str(__a			): device for device in jax.devices()}



    def  _lowercase						(self   :   str       ,      __a   :   Tuple			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      __a			) and column:
              if all(
                  isinstance(__a       ,      jax.Array			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
                   return jnp.stack(__a       ,      axis=0			)
         return column



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[int]			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      (str, bytes, type(__a			))			):
              return value
         elif isinstance(__a       ,      (np.character, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.character			):
              return value.tolist()

         UpperCAmelCase_	   =       {}

         if isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.integer			):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
              else:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
         elif isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.floating			):
              UpperCAmelCase_	   =       {"dtype": jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__a       ,      PIL.Image.Image			):
                   UpperCAmelCase_	   =       np.asarray(__a			)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]			):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__a       ,      **{**default_dtype, **self.jnp_array_kwargs}			)



    def  _lowercase						(self   :   int       ,      __a   :   Any			):
         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__a       ,      torch.Tensor			):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
         if hasattr(__a       ,      "__array__"			) and not isinstance(__a       ,      jax.Array			):
              UpperCAmelCase_	   =       data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(__a       ,      np.ndarray			):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         elif isinstance(__a       ,      (list, tuple)			):
              return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         return self._tensorize(__a			)



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   dict			):
         return map_nested(self._recursive_tensorize       ,      __a       ,      map_list=__a			)



    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_row(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_row(__a			)
         return self.recursive_tensorize(__a			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_column(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_column(__a       ,      pa_table.column_names[0]			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         UpperCAmelCase_	   =       self._consolidate(__a			)
         return column






    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_batch(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_batch(__a			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         for column_name in batch:
              UpperCAmelCase_	   =       self._consolidate(batch[column_name]			)
         return batch




