


"""simple docstring"""


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

A  :     str    					=    get_logger()

A  :     Optional[dict]    					=    None
class 						_UpperCamelCase						(     TensorFormatter[Mapping, """jax.Array""", Mapping]	):
							'''simple docstring'''







							def __init__(  self					,     __a=None					,     __a=None					,     **__a			):
										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`."			)
										__lowerCAmelCase       					=    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:
													__lowerCAmelCase       					=    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]			)}."			)
													__lowerCAmelCase       					=    str(jax.devices()[0]			)
										__lowerCAmelCase       					=    jnp_array_kwargs

							@staticmethod
							def     snake_case       (  ):
										import jax

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

							def     snake_case       (  self					,     __a			):
										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     snake_case       (  self					,     __a			):
										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()

										__lowerCAmelCase       					=    {}

										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:
																__lowerCAmelCase       					=    {"dtype": jnp.intaa}
													else:
																__lowerCAmelCase       					=    {"dtype": jnp.intaa}
										elif isinstance(__a					,     (np.number, np.ndarray)			) and np.issubdtype(value.dtype					,     np.floating			):
													__lowerCAmelCase       					=    {"dtype": jnp.floataa}
										elif config.PIL_AVAILABLE and "PIL" in sys.modules:
													import PIL.Image

													if isinstance(__a					,     PIL.Image.Image			):
																__lowerCAmelCase       					=    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:
													__lowerCAmelCase       					=    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     snake_case       (  self					,     __a			):
										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			):
													__lowerCAmelCase       					=    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     snake_case       (  self					,     __a			):
										return map_nested(self._recursive_tensorize					,     __a					,     map_list=__a			)

							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    self.numpy_arrow_extractor().extract_row(__a			)
										__lowerCAmelCase       					=    self.python_features_decoder.decode_row(__a			)
										return self.recursive_tensorize(__a			)

							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    self.numpy_arrow_extractor().extract_column(__a			)
										__lowerCAmelCase       					=    self.python_features_decoder.decode_column(__a					,     pa_table.column_names[0]			)
										__lowerCAmelCase       					=    self.recursive_tensorize(__a			)
										__lowerCAmelCase       					=    self._consolidate(__a			)
										return column



							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    self.numpy_arrow_extractor().extract_batch(__a			)
										__lowerCAmelCase       					=    self.python_features_decoder.decode_batch(__a			)
										__lowerCAmelCase       					=    self.recursive_tensorize(__a			)
										for column_name in batch:
													__lowerCAmelCase       					=    self._consolidate(batch[column_name]			)
										return batch








"""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 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Tuple          =[
							    # (model_name, model_kwargs)
							    ("""bert-base-cased""", {}),
							    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
							]

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

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

							@require_torch
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    ["[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()
													__lowerCAmelCase       					=    BertTokenizerFast(vocab_file.name			)

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

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "tf"					,     12					,     **__a			)
													__lowerCAmelCase       					=    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     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "pt"					,     12					,     **__a			)
													__lowerCAmelCase       					=    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     snake_case       (  self					,     __a					,     __a					,     __a					,     __a=None					,     **__a			):
										try:
													# Compute path
													with TemporaryDirectory() as tempdir:
																__lowerCAmelCase       					=    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     snake_case       (  self			):
										from transformers import BertModel

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

							@require_tf
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import TFBertModel

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

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    FeatureExtractionPipeline(__a					,     __a			)

										__lowerCAmelCase       					=    ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    ["input_ids", "attention_mask", "token_type_ids"]
										__lowerCAmelCase       					=    {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
										__lowerCAmelCase					,			__lowerCAmelCase       					=    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)
										__lowerCAmelCase					,			__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    generate_identified_filename(Path("/home/something/my_fake_model.onnx"			)					,     "-test"			)
										self.assertEqual("/home/something/my_fake_model-test.onnx"					,     generated.as_posix()			)




