


"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    inspect.getfile(accelerate.test_utils			)
										__lowerCAmelCase       					=    os.path.sep.join(
										    mod_file.split(os.path.sep			)[:-1] + ["scripts", "external_deps", "test_metrics.py"]			)

										from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

										__lowerCAmelCase       					=    test_metrics

							@require_cpu
							def     snake_case       (  self			):
										debug_launcher(self.test_metrics.main					,     num_processes=1			)

							@require_cpu
							def     snake_case       (  self			):
										debug_launcher(self.test_metrics.main			)

							@require_single_gpu
							def     snake_case       (  self			):
										self.test_metrics.main()



							@require_multi_gpu
							def     snake_case       (  self			):
										print(f"Found {torch.cuda.device_count()} devices."			)
										__lowerCAmelCase       					=    ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
										with patch_environment(omp_num_threads=1			):
													execute_subprocess_async(__a					,     env=os.environ.copy()			)








"""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()			)




