


"""simple docstring"""


import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    logging.get_logger()

										# the current default level is logging.WARNING
										__lowerCAmelCase       					=    logging.get_verbosity()

										logging.set_verbosity_error()
										self.assertEqual(logger.getEffectiveLevel()					,     logging.get_verbosity()			)

										logging.set_verbosity_warning()
										self.assertEqual(logger.getEffectiveLevel()					,     logging.get_verbosity()			)

										logging.set_verbosity_info()
										self.assertEqual(logger.getEffectiveLevel()					,     logging.get_verbosity()			)

										logging.set_verbosity_debug()
										self.assertEqual(logger.getEffectiveLevel()					,     logging.get_verbosity()			)

										# restore to the original level
										logging.set_verbosity(__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    logging.get_verbosity()

										__lowerCAmelCase       					=    logging.get_logger("transformers.models.bart.tokenization_bart"			)
										__lowerCAmelCase       					=    "Testing 1, 2, 3"

										# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
										if level_origin <= logging.WARNING:
													with CaptureLogger(__a			) as cl:
																logger.warning(__a			)
													self.assertEqual(cl.out					,     msg + "\n"			)

										# this is setting the level for all of `transformers.*` loggers
										logging.set_verbosity_error()

										# should not be able to log warnings
										with CaptureLogger(__a			) as cl:
													logger.warning(__a			)
										self.assertEqual(cl.out					,     ""			)

										# should be able to log warnings again
										logging.set_verbosity_warning()
										with CaptureLogger(__a			) as cl:
													logger.warning(__a			)
										self.assertEqual(cl.out					,     msg + "\n"			)

										# restore to the original level
										logging.set_verbosity(__a			)

							@mockenv(TRANSFORMERS_VERBOSITY="error"			)
							def     snake_case       (  self			):
										# reset for the env var to take effect, next time some logger call is made
										transformers.utils.logging._reset_library_root_logger()
										# this action activates the env var
										__lowerCAmelCase       					=    logging.get_logger("transformers.models.bart.tokenization_bart"			)

										__lowerCAmelCase       					=    os.getenv("TRANSFORMERS_VERBOSITY"					,     __a			)
										__lowerCAmelCase       					=    logging.log_levels[env_level_str]

										__lowerCAmelCase       					=    logging.get_verbosity()
										self.assertEqual(
										    __a					,     __a					,     f"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"					,     )

										# restore to the original level
										__lowerCAmelCase       					=    ""
										transformers.utils.logging._reset_library_root_logger()

							@mockenv(TRANSFORMERS_VERBOSITY="super-error"			)
							def     snake_case       (  self			):
										# reset for the env var to take effect, next time some logger call is made
										transformers.utils.logging._reset_library_root_logger()
										__lowerCAmelCase       					=    logging.logging.getLogger()
										with CaptureLogger(__a			) as cl:
													# this action activates the env var
													logging.get_logger("transformers.models.bart.tokenization_bart"			)
										self.assertIn("Unknown option TRANSFORMERS_VERBOSITY=super-error"					,     cl.out			)

										# no need to restore as nothing was changed



							def     snake_case       (  self			):
										# testing `logger.warning_advice()`
										transformers.utils.logging._reset_library_root_logger()

										__lowerCAmelCase       					=    logging.get_logger("transformers.models.bart.tokenization_bart"			)
										__lowerCAmelCase       					=    "Testing 1, 2, 3"

										with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="1"			):
													# nothing should be logged as env var disables this method
													with CaptureLogger(__a			) as cl:
																logger.warning_advice(__a			)
													self.assertEqual(cl.out					,     ""			)

										with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""			):
													# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
													with CaptureLogger(__a			) as cl:
																logger.warning_advice(__a			)
													self.assertEqual(cl.out					,     msg + "\n"			)


def _lowerCamelCase			(	):

			'''simple docstring'''




			disable_progress_bar()
			assert are_progress_bars_disabled()

			enable_progress_bar()
			assert not are_progress_bars_disabled()








"""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()			)




