




'''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     A_					(						self       :   Optional[int]			,							lowercase_       :   Optional[int]			,							lowercase_       :   List[str]			,							lowercase_       :   Union[str, Any]   ):
									return None







class 		a							:


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Optional[int]			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[str]   ):
									return None







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


				@require_tf
				@slow
				def     A_					(						self       :   str   ):
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														self._test_export(lowercase_			,							'''tf'''			,							12			,							**lowercase_   )


				@require_torch
				@slow
				def     A_					(						self       :   List[str]   ):
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														self._test_export(lowercase_			,							'''pt'''			,							12			,							**lowercase_   )


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

									snake_case_		     =							['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
									with NamedTemporaryFile(mode='''w+t'''   ) as vocab_file:
														vocab_file.write('''\n'''.join(lowercase_   )   )
														vocab_file.flush()
														snake_case_		     =							BertTokenizerFast(vocab_file.name   )

									with TemporaryDirectory() as bert_save_dir:
														snake_case_		     =							BertModel(BertConfig(vocab_size=len(lowercase_   )   )   )
														model.save_pretrained(lowercase_   )
														self._test_export(lowercase_			,							'''pt'''			,							12			,							lowercase_   )


				@require_tf
				@slow
				def     A_					(						self       :   Optional[int]   ):
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														snake_case_		     =							self._test_export(lowercase_			,							'''tf'''			,							12			,							**lowercase_   )
														snake_case_		     =							quantize(Path(lowercase_   )   )

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


				@require_torch
				@slow
				def     A_					(						self       :   str   ):
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														snake_case_		     =							self._test_export(lowercase_			,							'''pt'''			,							12			,							**lowercase_   )
														snake_case_		     =							quantize(lowercase_   )

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


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Optional[int]			,							lowercase_       :   List[Any]			,							lowercase_       :   Dict			,							lowercase_       :   Optional[int]=None			,							**lowercase_       :   Optional[Any]   ):
									try:
														# Compute path
														with TemporaryDirectory() as tempdir:
																			snake_case_		     =							Path(lowercase_   ).joinpath('''model.onnx'''   )

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

														# Export
														convert(lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							**lowercase_   )

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


				@require_torch
				@require_tokenizers
				@slow
				def     A_					(						self       :   int   ):
									from transformers import BertModel

									snake_case_		     =							BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''   )   )
									snake_case_		     =							BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''   )
									self._test_infer_dynamic_axis(lowercase_			,							lowercase_			,							'''pt'''   )


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

									snake_case_		     =							TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''   )   )
									snake_case_		     =							BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''   )
									self._test_infer_dynamic_axis(lowercase_			,							lowercase_			,							'''tf'''   )


				def     A_					(						self       :   Tuple			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Any			,							lowercase_       :   Optional[int]   ):
									snake_case_		     =							FeatureExtractionPipeline(lowercase_			,							lowercase_   )

									snake_case_		     =							['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
									snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							infer_shapes(lowercase_			,							lowercase_   )

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

									# 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     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							['''input_ids''', '''attention_mask''', '''token_type_ids''']
									snake_case_		     =							{'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
									snake_case_     ,snake_case_		     =							ensure_valid_input(FuncContiguousArgs()			,							lowercase_			,							lowercase_   )

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

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

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

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

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

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


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							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'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a    :							Tuple										=    {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a    :							str										=    [
							    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'EncodecModel',
							    'EncodecPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_encodec import (
							    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    EncodecConfig,
							)
							from .feature_extraction_encodec import EncodecFeatureExtractor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_encodec import (
														    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EncodecModel,
														    EncodecPreTrainedModel,
														)

else:
							import sys

							a    :							List[Any]										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

