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  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Tuple     )    ->				Tuple:
         return None



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				Dict:
         return None



class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     List[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 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Dict:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                self._test_export(SCREAMING_SNAKE_CASE__      ,	'tf'      ,	12      ,	**SCREAMING_SNAKE_CASE__     )







  @require_torch
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                self._test_export(SCREAMING_SNAKE_CASE__      ,	'pt'      ,	12      ,	**SCREAMING_SNAKE_CASE__     )







  @require_torch
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[Any]:
         from transformers import BertModel

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

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







  @require_tf
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Any:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                A						:     Optional[int]         =self._test_export(SCREAMING_SNAKE_CASE__      ,	'tf'      ,	12      ,	**SCREAMING_SNAKE_CASE__     )
                A						:     Dict         =quantize(Path(SCREAMING_SNAKE_CASE__     )     )

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







  @require_torch
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Dict:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                A						:     Optional[Any]         =self._test_export(SCREAMING_SNAKE_CASE__      ,	'pt'      ,	12      ,	**SCREAMING_SNAKE_CASE__     )
                A						:     int         =quantize(SCREAMING_SNAKE_CASE__     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	**SCREAMING_SNAKE_CASE__    :					Any     )    ->				int:
         try:
                # Compute path
                with TemporaryDirectory() as tempdir:
                       A						:     Union[str, Any]         =Path(SCREAMING_SNAKE_CASE__     ).joinpath('model.onnx'     )

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

                # Export
                convert(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )

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







  @require_torch
  @require_tokenizers
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Optional[Any]:
         from transformers import BertModel

         A						:     Union[str, Any]         =BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'     )     )
         A						:     Optional[int]         =BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'     )
         self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'pt'     )







  @require_tf
  @require_tokenizers
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Optional[Any]:
         from transformers import TFBertModel

         A						:     str         =TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'     )     )
         A						:     List[str]         =BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'     )
         self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'tf'     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Tuple:
         A						:     Any         =FeatureExtractionPipeline(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         A						:     int         =['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
         A						:     Any         =infer_shapes(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

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

         # 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 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				str:
         A						:     Dict         =['input_ids', 'attention_mask', 'token_type_ids']
         A						:     Optional[Any]         ={'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
         A						:     Optional[int]         =ensure_valid_input(FuncContiguousArgs()      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

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

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

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

         # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
         A						:     List[Any]         =ensure_valid_input(FuncNonContiguousArgs()      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

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

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				List[str]:
         A						:     str         =generate_identified_filename(Path('/home/something/my_fake_model.onnx'     )      ,	'-test'     )
         self.assertEqual('/home/something/my_fake_model-test.onnx'      ,	generated.as_posix()     )







_lowercase							:      Dict      							='''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
    from .utils import rich






