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:



 '''simple docstring'''


 def        a__				(       self      :						Any		,							A_      :						int		,							A_      :						Optional[Any]		,							A_      :						Tuple						)     ->			str:

       """simple docstring"""






       return None







class 						A:



 '''simple docstring'''


 def        a__				(       self      :						List[str]		,							A_      :						Tuple		,							A_      :						List[Any]		,							A_      :						Union[str, Any]		,							A_      :						int						)     ->			List[Any]:

       """simple docstring"""






       return None







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		[
     # (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      :						Any						)     ->			List[str]:

       """simple docstring"""






       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(A_		,							'tf'		,							12		,							**A_						)

 @require_torch
 @slow
 def        a__				(       self      :						int						)     ->			Tuple:

       """simple docstring"""






       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(A_		,							'pt'		,							12		,							**A_						)

 @require_torch
 @slow
 def        a__				(       self      :						Tuple						)     ->			Dict:

       """simple docstring"""






       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        a__				(       self      :						Dict						)     ->			List[Any]:

       """simple docstring"""






       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        a__				(       self      :						Dict						)     ->			str:

       """simple docstring"""






       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        a__				(       self      :						Optional[int]		,							A_      :						List[str]		,							A_      :						List[Any]		,							A_      :						Optional[Any]		,							A_      :						Any=None		,							**A_      :						Any						)     ->			List[str]:

       """simple docstring"""






       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        a__				(       self      :						List[str]						)     ->			List[Any]:

       """simple docstring"""






       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        a__				(       self      :						Optional[int]						)     ->			List[Any]:

       """simple docstring"""






       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        a__				(       self      :						Any		,							A_      :						str		,							A_      :						Union[str, Any]		,							A_      :						Optional[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					FeatureExtractionPipeline(A_		,							A_						)

       lowerCamelCase_	  =					["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
       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        a__				(       self      :						Tuple						)     ->			Optional[Any]:

       """simple docstring"""






       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_	  =					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_	  =					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        a__				(       self      :						Any						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					generate_identified_filename(Path('/home/something/my_fake_model.onnx'						)		,							'-test'						)
       self.assertEqual('/home/something/my_fake_model-test.onnx'		,							generated.as_posix()						)









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
 import tensorflow as tf
 from tensorflow.python.eager import context
 from tensorflow.python.framework import ops

 from transformers import GradientAccumulator, create_optimizer







@require_tf
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Optional[int]		,							A_      :						Tuple		,							A_      :						str		,							A_      :						int						)     ->			Any:

       """simple docstring"""






       self.assertEqual(len(A_						)		,							len(A_						)						)
       for a, b in zip(A_		,							A_						):
             self.assertAlmostEqual(A_		,							A_		,							delta=A_						)

 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]						)]						)
       accumulator([tf.constant([-2.0, 1.0]						)]						)
       accumulator([tf.constant([-1.0, 2.0]						)]						)
       with self.assertRaises(A_						):
             accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
       self.assertEqual(accumulator.step		,							3						)
       self.assertEqual(len(accumulator.gradients						)		,							1						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[-2.0, 5.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[0.0, 0.0]		,							tol=1E-2						)





 def        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					None
       ops.enable_eager_execution_internal()
       lowerCamelCase_	  =					tf.config.list_physical_devices('CPU'						)
       if len(A_						) == 1:
             tf.config.set_logical_device_configuration(
                 physical_devices[0]		,							[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       lowerCamelCase_	  =					tf.config.list_logical_devices(device_type='CPU'						)
       lowerCamelCase_	  =					tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
             lowerCamelCase_	  =					GradientAccumulator()
             lowerCamelCase_	  =					tf.Variable([4.0, 3.0]						)
             lowerCamelCase_  ,       lowerCamelCase_	  =					create_optimizer(5E-5		,							10		,							5						)
             lowerCamelCase_	  =					tf.Variable([0.0, 0.0]		,							trainable=A_						)

       def accumulate_on_replica(A_      :						Any						):
             accumulator([gradient]						)

       def apply_on_replica():
             optimizer.apply_gradients(list(zip(accumulator.gradients		,							[variable]						)						)						)

       @tf.function
       def accumulate(A_      :						List[Any]		,							A_      :						Tuple						):
             with strategy.scope():
                   lowerCamelCase_	  =					strategy.experimental_local_results(A_						)
                   local_variables[0].assign(A_						)
                   local_variables[1].assign(A_						)
                   strategy.run(A_		,							args=(gradient_placeholder,)						)

       @tf.function
       def apply_grad():
             with strategy.scope():
                   strategy.run(A_						)

       def _check_local_values(A_      :						List[Any]		,							A_      :						str						):
             lowerCamelCase_	  =					strategy.experimental_local_results(accumulator._gradients[0]						)
             self.assertListAlmostEqual(values[0].value()		,							A_		,							tol=1E-2						)
             self.assertListAlmostEqual(values[1].value()		,							A_		,							tol=1E-2						)

       accumulate([1.0, 2.0]		,							[-1.0, 1.0]						)
       accumulate([3.0, -1.0]		,							[-1.0, -1.0]						)
       accumulate([-2.0, 2.0]		,							[3.0, -2.0]						)
       self.assertEqual(accumulator.step		,							3						)
       _check_local_values([2.0, 3.0]		,							[1.0, -2.0]						)
       apply_grad()
       self.assertListAlmostEqual(variable.value()		,							[4.0, 3.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       _check_local_values([0.0, 0.0]		,							[0.0, 0.0]						)



