




'''simple docstring'''



import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			Union[str, Any]:
   if model.config.model_type == "gpt2":
      return model.transformer.h[0].mlp.c_fc
   return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
 import torch
 import torch.nn as nn







 class 		lowercase_   (			nn.Module       ):


    """simple docstring"""

    def __init__(   self :     Optional[int]  ,				__lowerCamelCase :     nn.Module  ,				__lowerCamelCase :     int   ):

       """simple docstring"""
       super().__init__()
       _SCREAMING_SNAKE_CASE	=				module
       _SCREAMING_SNAKE_CASE	=				nn.Sequential(
           nn.Linear(module.in_features  ,				__lowerCamelCase  ,				bias=__lowerCamelCase   )  ,				nn.Linear(__lowerCamelCase  ,				module.out_features  ,				bias=__lowerCamelCase   )  ,				)
       _SCREAMING_SNAKE_CASE	=				(2.0 / (5 * min(module.in_features  ,				module.out_features   ))) ** 0.5
       nn.init.normal_(self.adapter[0].weight  ,				std=__lowerCamelCase   )
       nn.init.zeros_(self.adapter[1].weight   )
       self.adapter.to(module.weight.device   )



    def 							lowerCAmelCase_      (   self :     str  ,				__lowerCamelCase :     List[Any]  ,				*__lowerCamelCase :     Optional[int]  ,				**__lowerCamelCase :     Optional[int]   ):

       """simple docstring"""
       return self.module(__lowerCamelCase  ,				*__lowerCamelCase  ,				**__lowerCamelCase   ) + self.adapter(__lowerCamelCase   )







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						'''bigscience/bloom-1b7'''

   # Constant values
   lowerCamelCase_      =						2.109_6595_5269_2574

   lowerCamelCase_      =						'''Hello my name is'''
   lowerCamelCase_      =						set()
   EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I'''       )
   EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n'''       )
   EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University'''       )
   lowerCamelCase_      =						10



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      # Models and tokenizer
      _SCREAMING_SNAKE_CASE	=				AutoTokenizer.from_pretrained(self.model_name   )







class 		lowercase_   (			A       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      super().setUp()

      # Models and tokenizer
      _SCREAMING_SNAKE_CASE	=				AutoModelForCausalLM.from_pretrained(
          self.model_name  ,				torch_dtype=torch.floataa  ,				device_map="auto"   )
      _SCREAMING_SNAKE_CASE	=				AutoModelForCausalLM.from_pretrained(self.model_name  ,				load_in_abit=__lowerCamelCase  ,				device_map="auto"   )



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      del self.model_fpaa
      del self.model_abit

      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_abit.config

      self.assertTrue(hasattr(__lowerCamelCase  ,				"quantization_config"   )   )

      _SCREAMING_SNAKE_CASE	=				config.to_dict()
      _SCREAMING_SNAKE_CASE	=				config.to_diff_dict()

      _SCREAMING_SNAKE_CASE	=				config.to_json_string()



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      from bitsandbytes.nn import Paramsabit

      _SCREAMING_SNAKE_CASE	=				self.model_fpaa.get_memory_footprint()
      _SCREAMING_SNAKE_CASE	=				self.model_abit.get_memory_footprint()

      self.assertAlmostEqual(mem_fpaa / mem_abit  ,				self.EXPECTED_RELATIVE_DIFFERENCE   )
      _SCREAMING_SNAKE_CASE	=				get_some_linear_layer(self.model_abit   )
      self.assertTrue(linear.weight.__class__ == Paramsabit   )



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      from transformers import TaPreTrainedModel

      self.model_fpaa.get_memory_footprint()
      self.model_abit.get_memory_footprint()

      for name, module in self.model_abit.named_modules():
         if isinstance(__lowerCamelCase  ,				torch.nn.Linear   ):
            if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
               # 4-bit parameters are packed in uint8 variables
               self.assertTrue(module.weight.dtype == torch.uinta   )



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.tokenizer(self.input_text  ,				return_tensors="pt"   )
      _SCREAMING_SNAKE_CASE	=				self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0   )  ,				max_new_tokens=1_0   )

      self.assertIn(self.tokenizer.decode(output_sequences[0]  ,				skip_special_tokens=__lowerCamelCase   )  ,				self.EXPECTED_OUTPUTS   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BitsAndBytesConfig()
      _SCREAMING_SNAKE_CASE	=				True

      _SCREAMING_SNAKE_CASE	=				AutoModelForCausalLM.from_pretrained(
          self.model_name  ,				quantization_config=__lowerCamelCase  ,				device_map="auto"   )

      _SCREAMING_SNAKE_CASE	=				self.tokenizer(self.input_text  ,				return_tensors="pt"   )
      _SCREAMING_SNAKE_CASE	=				model_abit_from_config.generate(
          input_ids=encoded_input["input_ids"].to(0   )  ,				max_new_tokens=1_0   )

      self.assertIn(self.tokenizer.decode(output_sequences[0]  ,				skip_special_tokens=__lowerCamelCase   )  ,				self.EXPECTED_OUTPUTS   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      with self.assertRaises(__lowerCamelCase   ), tempfile.TemporaryDirectory() as tmpdirname:
         self.model_abit.save_pretrained(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BitsAndBytesConfig()

      with self.assertRaises(__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				AutoModelForCausalLM.from_pretrained(
             self.model_name  ,				quantization_config=__lowerCamelCase  ,				load_in_abit=__lowerCamelCase  ,				device_map="auto"  ,				bnb_abit_quant_type="nf4"  ,				)



   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      with self.assertRaises(__lowerCamelCase   ):
         # Tries with `str`
         self.model_abit.to("cpu"   )

      with self.assertRaises(__lowerCamelCase   ):
         # Tries with a `dtype``
         self.model_abit.to(torch.floataa   )

      with self.assertRaises(__lowerCamelCase   ):
         # Tries with a `device`
         self.model_abit.to(torch.device("cuda:0"   )   )

      with self.assertRaises(__lowerCamelCase   ):
         # Tries with a `device`
         self.model_abit.float()

      with self.assertRaises(__lowerCamelCase   ):
         # Tries with a `device`
         self.model_abit.half()

      # Test if we did not break anything
      _SCREAMING_SNAKE_CASE	=				self.tokenizer(self.input_text  ,				return_tensors="pt"   )

      _SCREAMING_SNAKE_CASE	=				self.model_fpaa.to(torch.floataa   )
      _SCREAMING_SNAKE_CASE	=				self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0   )  ,				max_new_tokens=1_0   )

      # Check this does not throw an error
      _SCREAMING_SNAKE_CASE	=				self.model_fpaa.to("cpu"   )

      # Check this does not throw an error
      _SCREAMING_SNAKE_CASE	=				self.model_fpaa.half()

      # Check this does not throw an error
      _SCREAMING_SNAKE_CASE	=				self.model_fpaa.float()



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				AutoModelForSeqaSeqLM.from_pretrained("t5-small"  ,				load_in_abit=__lowerCamelCase  ,				device_map="auto"   )
      self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa   )







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   @classmethod
   def 							lowerCAmelCase_      (   cls :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"t5-small"
      _SCREAMING_SNAKE_CASE	=				"google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
      _SCREAMING_SNAKE_CASE	=				AutoTokenizer.from_pretrained(cls.model_name   )
      _SCREAMING_SNAKE_CASE	=				"Translate in German: Hello, my dog is cute"



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      from transformers import TaForConditionalGeneration

      _SCREAMING_SNAKE_CASE	=				TaForConditionalGeneration._keep_in_fpaa_modules
      _SCREAMING_SNAKE_CASE	=				None

      # test with `t5-small`
      _SCREAMING_SNAKE_CASE	=				TaForConditionalGeneration.from_pretrained(self.model_name  ,				load_in_abit=__lowerCamelCase  ,				device_map="auto"   )
      _SCREAMING_SNAKE_CASE	=				self.tokenizer(self.input_text  ,				return_tensors="pt"   ).to(0   )
      _SCREAMING_SNAKE_CASE	=				model.generate(**__lowerCamelCase   )

      # test with `flan-t5-small`
      _SCREAMING_SNAKE_CASE	=				TaForConditionalGeneration.from_pretrained(
          self.dense_act_model_name  ,				load_in_abit=__lowerCamelCase  ,				device_map="auto"   )
      _SCREAMING_SNAKE_CASE	=				self.tokenizer(self.input_text  ,				return_tensors="pt"   ).to(0   )
      _SCREAMING_SNAKE_CASE	=				model.generate(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				modules



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      import bitsandbytes as bnb

      from transformers import TaForConditionalGeneration

      # test with `t5-small`
      _SCREAMING_SNAKE_CASE	=				TaForConditionalGeneration.from_pretrained(self.model_name  ,				load_in_abit=__lowerCamelCase  ,				device_map="auto"   )

      # there was a bug with decoders - this test checks that it is fixed
      self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q  ,				bnb.nn.Linearabit   )   )

      _SCREAMING_SNAKE_CASE	=				self.tokenizer(self.input_text  ,				return_tensors="pt"   ).to(0   )
      _SCREAMING_SNAKE_CASE	=				model.generate(**__lowerCamelCase   )

      # test with `flan-t5-small`
      _SCREAMING_SNAKE_CASE	=				TaForConditionalGeneration.from_pretrained(
          self.dense_act_model_name  ,				load_in_abit=__lowerCamelCase  ,				device_map="auto"   )
      _SCREAMING_SNAKE_CASE	=				self.tokenizer(self.input_text  ,				return_tensors="pt"   ).to(0   )
      _SCREAMING_SNAKE_CASE	=				model.generate(**__lowerCamelCase   )







class 		lowercase_   (			A       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      super().setUp()
      # model_name
      _SCREAMING_SNAKE_CASE	=				"bigscience/bloom-560m"
      _SCREAMING_SNAKE_CASE	=				"t5-small"

      # Different types of model

      _SCREAMING_SNAKE_CASE	=				AutoModel.from_pretrained(self.model_name  ,				load_in_abit=__lowerCamelCase  ,				device_map="auto"   )
      # Sequence classification model
      _SCREAMING_SNAKE_CASE	=				AutoModelForSequenceClassification.from_pretrained(
          self.model_name  ,				load_in_abit=__lowerCamelCase  ,				device_map="auto"   )
      # CausalLM model
      _SCREAMING_SNAKE_CASE	=				AutoModelForCausalLM.from_pretrained(self.model_name  ,				load_in_abit=__lowerCamelCase  ,				device_map="auto"   )
      # Seq2seq model
      _SCREAMING_SNAKE_CASE	=				AutoModelForSeqaSeqLM.from_pretrained(
          self.seq_to_seq_name  ,				load_in_abit=__lowerCamelCase  ,				device_map="auto"   )



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      del self.base_model
      del self.sequence_model
      del self.model_abit
      del self.seq_to_seq_model

      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      from bitsandbytes.nn import Paramsabit

      self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit   )

      # Other heads should be nn.Parameter
      self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter   )
      self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter   )
      self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter   )







class 		lowercase_   (			A       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      super().setUp()



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      del self.pipe

      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				pipeline(
          "text-generation"  ,				model=self.model_name  ,				model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa}  ,				max_new_tokens=self.MAX_NEW_TOKENS  ,				)

      # Real second forward pass
      _SCREAMING_SNAKE_CASE	=				self.pipe(self.input_text   )
      self.assertIn(pipeline_output[0]["generated_text"]  ,				self.EXPECTED_OUTPUTS   )







@require_torch_multi_gpu
class 		lowercase_   (			A       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      super().setUp()



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				AutoModelForCausalLM.from_pretrained(
          self.model_name  ,				load_in_abit=__lowerCamelCase  ,				device_map="balanced"   )

      # Check correct device map
      self.assertEqual(set(model_parallel.hf_device_map.values()   )  ,				{0, 1}   )

      # Check that inference pass works on the model
      _SCREAMING_SNAKE_CASE	=				self.tokenizer(self.input_text  ,				return_tensors="pt"   )

      # Second real batch
      _SCREAMING_SNAKE_CASE	=				model_parallel.generate(input_ids=encoded_input["input_ids"].to(0   )  ,				max_new_tokens=1_0   )
      self.assertIn(self.tokenizer.decode(output_parallel[0]  ,				skip_special_tokens=__lowerCamelCase   )  ,				self.EXPECTED_OUTPUTS   )







class 		lowercase_   (			A       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"facebook/opt-350m"
      super().setUp()



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      if version.parse(importlib.metadata.version("bitsandbytes"   )   ) < version.parse("0.37.0"   ):
         return

      # Step 1: freeze all parameters
      _SCREAMING_SNAKE_CASE	=				AutoModelForCausalLM.from_pretrained(self.model_name  ,				load_in_abit=__lowerCamelCase   )

      self.assertEqual(set(model.hf_device_map.values()   )  ,				{torch.cuda.current_device()}   )

      for param in model.parameters():
         _SCREAMING_SNAKE_CASE	=				False  # freeze the model - train adapters later
         if param.ndim == 1:
            # cast the small parameters (e.g. layernorm) to fp32 for stability
            _SCREAMING_SNAKE_CASE	=				param.data.to(torch.floataa   )

        # Step 2: add adapters
      for _, module in model.named_modules():
         if "OPTAttention" in repr(type(__lowerCamelCase   )   ):
            _SCREAMING_SNAKE_CASE	=				LoRALayer(module.q_proj  ,				rank=1_6   )
            _SCREAMING_SNAKE_CASE	=				LoRALayer(module.k_proj  ,				rank=1_6   )
            _SCREAMING_SNAKE_CASE	=				LoRALayer(module.v_proj  ,				rank=1_6   )

        # Step 3: dummy batch
      _SCREAMING_SNAKE_CASE	=				self.tokenizer("Test batch "  ,				return_tensors="pt"   ).to(0   )

      # Step 4: Check if the gradient is not None
      with torch.cuda.amp.autocast():
         _SCREAMING_SNAKE_CASE	=				model.forward(**__lowerCamelCase   )
         out.logits.norm().backward()

      for module in model.modules():
         if isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
            self.assertTrue(module.adapter[1].weight.grad is not None   )
            self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0   )
         elif isinstance(__lowerCamelCase  ,				nn.Embedding   ):
            self.assertTrue(module.weight.grad is None   )







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''gpt2-xl'''
   lowerCamelCase_      =						3.3191_8548_5415_2187







'''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 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      return None







class 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      return None







class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						[
       # (model_name, model_kwargs)
       ('''bert-base-cased''', {}),
       ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
   ]



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      from transformers import BertModel

      _SCREAMING_SNAKE_CASE	=				["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
      with NamedTemporaryFile(mode="w+t"   ) as vocab_file:
         vocab_file.write("\n".join(__lowerCamelCase   )   )
         vocab_file.flush()
         _SCREAMING_SNAKE_CASE	=				BertTokenizerFast(vocab_file.name   )

      with TemporaryDirectory() as bert_save_dir:
         _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig(vocab_size=len(__lowerCamelCase   )   )   )
         model.save_pretrained(__lowerCamelCase   )
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				__lowerCamelCase   )



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(Path(__lowerCamelCase   )   )

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



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(__lowerCamelCase   )

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



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Optional[int]=None  ,				**__lowerCamelCase :     int   ):

      """simple docstring"""
      try:
         # Compute path
         with TemporaryDirectory() as tempdir:
            _SCREAMING_SNAKE_CASE	=				Path(__lowerCamelCase   ).joinpath("model.onnx"   )

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

         # Export
         convert(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   )

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



   @require_torch
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      from transformers import BertModel

      _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"pt"   )



   @require_tf
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      from transformers import TFBertModel

      _SCREAMING_SNAKE_CASE	=				TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"tf"   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				FeatureExtractionPipeline(__lowerCamelCase  ,				__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				infer_shapes(__lowerCamelCase  ,				__lowerCamelCase   )

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

      # 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 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				["input_ids", "attention_mask", "token_type_ids"]
      _SCREAMING_SNAKE_CASE	=				{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				ensure_valid_input(FuncContiguousArgs()  ,				__lowerCamelCase  ,				__lowerCamelCase   )

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

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

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

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

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

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



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_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()   )

