
import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel



class       _snake_case   :


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=99					,  SCREAMING_SNAKE_CASE_=13					,  SCREAMING_SNAKE_CASE_=7					,  SCREAMING_SNAKE_CASE_=9					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=32					,  SCREAMING_SNAKE_CASE_=5					,  SCREAMING_SNAKE_CASE_=4					,  SCREAMING_SNAKE_CASE_=37					,  SCREAMING_SNAKE_CASE_=8					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.0_0_2					,  SCREAMING_SNAKE_CASE_=1					,  SCREAMING_SNAKE_CASE_=0					,  SCREAMING_SNAKE_CASE_=0					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  ):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      parent
   lowercase__  :   Optional[Any]    		=      batch_size
   lowercase__  :   int    		=      encoder_seq_length
   lowercase__  :   List[Any]    		=      decoder_seq_length
   # For common tests
   lowercase__  :   Tuple    		=      self.decoder_seq_length
   lowercase__  :   Optional[int]    		=      is_training
   lowercase__  :   Dict    		=      use_attention_mask
   lowercase__  :   Any    		=      use_labels
   lowercase__  :   Optional[Any]    		=      vocab_size
   lowercase__  :   Dict    		=      hidden_size
   lowercase__  :   List[str]    		=      num_hidden_layers
   lowercase__  :   List[Any]    		=      num_attention_heads
   lowercase__  :   str    		=      d_ff
   lowercase__  :   List[str]    		=      relative_attention_num_buckets
   lowercase__  :   Dict    		=      dropout_rate
   lowercase__  :   Optional[int]    		=      initializer_factor
   lowercase__  :   Optional[Any]    		=      eos_token_id
   lowercase__  :   int    		=      pad_token_id
   lowercase__  :   Union[str, Any]    		=      decoder_start_token_id
   lowercase__  :   List[Any]    		=      None
   lowercase__  :   Any    		=      decoder_layers


 def      lowercase__     (					self):


   '''simple docstring'''




   return TaConfig.from_pretrained("""google/umt5-base""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  ):


   '''simple docstring'''




   if attention_mask is None:
     lowercase__  :   Any    		=      input_ids.ne(config.pad_token_id)
   if decoder_attention_mask is None:
     lowercase__  :   List[str]    		=      decoder_input_ids.ne(config.pad_token_id)
   if head_mask is None:
     lowercase__  :   Union[str, Any]    		=      torch.ones(config.num_hidden_layers					,  config.num_attention_heads					,  device=SCREAMING_SNAKE_CASE_)
   if decoder_head_mask is None:
     lowercase__  :   Union[str, Any]    		=      torch.ones(config.num_decoder_layers					,  config.num_attention_heads					,  device=SCREAMING_SNAKE_CASE_)
   if cross_attn_head_mask is None:
     lowercase__  :   Any    		=      torch.ones(
         config.num_decoder_layers					,  config.num_attention_heads					,  device=SCREAMING_SNAKE_CASE_)
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      ids_tensor([self.batch_size, self.encoder_seq_length]					,  self.vocab_size)
   lowercase__  :   List[str]    		=      ids_tensor([self.batch_size, self.decoder_seq_length]					,  self.vocab_size)

   # we need to clamp the input ids here to avoid having pad token in between
   # this is because for NllbMoe the position_ids are prepared such that
   # all pad tokens have pos id = 2 and rest are between 2..seq_length
   # and the seq_length here is seq_length - num_pad_tokens
   # but when using past, there is no way of knowing if the past input ids had
   # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
   # position_ids being off by num_pad_tokens in past input
   lowercase__  :   List[Any]    		=      input_ids.clamp(self.pad_token_id + 1)
   lowercase__  :   str    		=      decoder_input_ids.clamp(self.pad_token_id + 1)

   lowercase__  :   Optional[Any]    		=      self.get_config()
   lowercase__  :   Optional[int]    		=      config.num_attention_heads
   lowercase__  :   str    		=      self.prepare_inputs_dict(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   return config, input_dict


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__		,    lowercase__  :   Tuple    		=      self.prepare_config_and_inputs()
   return config, inputs_dict


 def      lowercase__     (					self):


   '''simple docstring'''




   return TaConfig(
       vocab_size=1_66					,  d_model=self.hidden_size					,  d_ff=self.d_ff					,  d_kv=self.hidden_size // self.num_attention_heads					,  num_layers=self.num_hidden_layers					,  num_decoder_layers=self.decoder_layers					,  num_heads=self.num_attention_heads					,  relative_attention_num_buckets=self.relative_attention_num_buckets					,  dropout_rate=self.dropout_rate					,  initializer_factor=self.initializer_factor					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.pad_token_id					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.decoder_start_token_id					,  )


 def      lowercase__     (					self):


   '''simple docstring'''




   return TaConfig(
       vocab_size=self.vocab_size					,  d_model=self.hidden_size					,  d_ff=self.d_ff					,  d_kv=self.hidden_size // self.num_attention_heads					,  num_layers=self.num_hidden_layers					,  num_decoder_layers=self.decoder_layers					,  num_heads=self.num_attention_heads					,  relative_attention_num_buckets=self.relative_attention_num_buckets					,  dropout_rate=self.dropout_rate					,  initializer_factor=self.initializer_factor					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.pad_token_id					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.decoder_start_token_id					,  )


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      UMTaModel(config=SCREAMING_SNAKE_CASE_)
   model.to(SCREAMING_SNAKE_CASE_)
   model.eval()
   lowercase__  :   List[Any]    		=      model(
       input_ids=SCREAMING_SNAKE_CASE_					,  decoder_input_ids=SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  decoder_attention_mask=SCREAMING_SNAKE_CASE_					,  )
   lowercase__  :   Optional[Any]    		=      model(input_ids=SCREAMING_SNAKE_CASE_					,  decoder_input_ids=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Dict    		=      result.last_hidden_state
   lowercase__  :   List[str]    		=      result.past_key_values
   lowercase__  :   Optional[Any]    		=      result.encoder_last_hidden_state

   self.parent.assertEqual(encoder_output.size()					,  (self.batch_size, self.encoder_seq_length, self.hidden_size))
   self.parent.assertEqual(decoder_output.size()					,  (self.batch_size, self.decoder_seq_length, self.hidden_size))
   # There should be `num_layers` key value embeddings stored in decoder_past
   self.parent.assertEqual(len(SCREAMING_SNAKE_CASE_)					,  config.num_layers)
   # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
   self.parent.assertEqual(len(decoder_past[0])					,  4)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      UMTaModel(config=SCREAMING_SNAKE_CASE_).get_decoder().to(SCREAMING_SNAKE_CASE_).eval()
   # first forward pass
   lowercase__  :   Any    		=      model(SCREAMING_SNAKE_CASE_					,  use_cache=SCREAMING_SNAKE_CASE_)
   lowercase__  :   str    		=      model(SCREAMING_SNAKE_CASE_)
   lowercase__  :   int    		=      model(SCREAMING_SNAKE_CASE_					,  use_cache=SCREAMING_SNAKE_CASE_)

   self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_) == len(SCREAMING_SNAKE_CASE_))
   self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_) == len(SCREAMING_SNAKE_CASE_) + 1)

   lowercase__		,    lowercase__  :   Optional[int]    		=      outputs.to_tuple()

   # create hypothetical next token and extent to next_input_ids
   lowercase__  :   Any    		=      ids_tensor((self.batch_size, 1)					,  config.vocab_size)

   # append to next input_ids and
   lowercase__  :   str    		=      torch.cat([input_ids, next_tokens]					,  dim=-1)

   lowercase__  :   int    		=      model(SCREAMING_SNAKE_CASE_)["""last_hidden_state"""]
   lowercase__  :   Tuple    		=      model(SCREAMING_SNAKE_CASE_					,  past_key_values=SCREAMING_SNAKE_CASE_)["""last_hidden_state"""]

   # select random slice
   lowercase__  :   Optional[Any]    		=      ids_tensor((1,)					,  output_from_past.shape[-1]).item()
   lowercase__  :   Union[str, Any]    		=      output_from_no_past[:, -1, random_slice_idx].detach()
   lowercase__  :   Union[str, Any]    		=      output_from_past[:, 0, random_slice_idx].detach()

   # test that outputs are equal for slice
   self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  atol=1E-3))



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      UMTaModel(config=SCREAMING_SNAKE_CASE_).to(SCREAMING_SNAKE_CASE_).half().eval()
   lowercase__  :   Optional[Any]    		=      model(**SCREAMING_SNAKE_CASE_)["""last_hidden_state"""]
   self.parent.assertFalse(torch.isnan(SCREAMING_SNAKE_CASE_).any().item())



@require_torch
class       _snake_case   (	UpperCAmelCase_  ,						UpperCAmelCase_  ,						UpperCAmelCase_  ,						unittest.TestCase							):
 __lowerCAmelCase  :     int        =			(
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 __lowerCAmelCase  :     Optional[Any]        =			(UMTaForConditionalGeneration,) if is_torch_available() else ()
 __lowerCAmelCase  :     Optional[Any]        =			(
     {
         'conversational': UMTaForConditionalGeneration,
         'feature-extraction': UMTaModel,
         'summarization': UMTaForConditionalGeneration,
         'text2text-generation': UMTaForConditionalGeneration,
         'translation': UMTaForConditionalGeneration,
         'question-answering': UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 __lowerCAmelCase  :     Union[str, Any]        =			True
 __lowerCAmelCase  :     List[str]        =			False
 __lowerCAmelCase  :     Dict        =			False
 __lowerCAmelCase  :     Tuple        =			True
 __lowerCAmelCase  :     Dict        =			True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 __lowerCAmelCase  :     Dict        =			[0.8, 0.9]


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      UMTaModelTester(self)


 @unittest.skip("""Test has a segmentation fault on torch 1.8.0""")
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      self.model_tester.prepare_config_and_inputs()
   lowercase__  :   Union[str, Any]    		=      UMTaModel(config_and_inputs[0]).to(SCREAMING_SNAKE_CASE_)
   with tempfile.TemporaryDirectory() as tmpdirname:
     torch.onnx.export(
         SCREAMING_SNAKE_CASE_					,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])					,  f'{tmpdirname}/t5_test.onnx'					,  export_params=SCREAMING_SNAKE_CASE_					,  opset_version=9					,  input_names=["""input_ids""", """decoder_input_ids"""]					,  )


 @unittest.skipIf(torch_device == """cpu"""					,  """Cant do half precision""")
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model_fpaa_forward(*SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
   lowercase__  :   Union[str, Any]    		=      self.model_tester.prepare_config_and_inputs()
   lowercase__  :   Any    		=      config_and_inputs[0]
   lowercase__  :   List[str]    		=      UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE_).eval()
   model.to(SCREAMING_SNAKE_CASE_)

   lowercase__  :   Union[str, Any]    		=      {
       """head_mask""": torch.zeros(config.num_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_),
       """decoder_head_mask""": torch.zeros(config.num_decoder_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_),
       """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_),
   }

   for attn_name, (name, mask) in zip(SCREAMING_SNAKE_CASE_					,  head_masking.items()):
     lowercase__  :   Optional[Any]    		=      {name: mask}
     # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
     if name == "head_mask":
       lowercase__  :   str    		=      torch.ones(
           config.num_decoder_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_)

     lowercase__  :   Optional[int]    		=      model.generate(
         config_and_inputs[1]["""input_ids"""]					,  num_beams=1					,  max_length=3					,  output_attentions=SCREAMING_SNAKE_CASE_					,  return_dict_in_generate=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_					,  )
     # We check the state of decoder_attentions and cross_attentions just from the last step
     lowercase__  :   Union[str, Any]    		=      out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
     self.assertEqual(sum([w.sum().item() for w in attn_weights])					,  0.0)



 @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""")
 def      lowercase__     (					self):


   '''simple docstring'''




   pass



@require_torch
@require_sentencepiece
@require_tokenizers
class       _snake_case   (	unittest.TestCase							):


 @slow
 @unittest.skip(
     """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""")
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""					,  return_dict=SCREAMING_SNAKE_CASE_).to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Dict    		=      AutoTokenizer.from_pretrained("""google/umt5-small"""					,  use_fast=SCREAMING_SNAKE_CASE_					,  legacy=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Optional[int]    		=      [
       """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
       """No se como puedo <extra_id_0>.""",
       """This is the reason why we <extra_id_0> them.""",
       """The <extra_id_0> walks in <extra_id_1>, seats""",
       """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
   ]
   lowercase__  :   List[str]    		=      tokenizer(SCREAMING_SNAKE_CASE_					,  return_tensors="""pt"""					,  padding=SCREAMING_SNAKE_CASE_).input_ids
   # fmt: off
   lowercase__  :   str    		=      torch.tensor(
       [
           [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
           [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
           [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
           [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
           [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
       ])
   # fmt: on
   torch.testing.assert_allclose(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

   lowercase__  :   Tuple    		=      model.generate(input_ids.to(SCREAMING_SNAKE_CASE_))
   lowercase__  :   str    		=      [
       """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
       """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
       """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
       """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
       """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
   ]
   lowercase__  :   List[str]    		=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_)
   self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)








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       _snake_case   :


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return None



class       _snake_case   :


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return None



class       _snake_case   (	unittest.TestCase							):
 __lowerCAmelCase  :     Optional[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      lowercase__     (					self):


   '''simple docstring'''




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


 @require_torch
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




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


 @require_torch
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   from transformers import BertModel

   lowercase__  :   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()
     lowercase__  :   Optional[int]    		=      BertTokenizerFast(vocab_file.name)

   with TemporaryDirectory() as bert_save_dir:
     lowercase__  :   Union[str, 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      lowercase__     (					self):


   '''simple docstring'''




   for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     lowercase__  :   Any    		=      self._test_export(SCREAMING_SNAKE_CASE_					,  """tf"""					,  12					,  **SCREAMING_SNAKE_CASE_)
     lowercase__  :   Optional[Any]    		=      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      lowercase__     (					self):


   '''simple docstring'''




   for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     lowercase__  :   Tuple    		=      self._test_export(SCREAMING_SNAKE_CASE_					,  """pt"""					,  12					,  **SCREAMING_SNAKE_CASE_)
     lowercase__  :   Dict    		=      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      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   try:
     # Compute path
     with TemporaryDirectory() as tempdir:
       lowercase__  :   List[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      lowercase__     (					self):


   '''simple docstring'''




   from transformers import BertModel

   lowercase__  :   Dict    		=      BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
   lowercase__  :   Union[str, Any]    		=      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      lowercase__     (					self):


   '''simple docstring'''




   from transformers import TFBertModel

   lowercase__  :   Union[str, Any]    		=      TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
   lowercase__  :   Optional[int]    		=      BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
   self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  """tf""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Dict    		=      FeatureExtractionPipeline(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

   lowercase__  :   Union[str, Any]    		=      ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
   lowercase__		,    lowercase__		,    lowercase__		,    lowercase__  :   List[str]    		=      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      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Any    		=      ["""input_ids""", """attention_mask""", """token_type_ids"""]
   lowercase__  :   Tuple    		=      {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
   lowercase__		,    lowercase__  :   Optional[Any]    		=      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)
   lowercase__		,    lowercase__  :   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      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      generate_identified_filename(Path("""/home/something/my_fake_model.onnx""")					,  """-test""")
   self.assertEqual("""/home/something/my_fake_model-test.onnx"""					,  generated.as_posix())






