




'''simple docstring'''



import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
 import torch

 from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
 from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			List[str]	,					__A    :			Dict	,					__A    :			str=None	,					__A    :			Any=None	,					__A    :			List[Any]=None	,					__A    :			List[Any]=None	,					__A    :			List[Any]=None	,					)			->			Any:





   if attention_mask is None:
      _SCREAMING_SNAKE_CASE	=				input_ids.ne(config.pad_token_id  )
   if decoder_attention_mask is None:
      _SCREAMING_SNAKE_CASE	=				decoder_input_ids.ne(config.pad_token_id  )
   if head_mask is None:
      _SCREAMING_SNAKE_CASE	=				torch.ones(config.encoder_layers	,					config.encoder_attention_heads	,					device=a__  )
   if decoder_head_mask is None:
      _SCREAMING_SNAKE_CASE	=				torch.ones(config.decoder_layers	,					config.decoder_attention_heads	,					device=a__  )
   if cross_attn_head_mask is None:
      _SCREAMING_SNAKE_CASE	=				torch.ones(config.decoder_layers	,					config.decoder_attention_heads	,					device=a__  )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     int=1_3  ,				__lowerCamelCase :     Union[str, Any]=7  ,				__lowerCamelCase :     Optional[int]=True  ,				__lowerCamelCase :     Union[str, Any]=False  ,				__lowerCamelCase :     int=9_9  ,				__lowerCamelCase :     List[Any]=1_6  ,				__lowerCamelCase :     Union[str, Any]=2  ,				__lowerCamelCase :     str=4  ,				__lowerCamelCase :     Dict=4  ,				__lowerCamelCase :     List[str]="relu"  ,				__lowerCamelCase :     Tuple=0.1  ,				__lowerCamelCase :     Dict=0.1  ,				__lowerCamelCase :     int=0.0  ,				__lowerCamelCase :     Optional[int]=0.0  ,				__lowerCamelCase :     str=2_0  ,				__lowerCamelCase :     Optional[Any]=2  ,				__lowerCamelCase :     List[str]=1  ,				__lowerCamelCase :     Optional[Any]=0  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				parent
      _SCREAMING_SNAKE_CASE	=				batch_size
      _SCREAMING_SNAKE_CASE	=				seq_length
      _SCREAMING_SNAKE_CASE	=				is_training
      _SCREAMING_SNAKE_CASE	=				use_labels
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				encoder_layerdrop
      _SCREAMING_SNAKE_CASE	=				decoder_layerdrop
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				eos_token_id
      _SCREAMING_SNAKE_CASE	=				pad_token_id
      _SCREAMING_SNAKE_CASE	=				bos_token_id



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size   )
      _SCREAMING_SNAKE_CASE	=				self.eos_token_id  # Eos Token
      _SCREAMING_SNAKE_CASE	=				ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size   )

      # we need to clamp the input ids here to avoid having pad token in between
      # this is because for M2M100 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
      _SCREAMING_SNAKE_CASE	=				input_ids.clamp(self.pad_token_id + 1   )
      _SCREAMING_SNAKE_CASE	=				decoder_input_ids.clamp(self.pad_token_id + 1   )

      _SCREAMING_SNAKE_CASE	=				self.get_config()
      _SCREAMING_SNAKE_CASE	=				prepare_mam_aaa_inputs_dict(__lowerCAmelCase  ,				__lowerCAmelCase  ,				__lowerCAmelCase   )
      return config, inputs_dict



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      return MaMaaaConfig(
          vocab_size=self.vocab_size  ,				d_model=self.hidden_size  ,				encoder_layers=self.num_hidden_layers  ,				decoder_layers=self.num_hidden_layers  ,				encoder_attention_heads=self.num_attention_heads  ,				decoder_attention_heads=self.num_attention_heads  ,				encoder_ffn_dim=self.intermediate_size  ,				decoder_ffn_dim=self.intermediate_size  ,				dropout=self.hidden_dropout_prob  ,				attention_dropout=self.attention_probs_dropout_prob  ,				encoder_layerdrop=self.encoder_layerdrop  ,				decoder_layerdrop=self.decoder_layerdrop  ,				max_position_embeddings=self.max_position_embeddings  ,				eos_token_id=self.eos_token_id  ,				bos_token_id=self.bos_token_id  ,				pad_token_id=self.pad_token_id  ,				)



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.prepare_config_and_inputs()
      return config, inputs_dict



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MaMaaaModel(config=__lowerCAmelCase   ).get_decoder().to(__lowerCAmelCase   ).eval()
      _SCREAMING_SNAKE_CASE	=				inputs_dict["input_ids"]
      _SCREAMING_SNAKE_CASE	=				inputs_dict["attention_mask"]
      _SCREAMING_SNAKE_CASE	=				inputs_dict["head_mask"]

      # first forward pass
      _SCREAMING_SNAKE_CASE	=				model(__lowerCAmelCase  ,				attention_mask=__lowerCAmelCase  ,				head_mask=__lowerCAmelCase  ,				use_cache=__lowerCAmelCase   )

      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				outputs.to_tuple()

      # create hypothetical multiple next token and extent to next_input_ids
      _SCREAMING_SNAKE_CASE	=				ids_tensor((self.batch_size, 3)  ,				config.vocab_size   )
      _SCREAMING_SNAKE_CASE	=				ids_tensor((self.batch_size, 3)  ,				2   )

      # append to next input_ids and
      _SCREAMING_SNAKE_CASE	=				torch.cat([input_ids, next_tokens]  ,				dim=-1   )
      _SCREAMING_SNAKE_CASE	=				torch.cat([attention_mask, next_attn_mask]  ,				dim=-1   )

      _SCREAMING_SNAKE_CASE	=				model(__lowerCAmelCase  ,				attention_mask=__lowerCAmelCase   )["last_hidden_state"]
      _SCREAMING_SNAKE_CASE	=				model(__lowerCAmelCase  ,				attention_mask=__lowerCAmelCase  ,				past_key_values=__lowerCAmelCase   )[
          "last_hidden_state"
      ]

      # select random slice
      _SCREAMING_SNAKE_CASE	=				ids_tensor((1,)  ,				output_from_past.shape[-1]   ).item()
      _SCREAMING_SNAKE_CASE	=				output_from_no_past[:, -3:, random_slice_idx].detach()
      _SCREAMING_SNAKE_CASE	=				output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(__lowerCAmelCase  ,				__lowerCAmelCase  ,				atol=1e-2   )   )



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MaMaaaModel(config=__lowerCAmelCase   ).to(__lowerCAmelCase   ).eval()
      _SCREAMING_SNAKE_CASE	=				model(**__lowerCAmelCase   )

      _SCREAMING_SNAKE_CASE	=				outputs.encoder_last_hidden_state
      _SCREAMING_SNAKE_CASE	=				outputs.last_hidden_state

      with tempfile.TemporaryDirectory() as tmpdirname:
         _SCREAMING_SNAKE_CASE	=				model.get_encoder()
         encoder.save_pretrained(__lowerCAmelCase   )
         _SCREAMING_SNAKE_CASE	=				MaMaaaEncoder.from_pretrained(__lowerCAmelCase   ).to(__lowerCAmelCase   )

      _SCREAMING_SNAKE_CASE	=				encoder(inputs_dict["input_ids"]  ,				attention_mask=inputs_dict["attention_mask"]   )[
          0
      ]

      self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3   )

      with tempfile.TemporaryDirectory() as tmpdirname:
         _SCREAMING_SNAKE_CASE	=				model.get_decoder()
         decoder.save_pretrained(__lowerCAmelCase   )
         _SCREAMING_SNAKE_CASE	=				MaMaaaDecoder.from_pretrained(__lowerCAmelCase   ).to(__lowerCAmelCase   )

      _SCREAMING_SNAKE_CASE	=				decoder(
          input_ids=inputs_dict["decoder_input_ids"]  ,				attention_mask=inputs_dict["decoder_attention_mask"]  ,				encoder_hidden_states=__lowerCAmelCase  ,				encoder_attention_mask=inputs_dict["attention_mask"]  ,				)[0]

      self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3   )







@require_torch
class 		lowercase_   (			_a					,  _a					,  _a					,  unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						(
       (
           MaMaaaModel,
           MaMaaaForConditionalGeneration,
       )
       if is_torch_available()
       else ()
   )
   lowerCamelCase_      =						(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
   lowerCamelCase_      =						(
       {
           """conversational""": MaMaaaForConditionalGeneration,
           """feature-extraction""": MaMaaaModel,
           """summarization""": MaMaaaForConditionalGeneration,
           """text2text-generation""": MaMaaaForConditionalGeneration,
           """translation""": MaMaaaForConditionalGeneration,
       }
       if is_torch_available()
       else {}
   )
   lowerCamelCase_      =						True
   lowerCamelCase_      =						True
   lowerCamelCase_      =						False
   lowerCamelCase_      =						False



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

      """simple docstring"""
      if pipeline_test_casse_name == "TranslationPipelineTests":
         # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
         # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
         return True

      return False



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MaMaaaModelTester(self   )
      _SCREAMING_SNAKE_CASE	=				ConfigTester(self  ,				config_class=__lowerCAmelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      self.config_tester.run_common_tests()



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
         _SCREAMING_SNAKE_CASE	=				model_class(__lowerCAmelCase   )

         with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(__lowerCAmelCase   )
            _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				model_class.from_pretrained(__lowerCAmelCase  ,				output_loading_info=__lowerCAmelCase   )
         self.assertEqual(info["missing_keys"]  ,				[]   )



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowerCAmelCase   )



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_encoder_decoder_model_standalone(*__lowerCAmelCase   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
         _SCREAMING_SNAKE_CASE	=				model_class(__lowerCAmelCase   )
         model.to(__lowerCAmelCase   )
         model.eval()

         _SCREAMING_SNAKE_CASE	=				copy.deepcopy(self._prepare_for_class(__lowerCAmelCase  ,				__lowerCAmelCase   )   )

         if not self.is_encoder_decoder:
            _SCREAMING_SNAKE_CASE	=				inputs["input_ids"]
            del inputs["input_ids"]
         else:
            _SCREAMING_SNAKE_CASE	=				inputs["input_ids"]
            _SCREAMING_SNAKE_CASE	=				inputs.get("decoder_input_ids"  ,				__lowerCAmelCase   )
            del inputs["input_ids"]
            inputs.pop("decoder_input_ids"  ,				__lowerCAmelCase   )

         _SCREAMING_SNAKE_CASE	=				model.get_input_embeddings()
         if not self.is_encoder_decoder:
            _SCREAMING_SNAKE_CASE	=				wte(__lowerCAmelCase   )
         else:
            _SCREAMING_SNAKE_CASE	=				wte(__lowerCAmelCase   )
            _SCREAMING_SNAKE_CASE	=				wte(__lowerCAmelCase   )

         with torch.no_grad():
            model(**__lowerCAmelCase   )[0]



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      _SCREAMING_SNAKE_CASE	=				input_dict["input_ids"]
      _SCREAMING_SNAKE_CASE	=				input_ids.ne(1   ).to(__lowerCAmelCase   )
      _SCREAMING_SNAKE_CASE	=				MaMaaaForConditionalGeneration(__lowerCAmelCase   ).eval().to(__lowerCAmelCase   )
      if torch_device == "cuda":
         model.half()
      model.generate(__lowerCAmelCase  ,				attention_mask=__lowerCAmelCase   )
      model.generate(num_beams=4  ,				do_sample=__lowerCAmelCase  ,				early_stopping=__lowerCAmelCase  ,				num_return_sequences=3   )


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]  )			->			List[str]:
   return torch.tensor(a__	,					dtype=torch.long	,					device=a__  )


lowerCamelCase_			=						1e-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

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

      """simple docstring"""
      return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"   )



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MaMaaaModel.from_pretrained("facebook/m2m100_418M"   ).to(__lowerCAmelCase   )
      _SCREAMING_SNAKE_CASE	=				_long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]   )
      _SCREAMING_SNAKE_CASE	=				_long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]   )
      _SCREAMING_SNAKE_CASE	=				prepare_mam_aaa_inputs_dict(model.config  ,				__lowerCAmelCase  ,				__lowerCAmelCase   )
      with torch.no_grad():
         _SCREAMING_SNAKE_CASE	=				model(**__lowerCAmelCase   )[0]
      _SCREAMING_SNAKE_CASE	=				torch.Size((1, 1_1, 1_0_2_4)   )
      self.assertEqual(output.shape  ,				__lowerCAmelCase   )
      # change to expected output here
      _SCREAMING_SNAKE_CASE	=				torch.tensor(
          [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]  ,				device=__lowerCAmelCase   )
      self.assertTrue(torch.allclose(output[:, :3, :3]  ,				__lowerCAmelCase  ,				atol=__lowerCAmelCase   )   )



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"   ).to(__lowerCAmelCase   )

      # change to intended input
      _SCREAMING_SNAKE_CASE	=				_long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]   )
      _SCREAMING_SNAKE_CASE	=				_long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]   )
      _SCREAMING_SNAKE_CASE	=				prepare_mam_aaa_inputs_dict(model.config  ,				__lowerCAmelCase  ,				__lowerCAmelCase   )
      with torch.no_grad():
         _SCREAMING_SNAKE_CASE	=				model(**__lowerCAmelCase   )[0]
      _SCREAMING_SNAKE_CASE	=				torch.Size((1, 1_1, model.config.vocab_size)   )
      self.assertEqual(output.shape  ,				__lowerCAmelCase   )
      # change to expected output here
      _SCREAMING_SNAKE_CASE	=				torch.tensor(
          [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]  ,				device=__lowerCAmelCase   )
      self.assertTrue(torch.allclose(output[:, :3, :3]  ,				__lowerCAmelCase  ,				atol=__lowerCAmelCase   )   )



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"   ).to(__lowerCAmelCase   )
      _SCREAMING_SNAKE_CASE	=				MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"  ,				src_lang="fr"  ,				tgt_lang="en"   )

      _SCREAMING_SNAKE_CASE	=				[
          "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
          "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
          "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
          " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
          " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
      ]

      # The below article tests that we don't add any hypotheses outside of the top n_beams
      _SCREAMING_SNAKE_CASE	=				tokenizer(__lowerCAmelCase  ,				padding=__lowerCAmelCase  ,				return_tensors="pt"   )

      _SCREAMING_SNAKE_CASE	=				model.generate(
          input_ids=dct["input_ids"].to(__lowerCAmelCase   )  ,				attention_mask=dct["attention_mask"].to(__lowerCAmelCase   )  ,				num_beams=5  ,				forced_bos_token_id=tokenizer.get_lang_id("en"   )  ,				)

      _SCREAMING_SNAKE_CASE	=				[
          "The NSA case highlights the total absence of intelligence debate",
          "I think there are two levels of response from the French government.",
          "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
          " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
          " communications in France.",
      ]

      _SCREAMING_SNAKE_CASE	=				tokenizer.batch_decode(
          hypotheses_batch.tolist()  ,				clean_up_tokenization_spaces=__lowerCAmelCase  ,				skip_special_tokens=__lowerCAmelCase   )
      assert generated == expected_en









'''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()   )

