import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       LayoutLMvaForQuestionAnswering,
       LayoutLMvaForSequenceClassification,
       LayoutLMvaForTokenClassification,
       LayoutLMvaModel,
   )
   from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor







class __snake_case       :




   def __init__(					self    :       Optional[Any] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       List[str]=2 ,			_lowercase    :       Dict=3 ,			_lowercase    :       Union[str, Any]=4 ,			_lowercase    :       Optional[int]=2 ,			_lowercase    :       List[str]=7 ,			_lowercase    :       Tuple=True ,			_lowercase    :       Dict=True ,			_lowercase    :       Union[str, Any]=True ,			_lowercase    :       Optional[Any]=True ,			_lowercase    :       List[str]=99 ,			_lowercase    :       Union[str, Any]=36 ,			_lowercase    :       Tuple=3 ,			_lowercase    :       Optional[int]=4 ,			_lowercase    :       List[str]=37 ,			_lowercase    :       List[Any]="gelu" ,			_lowercase    :       List[str]=0.1 ,			_lowercase    :       Union[str, Any]=0.1 ,			_lowercase    :       Tuple=5_12 ,			_lowercase    :       Optional[int]=16 ,			_lowercase    :       Tuple=2 ,			_lowercase    :       Optional[int]=0.02 ,			_lowercase    :       int=6 ,			_lowercase    :       List[Any]=6 ,			_lowercase    :       Optional[Any]=3 ,			_lowercase    :       Union[str, Any]=4 ,			_lowercase    :       Optional[Any]=None ,			_lowercase    :       Any=10_00 ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   parent
     SCREAMING_SNAKE_CASE__    							=   batch_size
     SCREAMING_SNAKE_CASE__    							=   num_channels
     SCREAMING_SNAKE_CASE__    							=   image_size
     SCREAMING_SNAKE_CASE__    							=   patch_size
     SCREAMING_SNAKE_CASE__    							=   text_seq_length
     SCREAMING_SNAKE_CASE__    							=   is_training
     SCREAMING_SNAKE_CASE__    							=   use_input_mask
     SCREAMING_SNAKE_CASE__    							=   use_token_type_ids
     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__    							=   max_position_embeddings
     SCREAMING_SNAKE_CASE__    							=   type_vocab_size
     SCREAMING_SNAKE_CASE__    							=   type_sequence_label_size
     SCREAMING_SNAKE_CASE__    							=   initializer_range
     SCREAMING_SNAKE_CASE__    							=   coordinate_size
     SCREAMING_SNAKE_CASE__    							=   shape_size
     SCREAMING_SNAKE_CASE__    							=   num_labels
     SCREAMING_SNAKE_CASE__    							=   num_choices
     SCREAMING_SNAKE_CASE__    							=   scope
     SCREAMING_SNAKE_CASE__    							=   range_bbox

     # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
     SCREAMING_SNAKE_CASE__    							=   text_seq_length
     SCREAMING_SNAKE_CASE__    							=   (image_size // patch_size) ** 2 + 1
     SCREAMING_SNAKE_CASE__    							=   self.text_seq_length + self.image_seq_length




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ids_tensor([self.batch_size, self.text_seq_length] ,			self.vocab_size   )

     SCREAMING_SNAKE_CASE__    							=   ids_tensor([self.batch_size, self.text_seq_length, 4] ,			self.range_bbox   )
     # Ensure that bbox is legal
     for i in range(bbox.shape[0]   ):
       for j in range(bbox.shape[1]   ):
         if bbox[i, j, 3] < bbox[i, j, 1]:
           SCREAMING_SNAKE_CASE__    							=   bbox[i, j, 3]
           SCREAMING_SNAKE_CASE__    							=   bbox[i, j, 1]
           SCREAMING_SNAKE_CASE__    							=   t
         if bbox[i, j, 2] < bbox[i, j, 0]:
           SCREAMING_SNAKE_CASE__    							=   bbox[i, j, 2]
           SCREAMING_SNAKE_CASE__    							=   bbox[i, j, 0]
           SCREAMING_SNAKE_CASE__    							=   t

     SCREAMING_SNAKE_CASE__    							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

     SCREAMING_SNAKE_CASE__    							=   None
     if self.use_input_mask:
       SCREAMING_SNAKE_CASE__    							=   random_attention_mask([self.batch_size, self.text_seq_length]   )

     SCREAMING_SNAKE_CASE__    							=   None
     if self.use_token_type_ids:
       SCREAMING_SNAKE_CASE__    							=   ids_tensor([self.batch_size, self.text_seq_length] ,			self.type_vocab_size   )

     SCREAMING_SNAKE_CASE__    							=   None
     SCREAMING_SNAKE_CASE__    							=   None
     if self.use_labels:
       SCREAMING_SNAKE_CASE__    							=   ids_tensor([self.batch_size] ,			self.type_sequence_label_size   )
       SCREAMING_SNAKE_CASE__    							=   ids_tensor([self.batch_size, self.text_seq_length] ,			self.num_labels   )

     SCREAMING_SNAKE_CASE__    							=   LayoutLMvaConfig(
         vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			num_attention_heads=self.num_attention_heads ,			intermediate_size=self.intermediate_size ,			hidden_act=self.hidden_act ,			hidden_dropout_prob=self.hidden_dropout_prob ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob ,			max_position_embeddings=self.max_position_embeddings ,			type_vocab_size=self.type_vocab_size ,			initializer_range=self.initializer_range ,			coordinate_size=self.coordinate_size ,			shape_size=self.shape_size ,			input_size=self.image_size ,			patch_size=self.patch_size ,			)

     return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Optional[int] ,			_lowercase    :       List[Any] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional[Any] ,			_lowercase    :       int ,			_lowercase    :       Optional[int] ,			_lowercase    :       str ,			_lowercase    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   LayoutLMvaModel(config=__lowerCAmelCase   )
     model.to(__lowerCAmelCase   )
     model.eval()

     # text + image
     SCREAMING_SNAKE_CASE__    							=   model(__lowerCAmelCase ,			pixel_values=__lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__    							=   model(
         __lowerCAmelCase ,			bbox=__lowerCAmelCase ,			pixel_values=__lowerCAmelCase ,			attention_mask=__lowerCAmelCase ,			token_type_ids=__lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__    							=   model(__lowerCAmelCase ,			bbox=__lowerCAmelCase ,			pixel_values=__lowerCAmelCase ,			token_type_ids=__lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__    							=   model(__lowerCAmelCase ,			bbox=__lowerCAmelCase ,			pixel_values=__lowerCAmelCase   )

     self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)   )

     # text only
     SCREAMING_SNAKE_CASE__    							=   model(__lowerCAmelCase   )
     self.parent.assertEqual(
         result.last_hidden_state.shape ,			(self.batch_size, self.text_seq_length, self.hidden_size)   )

     # image only
     SCREAMING_SNAKE_CASE__    							=   model(pixel_values=__lowerCAmelCase   )
     self.parent.assertEqual(
         result.last_hidden_state.shape ,			(self.batch_size, self.image_seq_length, self.hidden_size)   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Tuple ,			_lowercase    :       List[str] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Dict ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.num_labels
     SCREAMING_SNAKE_CASE__    							=   LayoutLMvaForSequenceClassification(__lowerCAmelCase   )
     model.to(__lowerCAmelCase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(
         __lowerCAmelCase ,			bbox=__lowerCAmelCase ,			pixel_values=__lowerCAmelCase ,			attention_mask=__lowerCAmelCase ,			token_type_ids=__lowerCAmelCase ,			labels=__lowerCAmelCase ,			)
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       str ,			_lowercase    :       Optional[int] ,			_lowercase    :       Tuple ,			_lowercase    :       List[Any] ,			_lowercase    :       Any ,			_lowercase    :       Optional[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.num_labels
     SCREAMING_SNAKE_CASE__    							=   LayoutLMvaForTokenClassification(config=__lowerCAmelCase   )
     model.to(__lowerCAmelCase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(
         __lowerCAmelCase ,			bbox=__lowerCAmelCase ,			pixel_values=__lowerCAmelCase ,			attention_mask=__lowerCAmelCase ,			token_type_ids=__lowerCAmelCase ,			labels=__lowerCAmelCase ,			)
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.text_seq_length, self.num_labels)   )




   def 			__a		(					self    :       Any ,			_lowercase    :       int ,			_lowercase    :       Any ,			_lowercase    :       int ,			_lowercase    :       Dict ,			_lowercase    :       int ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   LayoutLMvaForQuestionAnswering(config=__lowerCAmelCase   )
     model.to(__lowerCAmelCase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(
         __lowerCAmelCase ,			bbox=__lowerCAmelCase ,			pixel_values=__lowerCAmelCase ,			attention_mask=__lowerCAmelCase ,			token_type_ids=__lowerCAmelCase ,			start_positions=__lowerCAmelCase ,			end_positions=__lowerCAmelCase ,			)
     self.parent.assertEqual(result.start_logits.shape ,			(self.batch_size, self.seq_length)   )
     self.parent.assertEqual(result.end_logits.shape ,			(self.batch_size, self.seq_length)   )




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE__			
     )							,  (
         SCREAMING_SNAKE_CASE__			
     )							,  (
         SCREAMING_SNAKE_CASE__			
     )							,  (
         SCREAMING_SNAKE_CASE__			
     )							,  (
         SCREAMING_SNAKE_CASE__			
     )							,  (
         SCREAMING_SNAKE_CASE__			
     )							,  (
         SCREAMING_SNAKE_CASE__			
     )							,  (
         SCREAMING_SNAKE_CASE__			
     )							,  			
     )    							=   config_and_inputs
     SCREAMING_SNAKE_CASE__    							=   {
         """input_ids""": input_ids,
         """bbox""": bbox,
         """pixel_values""": pixel_values,
         """token_type_ids""": token_type_ids,
         """attention_mask""": input_mask,
     }
     return config, inputs_dict







@require_torch
class __snake_case       (		lowerCamelCase_     ,	lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False

   lowerCAmelCase_														=							(
       (
           LayoutLMvaModel,
           LayoutLMvaForSequenceClassification,
           LayoutLMvaForTokenClassification,
           LayoutLMvaForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   lowerCAmelCase_														=							(
       {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
       if is_torch_available()
       else {}
   )




   def 			__a		(					self    :       int ,			_lowercase    :       Any ,			_lowercase    :       str ,			_lowercase    :       Tuple ,			_lowercase    :       int ,			_lowercase    :       str   ):



     """simple docstring"""






     return True




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   LayoutLMvaModelTester(self   )
     SCREAMING_SNAKE_CASE__    							=   ConfigTester(self ,			config_class=__lowerCAmelCase ,			hidden_size=37   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[str] ,			_lowercase    :       Dict ,			_lowercase    :       int=False   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   copy.deepcopy(__lowerCAmelCase   )
     if model_class in get_values(__lowerCAmelCase   ):
       SCREAMING_SNAKE_CASE__    							=   {
           k: v.unsqueeze(1   ).expand(-1 ,			self.model_tester.num_choices ,			-1   ).contiguous()
           if isinstance(__lowerCAmelCase ,			torch.Tensor   ) and v.ndim > 1
           else v
           for k, v in inputs_dict.items()
       }
     if return_labels:
       if model_class in get_values(__lowerCAmelCase   ):
         SCREAMING_SNAKE_CASE__    							=   torch.ones(self.model_tester.batch_size ,			dtype=torch.long ,			device=__lowerCAmelCase   )
       elif model_class in get_values(__lowerCAmelCase   ):
         SCREAMING_SNAKE_CASE__    							=   torch.zeros(
             self.model_tester.batch_size ,			dtype=torch.long ,			device=__lowerCAmelCase   )
         SCREAMING_SNAKE_CASE__    							=   torch.zeros(
             self.model_tester.batch_size ,			dtype=torch.long ,			device=__lowerCAmelCase   )
       elif model_class in [
           *get_values(__lowerCAmelCase   ),
       ]:
         SCREAMING_SNAKE_CASE__    							=   torch.zeros(
             self.model_tester.batch_size ,			dtype=torch.long ,			device=__lowerCAmelCase   )
       elif model_class in [
           *get_values(__lowerCAmelCase   ),
       ]:
         SCREAMING_SNAKE_CASE__    							=   torch.zeros(
             (self.model_tester.batch_size, self.model_tester.text_seq_length) ,			dtype=torch.long ,			device=__lowerCAmelCase ,			)

     return inputs_dict




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCAmelCase   )




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
       SCREAMING_SNAKE_CASE__    							=   type
       self.model_tester.create_and_check_model(*__lowerCAmelCase   )




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase   )




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



     """simple docstring"""






     for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       SCREAMING_SNAKE_CASE__    							=   LayoutLMvaModel.from_pretrained(__lowerCAmelCase   )
       self.assertIsNotNone(__lowerCAmelCase   )





def 			__SCREAMING_SNAKE_CASE						(      )							->      Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
  return image







@require_torch
class __snake_case       (		unittest.TestCase	):




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



     """simple docstring"""






     return LayoutLMvaImageProcessor(apply_ocr=__lowerCAmelCase   ) if is_vision_available() else None




   @slow
   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""   ).to(__lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__    							=   self.default_image_processor
     SCREAMING_SNAKE_CASE__    							=   prepare_img()
     SCREAMING_SNAKE_CASE__    							=   image_processor(images=__lowerCAmelCase ,			return_tensors="""pt"""   ).pixel_values.to(__lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__    							=   torch.tensor([[1, 2]]   )
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]   ).unsqueeze(0   )

     # forward pass
     SCREAMING_SNAKE_CASE__    							=   model(
         input_ids=input_ids.to(__lowerCAmelCase   ) ,			bbox=bbox.to(__lowerCAmelCase   ) ,			pixel_values=pixel_values.to(__lowerCAmelCase   ) ,			)

     # verify the logits
     SCREAMING_SNAKE_CASE__    							=   torch.Size((1, 1_99, 7_68)   )
     self.assertEqual(outputs.last_hidden_state.shape ,			__lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__    							=   torch.tensor(
         [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]   ).to(__lowerCAmelCase   )

     self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] ,			__lowerCAmelCase ,			atol=1E-4   )   )

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 			__a		(					self    :       str ,			_lowercase    :       str ,			_lowercase    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return None







class __snake_case       :




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[str] ,			_lowercase    :       str ,			_lowercase    :       List[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     return None







class __snake_case       (		unittest.TestCase	):
   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 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






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




   @require_torch
   @slow
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






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




   @require_torch
   @slow
   def 			__a		(					self    :       Dict   ):



     """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(_lowercase   )   )
       vocab_file.flush()
       SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast(vocab_file.name   )

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




   @require_tf
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""tf""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   quantize(Path(_lowercase   )   )

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




   @require_torch
   @slow
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""pt""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   quantize(_lowercase   )

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




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional[Any] ,			_lowercase    :       str=None ,			**_lowercase    :       List[str]   ):



     """simple docstring"""






     try:
       # Compute path
       with TemporaryDirectory() as tempdir:
         SCREAMING_SNAKE_CASE__    							=   Path(_lowercase   ).joinpath("""model.onnx"""   )

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

       # Export
       convert(_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			**_lowercase   )

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




   @require_torch
   @require_tokenizers
   @slow
   def 			__a		(					self    :       List[str]   ):



     """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(_lowercase ,			_lowercase ,			"""pt"""   )




   @require_tf
   @require_tokenizers
   @slow
   def 			__a		(					self    :       Dict   ):



     """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(_lowercase ,			_lowercase ,			"""tf"""   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   FeatureExtractionPipeline(_lowercase ,			_lowercase   )

     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(_lowercase ,			_lowercase   )

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

     # 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    :       Dict   ):



     """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() ,			_lowercase ,			_lowercase   )

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

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

     # Parameter should be reordered according to their respective place in the function:
     # (input_ids, token_type_ids, attention_mask)
     self.assertEqual(_lowercase ,			(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() ,			_lowercase ,			_lowercase   )

     # Should have exactly the one arg (all before the one not provided "some_other_args")
     self.assertEqual(len(_lowercase   ) ,			1   )
     self.assertEqual(len(_lowercase   ) ,			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    :       List[str]   ):



     """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()   )
