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 		_lowerCamelCase		:
      def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Tuple:
       return None






class 		_lowerCamelCase		:
      def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Dict:
       return None






class 		_lowerCamelCase		(   unittest.TestCase    ):
      __a  =      [
          # (model_name, model_kwargs)
          ("bert-base-cased", {}),
          ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
      ]
      @require_tf
      @slow
      def 						UpperCamelCase_				(  self   )       ->					str:
       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
        self._test_export(lowerCAmelCase , '''tf''' , 12 , **lowerCAmelCase   )
      @require_torch
      @slow
      def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
        self._test_export(lowerCAmelCase , '''pt''' , 12 , **lowerCAmelCase   )
      @require_torch
      @slow
      def 						UpperCamelCase_				(  self   )       ->					Any:
       from transformers import BertModel

       SCREAMING_SNAKE_CASE__:   Dict=      ['''[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__:   List[str]=      BertTokenizerFast(vocab_file.name   )

       with TemporaryDirectory() as bert_save_dir:
        SCREAMING_SNAKE_CASE__:   Any=      BertModel(BertConfig(vocab_size=len(lowerCAmelCase   )   )   )
        model.save_pretrained(lowerCAmelCase   )
        self._test_export(lowerCAmelCase , '''pt''' , 12 , lowerCAmelCase   )
      @require_tf
      @slow
      def 						UpperCamelCase_				(  self   )       ->					List[str]:
       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
        SCREAMING_SNAKE_CASE__:   Tuple=      self._test_export(lowerCAmelCase , '''tf''' , 12 , **lowerCAmelCase   )
        SCREAMING_SNAKE_CASE__:   int=      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 						UpperCamelCase_				(  self   )       ->					str:
       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
        SCREAMING_SNAKE_CASE__:   Any=      self._test_export(lowerCAmelCase , '''pt''' , 12 , **lowerCAmelCase   )
        SCREAMING_SNAKE_CASE__:   List[str]=      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 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=None , **lowerCAmelCase   )       ->					Dict:
       try:
        # Compute path
        with TemporaryDirectory() as tempdir:
         SCREAMING_SNAKE_CASE__:   Union[str, Any]=      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 						UpperCamelCase_				(  self   )       ->					str:
       from transformers import BertModel

       SCREAMING_SNAKE_CASE__:   Dict=      BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''   )   )
       SCREAMING_SNAKE_CASE__:   Dict=      BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''   )
       self._test_infer_dynamic_axis(lowerCAmelCase , lowerCAmelCase , '''pt'''   )
      @require_tf
      @require_tokenizers
      @slow
      def 						UpperCamelCase_				(  self   )       ->					Tuple:
       from transformers import TFBertModel

       SCREAMING_SNAKE_CASE__:   Optional[Any]=      TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''   )   )
       SCREAMING_SNAKE_CASE__:   Optional[int]=      BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''   )
       self._test_infer_dynamic_axis(lowerCAmelCase , lowerCAmelCase , '''tf'''   )
      def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Dict:
       SCREAMING_SNAKE_CASE__:   Tuple=      FeatureExtractionPipeline(lowerCAmelCase , lowerCAmelCase   )

       SCREAMING_SNAKE_CASE__:   List[str]=      ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
       SCREAMING_SNAKE_CASE__:   Optional[int]=      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 						UpperCamelCase_				(  self   )       ->					int:
       SCREAMING_SNAKE_CASE__:   Any=      ['''input_ids''', '''attention_mask''', '''token_type_ids''']
       SCREAMING_SNAKE_CASE__:   List[str]=      {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
       SCREAMING_SNAKE_CASE__:   Optional[int]=      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__:   Optional[int]=      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 						UpperCamelCase_				(  self   )       ->					List[str]:
       SCREAMING_SNAKE_CASE__:   int=      generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''   ) , '''-test'''   )
       self.assertEqual('''/home/something/my_fake_model-test.onnx''' , generated.as_posix()   )


import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


lowercase_     :   List[str] 				=				{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class 		_lowerCamelCase		(   nn.Module    ):
    def __init__(  self , lowerCAmelCase   )       ->					str:
     super().__init__()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torchvision.models.resnetaaa(pretrained=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      list(model.children()   )[:-2]
     SCREAMING_SNAKE_CASE__:   Tuple=      nn.Sequential(*lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )



    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
     # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
     SCREAMING_SNAKE_CASE__:   str=      self.pool(self.model(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   str=      torch.flatten(lowerCAmelCase , start_dim=2   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      out.transpose(1 , 2   ).contiguous()
     return out  # BxNx2048






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [json.loads(lowerCAmelCase   ) for l in open(lowerCAmelCase   )]
     SCREAMING_SNAKE_CASE__:   List[str]=      os.path.dirname(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      tokenizer
     SCREAMING_SNAKE_CASE__:   Any=      labels
     SCREAMING_SNAKE_CASE__:   List[Any]=      len(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      max_seq_length

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      transforms
    def __len__(  self   )       ->					Optional[Any]:
     return len(self.data   )
    def __getitem__(  self , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] , add_special_tokens=lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Tuple=      sentence[0], sentence[1:-1], sentence[-1]
     SCREAMING_SNAKE_CASE__:   Tuple=      sentence[: self.max_seq_length]

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.zeros(self.n_classes   )
     SCREAMING_SNAKE_CASE__:   Dict=      1

     SCREAMING_SNAKE_CASE__:   Optional[int]=      Image.open(os.path.join(self.data_dir , self.data[index]['''img''']   )   ).convert('''RGB'''   )
     SCREAMING_SNAKE_CASE__:   int=      self.transforms(lowerCAmelCase   )

     return {
         "image_start_token": start_token,
         "image_end_token": end_token,
         "sentence": sentence,
         "image": image,
         "label": label,
     }



    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      Counter()
     for row in self.data:
      label_freqs.update(row['''label''']   )
     return label_freqs







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   Any=      [len(row['''sentence''']      ) for row in batch]
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      len(snake_case_      ), max(snake_case_      )

 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )

 for i_batch, (input_row, length) in enumerate(zip(snake_case_       ,				snake_case_      )      ):
  SCREAMING_SNAKE_CASE__:   Optional[int]=      input_row['''sentence''']
  SCREAMING_SNAKE_CASE__:   Dict=      1

 SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.stack([row['''image'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   List[str]=      torch.stack([row['''label'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Any=      torch.stack([row['''image_start_token'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.stack([row['''image_end_token'''] for row in batch]      )

 return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def 			A__      (	):
 return [
     "Crime",
     "Drama",
     "Thriller",
     "Action",
     "Comedy",
     "Romance",
     "Documentary",
     "Short",
     "Mystery",
     "History",
     "Family",
     "Adventure",
     "Fantasy",
     "Sci-Fi",
     "Western",
     "Horror",
     "Sport",
     "War",
     "Music",
     "Musical",
     "Animation",
     "Biography",
     "Film-Noir",
 ]







def 			A__      (	):
 return transforms.Compose(
     [
         transforms.Resize(256      ),
         transforms.CenterCrop(224      ),
         transforms.ToTensor(),
         transforms.Normalize(
             mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]       ,				std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]       ,				),
     ]      )

