


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     __UpperCAmelCase							:


     """simple docstring"""


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        return None







class     __UpperCAmelCase							:


     """simple docstring"""


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A    ):
        return None







class     __UpperCAmelCase							(       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 	snake_case_  (	self    ):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           self._test_export(__A  ,       """tf"""  ,       12  ,       **__A    )


     @require_torch
     @slow
     def 	snake_case_  (	self    ):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           self._test_export(__A  ,       """pt"""  ,       12  ,       **__A    )


     @require_torch
     @slow
     def 	snake_case_  (	self    ):
        from transformers import BertModel

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

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


     @require_tf
     @slow
     def 	snake_case_  (	self    ):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           __a					=  self._test_export(__A  ,       """tf"""  ,       12  ,       **__A    )
           __a					=  quantize(Path(__A    )    )

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


     @require_torch
     @slow
     def 	snake_case_  (	self    ):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           __a					=  self._test_export(__A  ,       """pt"""  ,       12  ,       **__A    )
           __a					=  quantize(__A    )

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


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A=None  ,       **__A    ):
        try:
           # Compute path
           with TemporaryDirectory() as tempdir:
              __a					=  Path(__A    ).joinpath("""model.onnx"""    )

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

           # Export
           convert(__A  ,       __A  ,       __A  ,       __A  ,       __A  ,       **__A    )

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


     @require_torch
     @require_tokenizers
     @slow
     def 	snake_case_  (	self    ):
        from transformers import BertModel

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


     @require_tf
     @require_tokenizers
     @slow
     def 	snake_case_  (	self    ):
        from transformers import TFBertModel

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


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  FeatureExtractionPipeline(__A  ,       __A    )

        __a					=  ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
        __a	,    __a	,    __a	,    __a					=  infer_shapes(__A  ,       __A    )

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

        # 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 	snake_case_  (	self    ):
        __a					=  ["""input_ids""", """attention_mask""", """token_type_ids"""]
        __a					=  {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
        __a	,    __a					=  ensure_valid_input(FuncContiguousArgs()  ,       __A  ,       __A    )

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

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

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

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

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

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


     def 	snake_case_  (	self    ):
        __a					=  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


SCREAMING_SNAKE_CASE							    =							{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     __UpperCAmelCase							(       nn.Module							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__()
        __a					=  torchvision.models.resnetaaa(pretrained=__A    )
        __a					=  list(model.children()    )[:-2]
        __a					=  nn.Sequential(*__A    )
        __a					=  nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )


     def 	snake_case_  (	self  ,       __A    ):
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        __a					=  self.pool(self.model(__A    )    )
        __a					=  torch.flatten(__A  ,       start_dim=2    )
        __a					=  out.transpose(1  ,       2    ).contiguous()
        return out  # BxNx2048







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        __a					=  [json.loads(__A    ) for l in open(__A    )]
        __a					=  os.path.dirname(__A    )
        __a					=  tokenizer
        __a					=  labels
        __a					=  len(__A    )
        __a					=  max_seq_length

        __a					=  transforms


     def __len__(	self    ):
        return len(self.data    )


     def __getitem__(	self  ,       __A    ):
        __a					=  torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]  ,       add_special_tokens=__A    )    )
        __a	,    __a	,    __a					=  sentence[0], sentence[1:-1], sentence[-1]
        __a					=  sentence[: self.max_seq_length]

        __a					=  torch.zeros(self.n_classes    )
        __a					=  1

        __a					=  Image.open(os.path.join(self.data_dir  ,       self.data[index]["""img"""]    )    ).convert("""RGB"""    )
        __a					=  self.transforms(__A    )

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


     def 	snake_case_  (	self    ):
        __a					=  Counter()
        for row in self.data:
           label_freqs.update(row["""label"""]    )
        return label_freqs







def  a   (lowerCAmelCase__   ):
   __a					=  [len(row["""sentence"""]   ) for row in batch]
   __a	,    __a					=  len(lowerCAmelCase__   ), max(lowerCAmelCase__   )

   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )
   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )

   for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase__		,				lowerCAmelCase__   )   ):
      __a					=  input_row["""sentence"""]
      __a					=  1

   __a					=  torch.stack([row["""image"""] for row in batch]   )
   __a					=  torch.stack([row["""label"""] for row in batch]   )
   __a					=  torch.stack([row["""image_start_token"""] for row in batch]   )
   __a					=  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=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]		,				std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]		,				),
       ]   )



