


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase					:       Optional[Any]			    =     get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
@require_tokenizers
class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		XGLMTokenizer
  _lowercase									=		XGLMTokenizerFast
  _lowercase									=		True
  _lowercase									=		True

  def    _UpperCamelCase( self			:  List[Any]       ):
        super().setUp()

        # We have a SentencePiece fixture for testing
        a__	:  str     =   XGLMTokenizer(lowerCamelCase__				,				keep_accents=lowerCamelCase__       )
        tokenizer.save_pretrained(self.tmpdirname       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  int     =   "<pad>"
        a__	:  Union[str, Any]     =   1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__       )				,				lowerCamelCase__       )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__       )				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  List[str]     =   list(self.get_tokenizer().get_vocab().keys()       )

        self.assertEqual(vocab_keys[0]				,				"<s>"       )
        self.assertEqual(vocab_keys[1]				,				"<pad>"       )
        self.assertEqual(len(lowerCamelCase__       )				,				1_008       )

  def    _UpperCamelCase( self			:  Dict       ):
        self.assertEqual(self.get_tokenizer().vocab_size				,				1_008       )

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  str     =   XGLMTokenizer(lowerCamelCase__				,				keep_accents=lowerCamelCase__       )

        a__	:  List[str]     =   tokenizer.tokenize("This is a test"       )
        self.assertListEqual(lowerCamelCase__				,				["▁This", "▁is", "▁a", "▁t", "est"]       )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowerCamelCase__       )				,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,				)

        a__	:  Any     =   tokenizer.tokenize("I was born in 92000, and this is falsé."       )
        self.assertListEqual(
            lowerCamelCase__				,				[
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "9",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "é",
                ".",
            ]				,				)
        a__	:  List[str]     =   tokenizer.convert_tokens_to_ids(lowerCamelCase__       )
        self.assertListEqual(
            lowerCamelCase__				,				[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]				,				)

        a__	:  Dict     =   tokenizer.convert_ids_to_tokens(lowerCamelCase__       )
        self.assertListEqual(
            lowerCamelCase__				,				[
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "<unk>",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "<unk>",
                ".",
            ]				,				)

  @cached_property
  def    _UpperCamelCase( self			:  Dict       ):
        return XGLMTokenizer.from_pretrained("facebook/xglm-564M"       )

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        with tempfile.NamedTemporaryFile() as f:
              shutil.copyfile(lowerCamelCase__				,				f.name       )
              a__	:  Any     =   XGLMTokenizer(f.name				,				keep_accents=lowerCamelCase__       )
              a__	:  List[str]     =   pickle.dumps(lowerCamelCase__       )
        pickle.loads(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        if not self.test_rust_tokenizer:
              return

        a__	:  Any     =   self.get_tokenizer()
        a__	:  Optional[Any]     =   self.get_rust_tokenizer()

        a__	:  Tuple     =   "I was born in 92000, and this is falsé."

        a__	:  List[str]     =   tokenizer.tokenize(lowerCamelCase__       )
        a__	:  Union[str, Any]     =   rust_tokenizer.tokenize(lowerCamelCase__       )
        self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )

        a__	:  Optional[int]     =   tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
        a__	:  Union[str, Any]     =   rust_tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
        self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )

        a__	:  List[str]     =   self.get_rust_tokenizer()
        a__	:  Tuple     =   tokenizer.encode(lowerCamelCase__       )
        a__	:  Optional[Any]     =   rust_tokenizer.encode(lowerCamelCase__       )
        self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )

  @slow
  def    _UpperCamelCase( self			:  List[str]       ):
        a__	:  Union[str, Any]     =   "Hello World!"
        a__	:  List[str]     =   [2, 31_227, 4_447, 35]

        self.assertListEqual(lowerCamelCase__				,				self.big_tokenizer.encode(lowerCamelCase__       )       )

  @slow
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  Optional[int]     =   (
            "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
            " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
        )
        # fmt: off
        a__	:  Union[str, Any]     =   [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
        # fmt: on

        self.assertListEqual(lowerCamelCase__				,				self.big_tokenizer.encode(lowerCamelCase__       )       )

  @slow
  def    _UpperCamelCase( self			:  List[Any]       ):
        # fmt: off
        a__	:  Optional[int]     =   {
            "input_ids": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
            "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
        }  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCamelCase__				,				model_name="facebook/xglm-564M"				,				padding=lowerCamelCase__				,				)







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


a__		     =     {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    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						List[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Tuple             =      torchvision.models.resnetaaa(pretrained=lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      list(model.children()	)[:-2]
						__UpperCamelCase					:	List[str]             =      nn.Sequential(*lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.pool(self.model(lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      torch.flatten(lowerCAmelCase					,     start_dim=2	)
						__UpperCamelCase					:	Optional[int]             =      out.transpose(1					,     2	).contiguous()
						return out  # BxNx2048






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      [json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
						__UpperCamelCase					:	Optional[Any]             =      os.path.dirname(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      tokenizer
						__UpperCamelCase					:	Dict             =      labels
						__UpperCamelCase					:	int             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      max_seq_length

						__UpperCamelCase					:	int             =      transforms






	def __len__(					self		:						Union[str, Any]	)	->       List[Any]:







						"""simple docstring"""







						return len(self.data	)






	def __getitem__(					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					,     add_special_tokens=lowerCAmelCase	)	)
						__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      sentence[0], sentence[1:-1], sentence[-1]
						__UpperCamelCase					:	Optional[int]             =      sentence[: self.max_seq_length]

						__UpperCamelCase					:	List[str]             =      torch.zeros(self.n_classes	)
						__UpperCamelCase					:	List[str]             =      1

						__UpperCamelCase					:	List[Any]             =      Image.open(os.path.join(self.data_dir					,     self.data[index]["""img"""]	)	).convert("""RGB"""	)
						__UpperCamelCase					:	Dict             =      self.transforms(lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      Counter()
						for row in self.data:
											label_freqs.update(row["""label"""]	)
						return label_freqs






def 				A__ (snake_case   :		int				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      [len(row["""sentence"""]				) for row in batch]
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      len(snake_case				), max(snake_case				)

					__UpperCamelCase					:	List[str]             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)
					__UpperCamelCase					:	Tuple             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)

					for i_batch, (input_row, length) in enumerate(zip(snake_case		,	snake_case				)				):
										__UpperCamelCase					:	Optional[Any]             =      input_row["""sentence"""]
										__UpperCamelCase					:	Any             =      1

					__UpperCamelCase					:	List[Any]             =      torch.stack([row["""image"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""label"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""image_start_token"""] for row in batch]				)
					__UpperCamelCase					:	List[str]             =      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__ ()     ->						Optional[Any]:
					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__ ()     ->						Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(2_56				),
					        transforms.CenterCrop(2_24				),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017]		,	std=[0.12221994, 0.12145835, 0.14380469]		,	),
					    ]				)






