




import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger
class 	_UpperCamelCase					( UpperCamelCase__						):
      def __init__(	self      :Optional[Any]       ,					lowerCamelCase      :Optional[Any]       ,					lowerCamelCase      :Optional[Any]					)   ->	Dict:
       UpperCAmelCase__							       =  params

       UpperCAmelCase__							       =  np.array(lowerCAmelCase__					)
       UpperCAmelCase__							       =  np.array([len(lowerCAmelCase__					) for t in data]					)

       self.check()
       self.remove_long_sequences()
       self.remove_empty_sequences()
       self.remove_unknown_sequences()
       self.check()
       self.print_statistics()
      def __getitem__(	self      :List[Any]       ,					lowerCamelCase      :List[Any]					)   ->	str:
       return (self.token_ids[index], self.lengths[index])
      def __len__(	self      :Dict					)   ->	int:
       return len(self.lengths					)
      def 		UpperCAmelCase_  (	self      :List[Any]					)   ->	Any:
       assert len(self.token_ids					) == len(self.lengths					)
       assert all(self.lengths[i] == len(self.token_ids[i]					) for i in range(len(self.lengths					)					)					)
      def 		UpperCAmelCase_  (	self      :List[str]					)   ->	List[str]:
       UpperCAmelCase__							       =  self.params.max_model_input_size
       UpperCAmelCase__							       =  self.lengths > max_len
       logger.info(f'''Splitting {sum(lowerCAmelCase__					)} too long sequences.'''					)

       def divide_chunks(lowerCamelCase      :Dict       ,					lowerCamelCase      :str					):
        return [l[i : i + n] for i in range(0       ,					len(lowerCAmelCase__					)       ,					lowerCAmelCase__					)]

       UpperCAmelCase__							       =  []
       UpperCAmelCase__							       =  []
       if self.params.mlm:
        UpperCAmelCase__							       =  self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
       else:
        UpperCAmelCase__							       =  self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

       for seq_, len_ in zip(self.token_ids       ,					self.lengths					):
        assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
        if len_ <= max_len:
         new_tok_ids.append(seq_					)
         new_lengths.append(len_					)
        else:
         UpperCAmelCase__							       =  []
         for sub_s in divide_chunks(seq_       ,					max_len - 2					):
          if sub_s[0] != cls_id:
           UpperCAmelCase__							       =  np.insert(lowerCAmelCase__       ,					0       ,					lowerCAmelCase__					)
          if sub_s[-1] != sep_id:
           UpperCAmelCase__							       =  np.insert(lowerCAmelCase__       ,					len(lowerCAmelCase__					)       ,					lowerCAmelCase__					)
          assert len(lowerCAmelCase__					) <= max_len
          assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
          sub_seqs.append(lowerCAmelCase__					)

         new_tok_ids.extend(lowerCAmelCase__					)
         new_lengths.extend([len(lowerCAmelCase__					) for l in sub_seqs]					)

       UpperCAmelCase__							       =  np.array(lowerCAmelCase__					)
       UpperCAmelCase__							       =  np.array(lowerCAmelCase__					)
      def 		UpperCAmelCase_  (	self      :str					)   ->	Optional[int]:
       UpperCAmelCase__							       =  len(self					)
       UpperCAmelCase__							       =  self.lengths > 11
       UpperCAmelCase__							       =  self.token_ids[indices]
       UpperCAmelCase__							       =  self.lengths[indices]
       UpperCAmelCase__							       =  len(self					)
       logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''					)
      def 		UpperCAmelCase_  (	self      :List[str]					)   ->	List[Any]:




       if "unk_token" not in self.params.special_tok_ids:
        return
       else:
        UpperCAmelCase__							       =  self.params.special_tok_ids["unk_token"]
       UpperCAmelCase__							       =  len(self					)
       UpperCAmelCase__							       =  np.array([np.count_nonzero(a == unk_token_id					) for a in self.token_ids]					)
       UpperCAmelCase__							       =  (unk_occs / self.lengths) < 0.5
       UpperCAmelCase__							       =  self.token_ids[indices]
       UpperCAmelCase__							       =  self.lengths[indices]
       UpperCAmelCase__							       =  len(self					)
       logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''					)
      def 		UpperCAmelCase_  (	self      :Union[str, Any]					)   ->	Tuple:




       if not self.params.is_master:
        return
       logger.info(f'''{len(self					)} sequences'''					)
       # data_len = sum(self.lengths)
       # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
       # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

       # unk_idx = self.params.special_tok_ids['unk_token']
       # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
       # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :Optional[int]					)   ->	List[Any]:
       UpperCAmelCase__							       =  [t[0] for t in batch]
       UpperCAmelCase__							       =  [t[1] for t in batch]
       assert len(lowerCAmelCase__					) == len(lowerCAmelCase__					)

       # Max for paddings
       UpperCAmelCase__							       =  max(lowerCAmelCase__					)

       # Pad token ids
       if self.params.mlm:
        UpperCAmelCase__							       =  self.params.special_tok_ids["pad_token"]
       else:
        UpperCAmelCase__							       =  self.params.special_tok_ids["unk_token"]
       UpperCAmelCase__							       =  [list(t.astype(lowerCAmelCase__					)					) + [pad_idx] * (max_seq_len_ - len(lowerCAmelCase__					)) for t in token_ids]
       assert len(tk_					) == len(lowerCAmelCase__					)
       assert all(len(lowerCAmelCase__					) == max_seq_len_ for t in tk_					)

       UpperCAmelCase__							       =  torch.tensor(tk_					)  # (bs, max_seq_len_)
       UpperCAmelCase__							       =  torch.tensor(lowerCAmelCase__					)  # (bs)
       return tk_t, lg_t












import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import CLIPSegProcessor, ViTImageProcessor







@require_vision
class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Any										=tempfile.mkdtemp()

        # fmt: off
        a__	:					List[Any]										=["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
        # fmt: on
        a__	:					str										=dict(zip(lowerCAmelCase__		,      range(len(lowerCAmelCase__							)							)							)							)
        a__	:					List[Any]										=["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        a__	:					Optional[int]										={"unk_token": "<unk>"}

        a__	:					Optional[Any]										=os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]							)
        a__	:					Tuple										=os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["merges_file"]							)
        with open(self.vocab_file		,      "w"		,      encoding="utf-8"							) as fp:
          fp.write(json.dumps(lowerCAmelCase__							) + "\n"							)
        with open(self.merges_file		,      "w"		,      encoding="utf-8"							) as fp:
          fp.write("\n".join(lowerCAmelCase__							)							)

        a__	:					Optional[Any]										={
            "do_resize": True,
            "size": 2_0,
            "do_center_crop": True,
            "crop_size": 1_8,
            "do_normalize": True,
            "image_mean": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            "image_std": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        a__	:					Dict										=os.path.join(self.tmpdirname		,      lowerCAmelCase__							)
        with open(self.image_processor_file		,      "w"		,      encoding="utf-8"							) as fp:
          json.dump(lowerCAmelCase__		,      lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        return CLIPTokenizer.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        return ViTImageProcessor.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        shutil.rmtree(self.tmpdirname							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=[np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

        a__	:					List[Any]										=[Image.fromarray(np.moveaxis(lowerCAmelCase__		,      0		,      -1							)							) for x in image_inputs]

        return image_inputs







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_tokenizer()
        a__	:					int										=self.get_rust_tokenizer()
        a__	:					List[str]										=self.get_image_processor()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_slow.save_pretrained(self.tmpdirname							)
        a__	:					Optional[Any]										=CLIPSegProcessor.from_pretrained(self.tmpdirname		,      use_fast=lowerCAmelCase__							)

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_fast.save_pretrained(self.tmpdirname							)
        a__	:					Dict										=CLIPSegProcessor.from_pretrained(self.tmpdirname							)

        self.assertEqual(processor_slow.tokenizer.get_vocab()		,      tokenizer_slow.get_vocab()							)
        self.assertEqual(processor_fast.tokenizer.get_vocab()		,      tokenizer_fast.get_vocab()							)
        self.assertEqual(tokenizer_slow.get_vocab()		,      tokenizer_fast.get_vocab()							)
        self.assertIsInstance(processor_slow.tokenizer		,      lowerCAmelCase__							)
        self.assertIsInstance(processor_fast.tokenizer		,      lowerCAmelCase__							)

        self.assertEqual(processor_slow.image_processor.to_json_string()		,      image_processor.to_json_string()							)
        self.assertEqual(processor_fast.image_processor.to_json_string()		,      image_processor.to_json_string()							)
        self.assertIsInstance(processor_slow.image_processor		,      lowerCAmelCase__							)
        self.assertIsInstance(processor_fast.image_processor		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					List[str]										=CLIPSegProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
        processor.save_pretrained(self.tmpdirname							)

        a__	:					str										=self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"							)
        a__	:					int										=self.get_image_processor(do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        a__	:					Optional[Any]										=CLIPSegProcessor.from_pretrained(
            self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        self.assertEqual(processor.tokenizer.get_vocab()		,      tokenizer_add_kwargs.get_vocab()							)
        self.assertIsInstance(processor.tokenizer		,      lowerCAmelCase__							)

        self.assertEqual(processor.image_processor.to_json_string()		,      image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					str										=self.get_image_processor()
        a__	:					Optional[int]										=self.get_tokenizer()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					str										=self.prepare_image_inputs()

        a__	:					Any										=image_processor(lowerCAmelCase__		,      return_tensors="np"							)
        a__	:					Optional[int]										=processor(images=lowerCAmelCase__		,      return_tensors="np"							)

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()		,      input_processor[key].sum()		,      delta=1E-2							)







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



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					List[Any]										=self.get_tokenizer()

        a__	:					Optional[int]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Union[str, Any]										="lower newer"

        a__	:					List[str]										=processor(text=lowerCAmelCase__							)

        a__	:					str										=tokenizer(lowerCAmelCase__							)

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]		,      encoded_processor[key]							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Any										=self.get_image_processor()
        a__	:					Dict										=self.get_tokenizer()

        a__	:					Union[str, Any]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										="lower newer"
        a__	:					int										=self.prepare_image_inputs()

        a__	:					Any										=processor(text=lowerCAmelCase__		,      images=lowerCAmelCase__							)

        self.assertListEqual(list(inputs.keys()							)		,      ["input_ids", "attention_mask", "pixel_values"]							)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__							):
          processor()







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_image_processor()
        a__	:					Optional[Any]										=self.get_tokenizer()

        a__	:					str										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					int										=self.prepare_image_inputs()
        a__	:					Union[str, Any]										=self.prepare_image_inputs()

        a__	:					Tuple										=processor(images=lowerCAmelCase__		,      visual_prompt=lowerCAmelCase__							)

        self.assertListEqual(list(inputs.keys()							)		,      ["pixel_values", "conditional_pixel_values"]							)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__							):
          processor()







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




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

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        a__	:					Optional[Any]										=processor.batch_decode(lowerCAmelCase__							)
        a__	:					Dict										=tokenizer.batch_decode(lowerCAmelCase__							)

        self.assertListEqual(lowerCAmelCase__		,      lowerCAmelCase__							)






