





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      _SCREAMING_SNAKE_CASE		(			unittest.TestCase					):







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__:   Optional[int]    						=tempfile.mkdtemp()

   # fmt: off
   lowerCamelCase__:   Union[str, 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
   lowerCamelCase__:   int    						=dict(zip(UpperCAmelCase_ ,				range(len(UpperCAmelCase_))))
   lowerCamelCase__:   int    						=["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
   lowerCamelCase__:   Tuple    						={"unk_token": "<unk>"}

   lowerCamelCase__:   Dict    						=os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES["vocab_file"])
   lowerCamelCase__:   List[str]    						=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(UpperCAmelCase_) + "\n")
   with open(self.merges_file ,				"w" ,				encoding="utf-8") as fp:
     fp.write("\n".join(UpperCAmelCase_))

   lowerCamelCase__:   Optional[int]    						={
       "do_resize": True,
       "size": 20,
       "do_center_crop": True,
       "crop_size": 18,
       "do_normalize": True,
       "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
       "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
   }
   lowerCamelCase__:   Tuple    						=os.path.join(self.tmpdirname ,				UpperCAmelCase_)
   with open(self.image_processor_file ,				"w" ,				encoding="utf-8") as fp:
     json.dump(UpperCAmelCase_ ,				UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict ,				**UpperCAmelCase_       :     Union[str, Any]) ->Union[str, Any]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple ,				**UpperCAmelCase_       :     Union[str, Any]) ->List[str]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any ,				**UpperCAmelCase_       :     Optional[int]) ->Optional[int]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any]) ->Tuple:



   '''simple docstring'''



   shutil.rmtree(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->Any:



   '''simple docstring'''



   lowerCamelCase__:   Tuple    						=[np.random.randint(255 ,				size=(3, 30, 400) ,				dtype=np.uinta)]

   lowerCamelCase__:   str    						=[Image.fromarray(np.moveaxis(UpperCAmelCase_ ,				0 ,				-1)) for x in image_inputs]

   return image_inputs

 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->str:



   '''simple docstring'''



   lowerCamelCase__:   Any    						=self.get_tokenizer()
   lowerCamelCase__:   str    						=self.get_rust_tokenizer()
   lowerCamelCase__:   str    						=self.get_image_processor()

   lowerCamelCase__:   Optional[int]    						=CLIPSegProcessor(tokenizer=UpperCAmelCase_ ,				image_processor=UpperCAmelCase_)
   processor_slow.save_pretrained(self.tmpdirname)
   lowerCamelCase__:   int    						=CLIPSegProcessor.from_pretrained(self.tmpdirname ,				use_fast=UpperCAmelCase_)

   lowerCamelCase__:   Any    						=CLIPSegProcessor(tokenizer=UpperCAmelCase_ ,				image_processor=UpperCAmelCase_)
   processor_fast.save_pretrained(self.tmpdirname)
   lowerCamelCase__:   Optional[Any]    						=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 ,				UpperCAmelCase_)
   self.assertIsInstance(processor_fast.tokenizer ,				UpperCAmelCase_)

   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 ,				UpperCAmelCase_)
   self.assertIsInstance(processor_fast.image_processor ,				UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:   int    						=CLIPSegProcessor(tokenizer=self.get_tokenizer() ,				image_processor=self.get_image_processor())
   processor.save_pretrained(self.tmpdirname)

   lowerCamelCase__:   Any    						=self.get_tokenizer(bos_token="(BOS)" ,				eos_token="(EOS)")
   lowerCamelCase__:   Dict    						=self.get_image_processor(do_normalize=UpperCAmelCase_ ,				padding_value=1.0)

   lowerCamelCase__:   Optional[int]    						=CLIPSegProcessor.from_pretrained(
       self.tmpdirname ,				bos_token="(BOS)" ,				eos_token="(EOS)" ,				do_normalize=UpperCAmelCase_ ,				padding_value=1.0)

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

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

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



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=self.get_image_processor()
   lowerCamelCase__:   Union[str, Any]    						=self.get_tokenizer()

   lowerCamelCase__:   Union[str, Any]    						=CLIPSegProcessor(tokenizer=UpperCAmelCase_ ,				image_processor=UpperCAmelCase_)

   lowerCamelCase__:   int    						=self.prepare_image_inputs()

   lowerCamelCase__:   List[Any]    						=image_processor(UpperCAmelCase_ ,				return_tensors="np")
   lowerCamelCase__:   str    						=processor(images=UpperCAmelCase_ ,				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 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:   Optional[Any]    						=self.get_image_processor()
   lowerCamelCase__:   Tuple    						=self.get_tokenizer()

   lowerCamelCase__:   Tuple    						=CLIPSegProcessor(tokenizer=UpperCAmelCase_ ,				image_processor=UpperCAmelCase_)

   lowerCamelCase__:   List[str]    						="lower newer"

   lowerCamelCase__:   Dict    						=processor(text=UpperCAmelCase_)

   lowerCamelCase__:   Tuple    						=tokenizer(UpperCAmelCase_)

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

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



   '''simple docstring'''



   lowerCamelCase__:   Tuple    						=self.get_image_processor()
   lowerCamelCase__:   Union[str, Any]    						=self.get_tokenizer()

   lowerCamelCase__:   Optional[Any]    						=CLIPSegProcessor(tokenizer=UpperCAmelCase_ ,				image_processor=UpperCAmelCase_)

   lowerCamelCase__:   Tuple    						="lower newer"
   lowerCamelCase__:   Any    						=self.prepare_image_inputs()

   lowerCamelCase__:   List[Any]    						=processor(text=UpperCAmelCase_ ,				images=UpperCAmelCase_)

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

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

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



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=self.get_image_processor()
   lowerCamelCase__:   Optional[int]    						=self.get_tokenizer()

   lowerCamelCase__:   Dict    						=CLIPSegProcessor(tokenizer=UpperCAmelCase_ ,				image_processor=UpperCAmelCase_)

   lowerCamelCase__:   List[Any]    						=self.prepare_image_inputs()
   lowerCamelCase__:   Union[str, Any]    						=self.prepare_image_inputs()

   lowerCamelCase__:   str    						=processor(images=UpperCAmelCase_ ,				visual_prompt=UpperCAmelCase_)

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

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







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



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						=self.get_image_processor()
   lowerCamelCase__:   int    						=self.get_tokenizer()

   lowerCamelCase__:   Optional[int]    						=CLIPSegProcessor(tokenizer=UpperCAmelCase_ ,				image_processor=UpperCAmelCase_)

   lowerCamelCase__:   Any    						=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   lowerCamelCase__:   int    						=processor.batch_decode(UpperCAmelCase_)
   lowerCamelCase__:   Tuple    						=tokenizer.batch_decode(UpperCAmelCase_)

   self.assertListEqual(UpperCAmelCase_ ,				UpperCAmelCase_)








import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__A             =       datasets.utils.logging.get_logger(__name__)



@dataclass
class      _SCREAMING_SNAKE_CASE		(			datasets.BuilderConfig					):







 '''simple docstring'''
 lowercase_	=    1_0000
 lowercase_	=    None
 lowercase_	=    None



class      _SCREAMING_SNAKE_CASE		(			datasets.ArrowBasedBuilder					):







 '''simple docstring'''
 lowercase_	=    ParquetConfig

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



   '''simple docstring'''



   return datasets.DatasetInfo(features=self.config.features)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any ,				UpperCAmelCase_       :     Any) ->Any:



   '''simple docstring'''



   if not self.config.data_files:
     raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""")
   lowerCamelCase__:   Optional[Any]    						=dl_manager.download_and_extract(self.config.data_files)
   if isinstance(UpperCAmelCase_ ,				(str, list, tuple)):
     lowerCamelCase__:   Any    						=data_files
     if isinstance(UpperCAmelCase_ ,				UpperCAmelCase_):
       lowerCamelCase__:   Union[str, Any]    						=[files]
     # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
     lowerCamelCase__:   Optional[int]    						=[dl_manager.iter_files(UpperCAmelCase_) for file in files]
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,				gen_kwargs={"files": files})]
   lowerCamelCase__:   int    						=[]
   for split_name, files in data_files.items():
     if isinstance(UpperCAmelCase_ ,				UpperCAmelCase_):
       lowerCamelCase__:   List[Any]    						=[files]
     # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
     lowerCamelCase__:   str    						=[dl_manager.iter_files(UpperCAmelCase_) for file in files]
     # Infer features is they are stoed in the arrow schema
     if self.info.features is None:
       for file in itertools.chain.from_iterable(UpperCAmelCase_):
         with open(UpperCAmelCase_ ,				"rb") as f:
           lowerCamelCase__:   Union[str, Any]    						=datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase_))
         break
     splits.append(datasets.SplitGenerator(name=UpperCAmelCase_ ,				gen_kwargs={"files": files}))
   return splits

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any ,				UpperCAmelCase_       :     pa.Table) ->pa.Table:



   '''simple docstring'''



   if self.info.features is not None:
     # more expensive cast to support nested features with keys in a different order
     # allows str <-> int/float or str to Audio for example
     lowerCamelCase__:   str    						=table_cast(UpperCAmelCase_ ,				self.info.features.arrow_schema)
   return pa_table







 def 							SCREAMING_SNAKE_CASE_      (self       :     str ,				UpperCAmelCase_       :     Optional[Any]) ->int:



   '''simple docstring'''



   lowerCamelCase__:   str    						=self.info.features.arrow_schema if self.info.features is not None else None
   if self.info.features is not None and self.config.columns is not None:
     if sorted(field.name for field in schema) != sorted(self.config.columns):
       raise ValueError(
           F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""")
   for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase_)):
     with open(UpperCAmelCase_ ,				"rb") as f:
       lowerCamelCase__:   Optional[Any]    						=pq.ParquetFile(UpperCAmelCase_)
       try:
         for batch_idx, record_batch in enumerate(
             parquet_file.iter_batches(batch_size=self.config.batch_size ,				columns=self.config.columns)):
           lowerCamelCase__:   Optional[Any]    						=pa.Table.from_batches([record_batch])
           # Uncomment for debugging (will print the Arrow table size and elements)
           # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
           # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
           yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCAmelCase_)
       except ValueError as e:
         logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_)}: {e}""")
         raise

