

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						lowerCAmelCase							:						List[str]     			=		False

if is_vision_available():
						from PIL import Image

						from transformers import (
						    AutoProcessor,
						    PixaStructImageProcessor,
						    PixaStructProcessor,
						    PreTrainedTokenizerFast,
						    TaTokenizer,
						)



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11	, reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''	, )
@require_vision
@require_torch
class 						__lowercase       (				unittest.TestCase			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									SCREAMING_SNAKE_CASE_:				List[Any]							= tempfile.mkdtemp()

									SCREAMING_SNAKE_CASE_:				List[Any]							= PixaStructImageProcessor()
									SCREAMING_SNAKE_CASE_:				Tuple							= TaTokenizer.from_pretrained("t5-small")

									SCREAMING_SNAKE_CASE_:				Any							= PixaStructProcessor(lowerCAmelCase__     ,		lowerCAmelCase__)

									processor.save_pretrained(self.tmpdirname)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]     ,		**lowerCAmelCase__						:  int):
									return AutoProcessor.from_pretrained(self.tmpdirname     ,		**lowerCAmelCase__).tokenizer



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		**lowerCAmelCase__						:  Any):
									return AutoProcessor.from_pretrained(self.tmpdirname     ,		**lowerCAmelCase__).image_processor



			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									shutil.rmtree(self.tmpdirname)



			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									SCREAMING_SNAKE_CASE_:				Any							= [np.random.randint(255     ,		size=(3, 30, 400)     ,		dtype=np.uinta)]

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

									return image_inputs



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									SCREAMING_SNAKE_CASE_:				Dict							= PixaStructProcessor(tokenizer=self.get_tokenizer()     ,		image_processor=self.get_image_processor())
									processor.save_pretrained(self.tmpdirname)

									SCREAMING_SNAKE_CASE_:				Dict							= self.get_tokenizer(bos_token="(BOS)"     ,		eos_token="(EOS)")
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.get_image_processor(do_normalize=lowerCAmelCase__     ,		padding_value=1.0)

									SCREAMING_SNAKE_CASE_:				Any							= PixaStructProcessor.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  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= self.get_image_processor()
									SCREAMING_SNAKE_CASE_:				int							= self.get_tokenizer()

									SCREAMING_SNAKE_CASE_:				Optional[int]							= PixaStructProcessor(tokenizer=lowerCAmelCase__     ,		image_processor=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				List[Any]							= self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE_:				Optional[int]							= image_processor(lowerCAmelCase__     ,		return_tensors="np")
									SCREAMING_SNAKE_CASE_:				Any							= 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  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									SCREAMING_SNAKE_CASE_:				Tuple							= self.get_image_processor()
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.get_tokenizer()

									SCREAMING_SNAKE_CASE_:				Tuple							= PixaStructProcessor(tokenizer=lowerCAmelCase__     ,		image_processor=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Tuple							= "lower newer"

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= processor(text=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				str							= tokenizer(lowerCAmelCase__     ,		return_token_type_ids=lowerCAmelCase__     ,		add_special_tokens=lowerCAmelCase__)

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



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self.get_image_processor()
									SCREAMING_SNAKE_CASE_:				str							= self.get_tokenizer()

									SCREAMING_SNAKE_CASE_:				Optional[int]							= PixaStructProcessor(tokenizer=lowerCAmelCase__     ,		image_processor=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				List[Any]							= "lower newer"
									SCREAMING_SNAKE_CASE_:				List[str]							= self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE_:				str							= processor(text=lowerCAmelCase__     ,		images=lowerCAmelCase__)

									self.assertListEqual(
									    list(inputs.keys())     ,		["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"])

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



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									SCREAMING_SNAKE_CASE_:				Any							= self.get_image_processor()
									SCREAMING_SNAKE_CASE_:				List[Any]							= self.get_tokenizer()

									SCREAMING_SNAKE_CASE_:				int							= PixaStructProcessor(tokenizer=lowerCAmelCase__     ,		image_processor=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				int							= "lower newer"
									SCREAMING_SNAKE_CASE_:				List[Any]							= self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE_:				List[Any]							= processor(text=lowerCAmelCase__     ,		images=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				List[str]							= [512, 1024, 2048, 4096]
									SCREAMING_SNAKE_CASE_:				Any							= [770, 770, 770, 770]
									# with text
									for i, max_patch in enumerate(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:				Tuple							= processor(text=lowerCAmelCase__     ,		images=lowerCAmelCase__     ,		max_patches=lowerCAmelCase__)
															self.assertEqual(inputs["flattened_patches"][0].shape[0]     ,		lowerCAmelCase__)
															self.assertEqual(inputs["flattened_patches"][0].shape[1]     ,		expected_hidden_size[i])

									# without text input
									for i, max_patch in enumerate(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:				Optional[int]							= processor(images=lowerCAmelCase__     ,		max_patches=lowerCAmelCase__)
															self.assertEqual(inputs["flattened_patches"][0].shape[0]     ,		lowerCAmelCase__)
															self.assertEqual(inputs["flattened_patches"][0].shape[1]     ,		expected_hidden_size[i])



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.get_image_processor()
									SCREAMING_SNAKE_CASE_:				List[Any]							= self.get_tokenizer()

									SCREAMING_SNAKE_CASE_:				Any							= PixaStructProcessor(tokenizer=lowerCAmelCase__     ,		image_processor=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Optional[int]							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									SCREAMING_SNAKE_CASE_:				Dict							= processor.batch_decode(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				str							= tokenizer.batch_decode(lowerCAmelCase__)

									self.assertListEqual(lowerCAmelCase__     ,		lowerCAmelCase__)




			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									SCREAMING_SNAKE_CASE_:				str							= self.get_image_processor()
									SCREAMING_SNAKE_CASE_:				Any							= self.get_tokenizer()

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= PixaStructProcessor(tokenizer=lowerCAmelCase__     ,		image_processor=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Optional[int]							= "lower newer"
									SCREAMING_SNAKE_CASE_:				List[str]							= self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= processor(text=lowerCAmelCase__     ,		images=lowerCAmelCase__)

									# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
									self.assertListEqual(
									    list(inputs.keys())     ,		["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"])

									SCREAMING_SNAKE_CASE_:				int							= processor(text=lowerCAmelCase__)

									# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
									self.assertListEqual(list(inputs.keys())     ,		["input_ids", "attention_mask"])









import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


lowerCAmelCase							:						Tuple     			=		datasets.utils.logging.get_logger(__name__)



@dataclass
class 						__lowercase       (				datasets.BuilderConfig			):




			"""simple docstring"""







			_UpperCAmelCase     :							Optional[datasets.Features]    							=  None
			_UpperCAmelCase     :							str    							=  "utf-8"
			_UpperCAmelCase     :							Optional[str]    							=  None
			_UpperCAmelCase     :							int    							=  10 << 20  # 10MB
			_UpperCAmelCase     :							bool    							=  False
			_UpperCAmelCase     :							str    							=  "line"







class 						__lowercase       (				datasets.ArrowBasedBuilder			):




			"""simple docstring"""







			_UpperCAmelCase     :							List[Any]    							=  TextConfig



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									return datasets.DatasetInfo(features=self.config.features)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]     ,		lowerCAmelCase__						:  Tuple):
									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}")
									SCREAMING_SNAKE_CASE_:				Dict							= dl_manager.download_and_extract(self.config.data_files)
									if isinstance(lowerCAmelCase__     ,		(str, list, tuple)):
															SCREAMING_SNAKE_CASE_:				Dict							= data_files
															if isinstance(lowerCAmelCase__     ,		lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_:				Dict							= [files]
															SCREAMING_SNAKE_CASE_:				List[str]							= [dl_manager.iter_files(lowerCAmelCase__) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,		gen_kwargs={"files": files})]
									SCREAMING_SNAKE_CASE_:				Any							= []
									for split_name, files in data_files.items():
															if isinstance(lowerCAmelCase__     ,		lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_:				List[Any]							= [files]
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= [dl_manager.iter_files(lowerCAmelCase__) for file in files]
															splits.append(datasets.SplitGenerator(name=lowerCAmelCase__     ,		gen_kwargs={"files": files}))
									return splits



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]     ,		lowerCAmelCase__						:  pa.Table):
									if self.config.features is not None:
															SCREAMING_SNAKE_CASE_:				Optional[int]							= self.config.features.arrow_schema
															if all(not require_storage_cast(lowerCAmelCase__) for feature in self.config.features.values()):
																					# cheaper cast
																					SCREAMING_SNAKE_CASE_:				List[str]							= pa_table.cast(lowerCAmelCase__)
															else:
																					# more expensive cast; allows str <-> int/float or str to Audio for example
																					SCREAMING_SNAKE_CASE_:				List[Any]							= table_cast(lowerCAmelCase__     ,		lowerCAmelCase__)
															return pa_table
									else:
															return pa_table.cast(pa.schema({"text": pa.string()}))




			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  Dict):
									SCREAMING_SNAKE_CASE_:				str							= list(self.config.features) if self.config.features is not None else ["text"]
									for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__)):
															# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
															with open(lowerCAmelCase__     ,		encoding=self.config.encoding     ,		errors=self.config.errors) as f:
																					if self.config.sample_by == "line":
																											SCREAMING_SNAKE_CASE_:				Any							= 0
																											while True:
																																	SCREAMING_SNAKE_CASE_:				str							= f.read(self.config.chunksize)
																																	if not batch:
																																							break
																																	batch += f.readline()  # finish current line
																																	# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
																																	SCREAMING_SNAKE_CASE_:				Optional[Any]							= StringIO(lowerCAmelCase__).readlines()
																																	if not self.config.keep_linebreaks:
																																							SCREAMING_SNAKE_CASE_:				Optional[Any]							= [line.rstrip("\n") for line in batch]
																																	SCREAMING_SNAKE_CASE_:				Any							= pa.Table.from_arrays([pa.array(lowerCAmelCase__)]     ,		names=lowerCAmelCase__)
																																	# 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 (file_idx, batch_idx), self._cast_table(lowerCAmelCase__)
																																	batch_idx += 1
																					elif self.config.sample_by == "paragraph":
																											SCREAMING_SNAKE_CASE_:				List[str]							= 0
																											SCREAMING_SNAKE_CASE_:				Tuple							= ""
																											while True:
																																	SCREAMING_SNAKE_CASE_:				Dict							= f.read(self.config.chunksize)
																																	if not new_batch:
																																							break
																																	batch += new_batch
																																	batch += f.readline()  # finish current line
																																	SCREAMING_SNAKE_CASE_:				List[str]							= batch.split("\n\n")
																																	SCREAMING_SNAKE_CASE_:				Optional[int]							= pa.Table.from_arrays(
																																	    [pa.array([example for example in batch[:-1] if example])]     ,		names=lowerCAmelCase__)
																																	# 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 (file_idx, batch_idx), self._cast_table(lowerCAmelCase__)
																																	batch_idx += 1
																																	SCREAMING_SNAKE_CASE_:				Optional[Any]							= batch[-1]
																											if batch:
																																	SCREAMING_SNAKE_CASE_:				int							= pa.Table.from_arrays([pa.array([batch])]     ,		names=lowerCAmelCase__)
																																	yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__)
																					elif self.config.sample_by == "document":
																											SCREAMING_SNAKE_CASE_:				List[Any]							= f.read()
																											SCREAMING_SNAKE_CASE_:				Optional[int]							= pa.Table.from_arrays([pa.array([text])]     ,		names=lowerCAmelCase__)
																											yield file_idx, self._cast_table(lowerCAmelCase__)






