

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import torch







class 					lowercase							(			TensorFormatter[Mapping, '''torch.Tensor''', Mapping]     ):




				def __init__(   self ,  snake_case=None ,  **snake_case			):
											super().__init__(features=snake_case			)
											snake_case_     							=       torch_tensor_kwargs
											import torch  # noqa import torch at initialization




				def        a				(   self ,  snake_case			):
											import torch

											if isinstance(snake_case ,  snake_case			) and column:
																		if all(
																		    isinstance(snake_case ,  torch.Tensor			) and x.shape == column[0].shape and x.dtype == column[0].dtype
																		    for x in column			):
																									return torch.stack(snake_case			)
											return column




				def        a				(   self ,  snake_case			):
											import torch

											if isinstance(snake_case ,  (str, bytes, type(snake_case			))			):
																		return value
											elif isinstance(snake_case ,  (np.character, np.ndarray)			) and np.issubdtype(value.dtype ,  np.character			):
																		return value.tolist()

											snake_case_     							=       {}

											if isinstance(snake_case ,  (np.number, np.ndarray)			) and np.issubdtype(value.dtype ,  np.integer			):
																		snake_case_     							=       {'dtype': torch.intaa}
											elif isinstance(snake_case ,  (np.number, np.ndarray)			) and np.issubdtype(value.dtype ,  np.floating			):
																		snake_case_     							=       {'dtype': torch.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																		import PIL.Image

																		if isinstance(snake_case ,  PIL.Image.Image			):
																									snake_case_     							=       np.asarray(snake_case			)
											return torch.tensor(snake_case ,  **{**default_dtype, **self.torch_tensor_kwargs}			)




				def        a				(   self ,  snake_case			):
											import torch

											# support for torch, tf, jax etc.
											if hasattr(snake_case ,  '__array__'			) and not isinstance(snake_case ,  torch.Tensor			):
																		snake_case_     							=       data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(snake_case ,  np.ndarray			):
																		if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																									return self._consolidate([self.recursive_tensorize(snake_case			) for substruct in data_struct]			)
											elif isinstance(snake_case ,  (list, tuple)			):
																		return self._consolidate([self.recursive_tensorize(snake_case			) for substruct in data_struct]			)
											return self._tensorize(snake_case			)




				def        a				(   self ,  snake_case			):
											return map_nested(self._recursive_tensorize ,  snake_case ,  map_list=snake_case			)




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self.numpy_arrow_extractor().extract_row(snake_case			)
											snake_case_     							=       self.python_features_decoder.decode_row(snake_case			)
											return self.recursive_tensorize(snake_case			)




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self.numpy_arrow_extractor().extract_column(snake_case			)
											snake_case_     							=       self.python_features_decoder.decode_column(snake_case ,  pa_table.column_names[0]			)
											snake_case_     							=       self.recursive_tensorize(snake_case			)
											snake_case_     							=       self._consolidate(snake_case			)
											return column




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self.numpy_arrow_extractor().extract_batch(snake_case			)
											snake_case_     							=       self.python_features_decoder.decode_batch(snake_case			)
											snake_case_     							=       self.recursive_tensorize(snake_case			)
											for column_name in batch:
																		snake_case_     							=       self._consolidate(batch[column_name]			)
											return batch







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											snake_case_     							=       tempfile.mkdtemp()

											snake_case_     							=       BlipImageProcessor()
											snake_case_     							=       GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'			)

											snake_case_     							=       BlipaProcessor(snake_case ,  snake_case			)

											processor.save_pretrained(self.tmpdirname			)




				def        a				(   self ,  **snake_case			):
											return AutoProcessor.from_pretrained(self.tmpdirname ,  **snake_case			).tokenizer




				def        a				(   self ,  **snake_case			):
											return AutoProcessor.from_pretrained(self.tmpdirname ,  **snake_case			).image_processor




				def        a				(   self			):
											shutil.rmtree(self.tmpdirname			)




				def        a				(   self			):
											snake_case_     							=       [np.random.randint(255 ,  size=(3, 30, 400) ,  dtype=np.uinta			)]

											snake_case_     							=       [Image.fromarray(np.moveaxis(snake_case ,  0 ,  -1			)			) for x in image_inputs]

											return image_inputs




				def        a				(   self			):
											snake_case_     							=       BlipaProcessor(tokenizer=self.get_tokenizer() ,  image_processor=self.get_image_processor()			)
											processor.save_pretrained(self.tmpdirname			)

											snake_case_     							=       self.get_tokenizer(bos_token='(BOS)' ,  eos_token='(EOS)'			)
											snake_case_     							=       self.get_image_processor(do_normalize=snake_case ,  padding_value=1.0			)

											snake_case_     							=       BlipaProcessor.from_pretrained(
											    self.tmpdirname ,  bos_token='(BOS)' ,  eos_token='(EOS)' ,  do_normalize=snake_case ,  padding_value=1.0			)

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

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




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       image_processor(snake_case ,  return_tensors='np'			)
											snake_case_     							=       processor(images=snake_case ,  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        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'

											snake_case_     							=       processor(text=snake_case			)

											snake_case_     							=       tokenizer(snake_case ,  return_token_type_ids=snake_case			)

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




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'
											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       processor(text=snake_case ,  images=snake_case			)

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

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




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											snake_case_     							=       processor.batch_decode(snake_case			)
											snake_case_     							=       tokenizer.batch_decode(snake_case			)

											self.assertListEqual(snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'
											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       processor(text=snake_case ,  images=snake_case			)

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




