'''simple docstring'''





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 	__A				(			unittest.TestCase     ):



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       tempfile.mkdtemp()

         UpperCAmelCase_	   =       BlipImageProcessor()
         UpperCAmelCase_	   =       GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"			)

         UpperCAmelCase_	   =       BlipaProcessor(__a       ,      __a			)

         processor.save_pretrained(self.tmpdirname			)



    def  _lowercase						(self   :   Tuple       ,      **__a   :   List[str]			):
         return AutoProcessor.from_pretrained(self.tmpdirname       ,      **__a			).tokenizer



    def  _lowercase						(self   :   Dict       ,      **__a   :   List[str]			):
         return AutoProcessor.from_pretrained(self.tmpdirname       ,      **__a			).image_processor



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



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

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

         return image_inputs



    def  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       BlipaProcessor(tokenizer=self.get_tokenizer()       ,      image_processor=self.get_image_processor()			)
         processor.save_pretrained(self.tmpdirname			)

         UpperCAmelCase_	   =       self.get_tokenizer(bos_token="(BOS)"       ,      eos_token="(EOS)"			)
         UpperCAmelCase_	   =       self.get_image_processor(do_normalize=__a       ,      padding_value=1.0			)

         UpperCAmelCase_	   =       BlipaProcessor.from_pretrained(
             self.tmpdirname       ,      bos_token="(BOS)"       ,      eos_token="(EOS)"       ,      do_normalize=__a       ,      padding_value=1.0			)

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

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



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       self.get_image_processor()
         UpperCAmelCase_	   =       self.get_tokenizer()

         UpperCAmelCase_	   =       BlipaProcessor(tokenizer=__a       ,      image_processor=__a			)

         UpperCAmelCase_	   =       self.prepare_image_inputs()

         UpperCAmelCase_	   =       image_processor(__a       ,      return_tensors="np"			)
         UpperCAmelCase_	   =       processor(images=__a       ,      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   :   Any			):
         UpperCAmelCase_	   =       self.get_image_processor()
         UpperCAmelCase_	   =       self.get_tokenizer()

         UpperCAmelCase_	   =       BlipaProcessor(tokenizer=__a       ,      image_processor=__a			)

         UpperCAmelCase_	   =       "lower newer"

         UpperCAmelCase_	   =       processor(text=__a			)

         UpperCAmelCase_	   =       tokenizer(__a       ,      return_token_type_ids=__a			)

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



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       self.get_image_processor()
         UpperCAmelCase_	   =       self.get_tokenizer()

         UpperCAmelCase_	   =       BlipaProcessor(tokenizer=__a       ,      image_processor=__a			)

         UpperCAmelCase_	   =       "lower newer"
         UpperCAmelCase_	   =       self.prepare_image_inputs()

         UpperCAmelCase_	   =       processor(text=__a       ,      images=__a			)

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

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



    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       self.get_image_processor()
         UpperCAmelCase_	   =       self.get_tokenizer()

         UpperCAmelCase_	   =       BlipaProcessor(tokenizer=__a       ,      image_processor=__a			)

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

         UpperCAmelCase_	   =       processor.batch_decode(__a			)
         UpperCAmelCase_	   =       tokenizer.batch_decode(__a			)

         self.assertListEqual(__a       ,      __a			)






    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.get_image_processor()
         UpperCAmelCase_	   =       self.get_tokenizer()

         UpperCAmelCase_	   =       BlipaProcessor(tokenizer=__a       ,      image_processor=__a			)

         UpperCAmelCase_	   =       "lower newer"
         UpperCAmelCase_	   =       self.prepare_image_inputs()

         UpperCAmelCase_	   =       processor(text=__a       ,      images=__a			)

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





'''simple docstring'''





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

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
    import jax
    import jaxlib

SCREAMING_SNAKE_CASE_:					List[str]  =get_logger()

SCREAMING_SNAKE_CASE_:					Optional[dict]  =None




class 	__A				(			TensorFormatter[Mapping, """jax.Array""", Mapping]     ):



    def __init__(self   :   List[Any]       ,      __a   :   Optional[int]=None       ,      __a   :   Any=None       ,      **__a   :   Dict			):
         super().__init__(features=__a			)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(__a       ,      __a			):
              raise ValueError(
                  f"""Expected {device} to be a `str` not {type(__a			)}, as `jaxlib.xla_extension.Device` """
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."			)
         UpperCAmelCase_	   =       device if isinstance(__a       ,      __a			) else str(jax.devices()[0]			)
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()			):
              logger.warning(
                  f"""Device with string identifier {self.device} not listed among the available """
                  f"""devices: {list(DEVICE_MAPPING.keys()			)}, so falling back to the default """
                  f"""device: {str(jax.devices()[0]			)}."""			)
              UpperCAmelCase_	   =       str(jax.devices()[0]			)
         UpperCAmelCase_	   =       jnp_array_kwargs



    @staticmethod
    def  _lowercase						():
         import jax

         return {str(__a			): device for device in jax.devices()}



    def  _lowercase						(self   :   str       ,      __a   :   Tuple			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      __a			) and column:
              if all(
                  isinstance(__a       ,      jax.Array			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
                   return jnp.stack(__a       ,      axis=0			)
         return column



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[int]			):
         import jax
         import jax.numpy as jnp

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

         UpperCAmelCase_	   =       {}

         if isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.integer			):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
              else:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
         elif isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.floating			):
              UpperCAmelCase_	   =       {"dtype": jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__a       ,      PIL.Image.Image			):
                   UpperCAmelCase_	   =       np.asarray(__a			)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]			):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__a       ,      **{**default_dtype, **self.jnp_array_kwargs}			)



    def  _lowercase						(self   :   int       ,      __a   :   Any			):
         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__a       ,      torch.Tensor			):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
         if hasattr(__a       ,      "__array__"			) and not isinstance(__a       ,      jax.Array			):
              UpperCAmelCase_	   =       data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(__a       ,      np.ndarray			):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         elif isinstance(__a       ,      (list, tuple)			):
              return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         return self._tensorize(__a			)



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   dict			):
         return map_nested(self._recursive_tensorize       ,      __a       ,      map_list=__a			)



    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_row(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_row(__a			)
         return self.recursive_tensorize(__a			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_column(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_column(__a       ,      pa_table.column_names[0]			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         UpperCAmelCase_	   =       self._consolidate(__a			)
         return column






    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_batch(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_batch(__a			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         for column_name in batch:
              UpperCAmelCase_	   =       self._consolidate(batch[column_name]			)
         return batch




