


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 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Any		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  tempfile.mkdtemp()

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

              UpperCamelCase_          =  BlipaProcessor(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              processor.save_pretrained(self.tmpdirname		)

       def lowercase (		self: int					,   **_SCREAMING_SNAKE_CASE: List[Any]		)       -> int:




              """simple docstring"""


              return AutoProcessor.from_pretrained(self.tmpdirname					,   **_SCREAMING_SNAKE_CASE		).tokenizer

       def lowercase (		self: Tuple					,   **_SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> int:




              """simple docstring"""


              return AutoProcessor.from_pretrained(self.tmpdirname					,   **_SCREAMING_SNAKE_CASE		).image_processor

       def lowercase (		self: str		)       -> Any:




              """simple docstring"""


              shutil.rmtree(self.tmpdirname		)

       def lowercase (		self: List[str]		)       -> Any:




              """simple docstring"""


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

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

              return image_inputs

       def lowercase (		self: List[Any]		)       -> Dict:




              """simple docstring"""


              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=_SCREAMING_SNAKE_CASE					,   padding_value=1.0		)

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

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

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

       def lowercase (		self: Any		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  self.get_image_processor()
              UpperCamelCase_          =  self.get_tokenizer()

              UpperCamelCase_          =  BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE					,   image_processor=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.prepare_image_inputs()

              UpperCamelCase_          =  image_processor(_SCREAMING_SNAKE_CASE					,   return_tensors="np"		)
              UpperCamelCase_          =  processor(images=_SCREAMING_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 lowercase (		self: Tuple		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  self.get_image_processor()
              UpperCamelCase_          =  self.get_tokenizer()

              UpperCamelCase_          =  BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE					,   image_processor=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  "lower newer"

              UpperCamelCase_          =  processor(text=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokenizer(_SCREAMING_SNAKE_CASE					,   return_token_type_ids=_SCREAMING_SNAKE_CASE		)

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

       def lowercase (		self: Tuple		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  self.get_image_processor()
              UpperCamelCase_          =  self.get_tokenizer()

              UpperCamelCase_          =  BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE					,   image_processor=_SCREAMING_SNAKE_CASE		)

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

              UpperCamelCase_          =  processor(text=_SCREAMING_SNAKE_CASE					,   images=_SCREAMING_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(_SCREAMING_SNAKE_CASE		):
                     processor()

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




              """simple docstring"""


              UpperCamelCase_          =  self.get_image_processor()
              UpperCamelCase_          =  self.get_tokenizer()

              UpperCamelCase_          =  BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE					,   image_processor=_SCREAMING_SNAKE_CASE		)

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

              UpperCamelCase_          =  processor.batch_decode(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE		)

              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)



       def lowercase (		self: Optional[Any]		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.get_image_processor()
              UpperCamelCase_          =  self.get_tokenizer()

              UpperCamelCase_          =  BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE					,   image_processor=_SCREAMING_SNAKE_CASE		)

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

              UpperCamelCase_          =  processor(text=_SCREAMING_SNAKE_CASE					,   images=_SCREAMING_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"]		)








import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				'T5Config'




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		jnp.ndarray:
       UpperCamelCase_          =  jnp.zeros_like(UpperCamelCase_  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 0].set(UpperCamelCase_  )

       UpperCamelCase_          =  jnp.where(shifted_input_ids == -100					,		UpperCamelCase_					,		UpperCamelCase_  )
       return shifted_input_ids



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Union[str, Any]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Optional[int]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Union[str, Any]         =   '''mt5'''
       _UpperCamelCase				:					List[Any]         =   MTaConfig

