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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				str      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     tempfile.mkdtemp()

        SCREAMING_SNAKE_CASE_      =     BlipImageProcessor()
        SCREAMING_SNAKE_CASE_      =     BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"      )

        SCREAMING_SNAKE_CASE_      =     BlipProcessor(__lowerCamelCase     ,   __lowerCamelCase      )

        processor.save_pretrained(self.tmpdirname      )







  def       __A   (							self       :				int     ,   **__magic_name__       :				List[Any]      )     ->		Any:
        return AutoProcessor.from_pretrained(self.tmpdirname     ,   **__lowerCamelCase      ).tokenizer







  def       __A   (							self       :				List[Any]     ,   **__magic_name__       :				List[str]      )     ->		Any:
        return AutoProcessor.from_pretrained(self.tmpdirname     ,   **__lowerCamelCase      ).image_processor







  def       __A   (							self       :				Dict      )     ->		str:
        shutil.rmtree(self.tmpdirname      )







  def       __A   (							self       :				int      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     [np.random.randint(255     ,   size=(3, 30, 400)     ,   dtype=np.uinta      )]

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

        return image_inputs







  def       __A   (							self       :				int      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     BlipProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()      )
        processor.save_pretrained(self.tmpdirname      )

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

        SCREAMING_SNAKE_CASE_      =     BlipProcessor.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       __A   (							self       :				int      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     self.get_image_processor()
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()

        SCREAMING_SNAKE_CASE_      =     BlipProcessor(tokenizer=__lowerCamelCase     ,   image_processor=__lowerCamelCase      )

        SCREAMING_SNAKE_CASE_      =     self.prepare_image_inputs()

        SCREAMING_SNAKE_CASE_      =     image_processor(__lowerCamelCase     ,   return_tensors="np"      )
        SCREAMING_SNAKE_CASE_      =     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       __A   (							self       :				Optional[int]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     self.get_image_processor()
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()

        SCREAMING_SNAKE_CASE_      =     BlipProcessor(tokenizer=__lowerCamelCase     ,   image_processor=__lowerCamelCase      )

        SCREAMING_SNAKE_CASE_      =     "lower newer"

        SCREAMING_SNAKE_CASE_      =     processor(text=__lowerCamelCase      )

        SCREAMING_SNAKE_CASE_      =     tokenizer(__lowerCamelCase     ,   return_token_type_ids=__lowerCamelCase      )

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







  def       __A   (							self       :				Any      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.get_image_processor()
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()

        SCREAMING_SNAKE_CASE_      =     BlipProcessor(tokenizer=__lowerCamelCase     ,   image_processor=__lowerCamelCase      )

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

        SCREAMING_SNAKE_CASE_      =     processor(text=__lowerCamelCase     ,   images=__lowerCamelCase      )

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

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







  def       __A   (							self       :				Union[str, Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.get_image_processor()
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()

        SCREAMING_SNAKE_CASE_      =     BlipProcessor(tokenizer=__lowerCamelCase     ,   image_processor=__lowerCamelCase      )

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

        SCREAMING_SNAKE_CASE_      =     processor.batch_decode(__lowerCamelCase      )
        SCREAMING_SNAKE_CASE_      =     tokenizer.batch_decode(__lowerCamelCase      )

        self.assertListEqual(__lowerCamelCase     ,   __lowerCamelCase      )







  def       __A   (							self       :				Tuple      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     self.get_image_processor()
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()

        SCREAMING_SNAKE_CASE_      =     BlipProcessor(tokenizer=__lowerCamelCase     ,   image_processor=__lowerCamelCase      )

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

        SCREAMING_SNAKE_CASE_      =     processor(text=__lowerCamelCase     ,   images=__lowerCamelCase      )

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









import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
       import jax

       from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
       from transformers.models.bert.modeling_flax_bert import FlaxBertModel
       from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel




@require_flax
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["bert-base-cased", "bert-large-uncased"]:
               with self.subTest(__lowerCamelCase ):
                      A__											=						AutoConfig.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

                      A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["roberta-base", "roberta-large"]:
               with self.subTest(__lowerCamelCase ):
                      A__											=						AutoConfig.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

                      A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["bert-base-cased", "bert-large-uncased"]:
               A__											=						AutoTokenizer.from_pretrained(__lowerCamelCase )
               A__											=						FlaxBertModel.from_pretrained(__lowerCamelCase )
               A__											=						tokenizer('''Do you support jax jitted function?''',return_tensors=TensorType.JAX )

               @jax.jit
               def eval(**__lowerCamelCase ):
                      return model(**__lowerCamelCase )

               eval(**__lowerCamelCase ).block_until_ready()



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["roberta-base", "roberta-large"]:
               A__											=						AutoTokenizer.from_pretrained(__lowerCamelCase )
               A__											=						FlaxRobertaModel.from_pretrained(__lowerCamelCase )
               A__											=						tokenizer('''Do you support jax jitted function?''',return_tensors=TensorType.JAX )

               @jax.jit
               def eval(**__lowerCamelCase ):
                      return model(**__lowerCamelCase )

               eval(**__lowerCamelCase ).block_until_ready()



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,'''bert-base is not a local folder and is not a valid model identifier''' ):
               A__											=						FlaxAutoModel.from_pretrained('''bert-base''' )



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
               A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase,revision='''aaaaaa''' )



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack''',):
               A__											=						FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )




 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(__lowerCamelCase,'''Use `from_pt=True` to load this model''' ):
               A__											=						FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )





