






import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor






class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def __init__( self :		Tuple					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		List[str]=3					,   __UpperCAmelCase :		Tuple=32					,   __UpperCAmelCase :		List[Any]=3					,   __UpperCAmelCase :		Any=10					,   __UpperCAmelCase :		Union[str, Any]=[10, 20, 30, 40]					,   __UpperCAmelCase :		Optional[int]=[1, 1, 2, 1]					,   __UpperCAmelCase :		str=True					,   __UpperCAmelCase :		List[str]=True					,   __UpperCAmelCase :		Any="relu"					,   __UpperCAmelCase :		Union[str, Any]=3					,   __UpperCAmelCase :		str=None					,   )       ->Tuple:




       """simple docstring"""


       a						    = parent
       a						    = batch_size
       a						    = image_size
       a						    = num_channels
       a						    = embeddings_size
       a						    = hidden_sizes
       a						    = depths
       a						    = is_training
       a						    = use_labels
       a						    = hidden_act
       a						    = num_labels
       a						    = scope
       a						    = len(__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Dict:




       """simple docstring"""


       a						    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

       a						    = self.get_config()

       return config, pixel_values







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->List[Any]:




       """simple docstring"""


       return RegNetConfig(
           num_channels=self.num_channels					,   embeddings_size=self.embeddings_size					,   hidden_sizes=self.hidden_sizes					,   depths=self.depths					,   hidden_act=self.hidden_act					,   num_labels=self.num_labels					,   image_size=self.image_size					,   )







      def  __lowerCAmelCase   ( self :		Optional[int]					,   __UpperCAmelCase :		List[Any]					,   __UpperCAmelCase :		Any    )       ->Optional[int]:




       """simple docstring"""


       a						    = FlaxRegNetModel(config=__UpperCAmelCase    )
       a						    = model(__UpperCAmelCase    )

       # Output shape (b, c, h, w)
       self.parent.assertEqual(
           result.last_hidden_state.shape					,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,   )







      def  __lowerCAmelCase   ( self :		List[str]					,   __UpperCAmelCase :		Union[str, Any]					,   __UpperCAmelCase :		Optional[Any]    )       ->Optional[Any]:




       """simple docstring"""


       a						    = self.num_labels
       a						    = FlaxRegNetForImageClassification(config=__UpperCAmelCase    )
       a						    = model(__UpperCAmelCase    )
       self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_labels)    )







      def  __lowerCAmelCase   ( self :		str    )       ->int:




       """simple docstring"""


       a						    = self.prepare_config_and_inputs()
       a    ,	a						    = config_and_inputs
       a						    = {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_flax
class 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

      __snake_case	    =    False
      __snake_case	    =    False
      __snake_case	    =    False







      def  __lowerCAmelCase   ( self :		List[Any]    )       ->None:




       """simple docstring"""


       a						    = FlaxRegNetModelTester(self    )
       a						    = ConfigTester(self					,   config_class=__UpperCAmelCase					,   has_text_modality=__UpperCAmelCase    )







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




       """simple docstring"""


       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()







      def  __lowerCAmelCase   ( self :		str    )       ->Tuple:




       """simple docstring"""


       return







      def  __lowerCAmelCase   ( self :		Tuple    )       ->str:




       """simple docstring"""


       a						    = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Optional[int]:




       """simple docstring"""


       a						    = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase    )







      @unittest.skip(reason='''RegNet does not use inputs_embeds'''    )
      def  __lowerCAmelCase   ( self :		str    )       ->str:




       """simple docstring"""


       pass







      @unittest.skip(reason='''RegNet does not support input and output embeddings'''    )
      def  __lowerCAmelCase   ( self :		str    )       ->Dict:




       """simple docstring"""


       pass







      def  __lowerCAmelCase   ( self :		int    )       ->List[str]:




       """simple docstring"""


       a    ,	a						    = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        a						    = model_class(__UpperCAmelCase    )
        a						    = inspect.signature(model.__call__    )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a						    = [*signature.parameters.keys()]

        a						    = ['''pixel_values''']
        self.assertListEqual(arg_names[:1]					,   __UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Any    )       ->int:




       """simple docstring"""


       def check_hidden_states_output(__UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		Union[str, Any]					,   __UpperCAmelCase :		str    ):
        a						    = model_class(__UpperCAmelCase    )

        a						    = model(**self._prepare_for_class(__UpperCAmelCase					,   __UpperCAmelCase    )    )

        a						    = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        a						    = self.model_tester.num_stages
        self.assertEqual(len(__UpperCAmelCase    )					,   expected_num_stages + 1    )

       a    ,	a						    = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        a						    = True
        check_hidden_states_output(__UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase    )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a						    = True

        check_hidden_states_output(__UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		str    )       ->Tuple:




       """simple docstring"""


       a    ,	a						    = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__    ):
         a						    = self._prepare_for_class(__UpperCAmelCase					,   __UpperCAmelCase    )
         a						    = model_class(__UpperCAmelCase    )

         @jax.jit
         def model_jitted(__UpperCAmelCase :		Dict					,   **__UpperCAmelCase :		str    ):
          return model(pixel_values=__UpperCAmelCase					,   **__UpperCAmelCase    )

         with self.subTest('''JIT Enabled'''    ):
          a						    = model_jitted(**__UpperCAmelCase    ).to_tuple()

         with self.subTest('''JIT Disabled'''    ):
          with jax.disable_jit():
           a						    = model_jitted(**__UpperCAmelCase    ).to_tuple()

         self.assertEqual(len(__UpperCAmelCase    )					,   len(__UpperCAmelCase    )    )
         for jitted_output, output in zip(__UpperCAmelCase					,   __UpperCAmelCase    ):
          self.assertEqual(jitted_output.shape					,   output.shape    )
def 		_a       ( )				->     Dict:
 a						    = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
 return image






@require_flax
class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      @cached_property
      def  __lowerCAmelCase   ( self :		Any    )       ->Dict:




       """simple docstring"""


       return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''    ) if is_vision_available() else None







      @slow
      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->List[Any]:




       """simple docstring"""


       a						    = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''    )

       a						    = self.default_image_processor
       a						    = prepare_img()
       a						    = image_processor(images=__UpperCAmelCase					,   return_tensors='''np'''    )

       a						    = model(**__UpperCAmelCase    )

       # verify the logits
       a						    = (1, 1_000)
       self.assertEqual(outputs.logits.shape					,   __UpperCAmelCase    )

       a						    = jnp.array([-0.4180, -1.5051, -3.4836]    )

       self.assertTrue(jnp.allclose(outputs.logits[0, :3]					,   __UpperCAmelCase					,   atol=1e-4    )    )










import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     UpperCAmelCase__							  =						None


UpperCAmelCase__							  =						logging.get_logger(__name__)


UpperCAmelCase__							  =						"▁"

UpperCAmelCase__							  =						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__							  =						{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase__							  =						{
    "google/pegasus-xsum": 512,
}






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    VOCAB_FILES_NAMES
      __snake_case	    =    PRETRAINED_VOCAB_FILES_MAP
      __snake_case	    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case	    =    PegasusTokenizer
      __snake_case	    =    ['''input_ids''', '''attention_mask''']







      def __init__( self :		List[Any]					,   __UpperCAmelCase :		str=None					,   __UpperCAmelCase :		List[Any]=None					,   __UpperCAmelCase :		Any="<pad>"					,   __UpperCAmelCase :		str="</s>"					,   __UpperCAmelCase :		List[str]="<unk>"					,   __UpperCAmelCase :		List[str]="<mask_2>"					,   __UpperCAmelCase :		Tuple="<mask_1>"					,   __UpperCAmelCase :		int=None					,   __UpperCAmelCase :		str=103					,   **__UpperCAmelCase :		Optional[int]					,   )       ->Dict:




       """simple docstring"""


       a						    = offset

       if additional_special_tokens is not None:
        if not isinstance(__UpperCAmelCase					,   __UpperCAmelCase    ):
         raise TypeError(
             F"""additional_special_tokens should be of type {type(__UpperCAmelCase    )}, but is"""
             F""" {type(__UpperCAmelCase    )}"""    )

        a						    = (
            ([mask_token_sent] + additional_special_tokens)
            if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
            else additional_special_tokens
        )
        # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
        additional_special_tokens_extended += [
            F"""<unk_{i}>""" for i in range(len(__UpperCAmelCase    )					,   self.offset - 1    )
        ]

        if len(set(__UpperCAmelCase    )    ) != len(__UpperCAmelCase    ):
         raise ValueError(
             '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
             F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""    )
        a						    = additional_special_tokens_extended
       else:
        a						    = [mask_token_sent] if mask_token_sent is not None else []
        additional_special_tokens += [F"""<unk_{i}>""" for i in range(2					,   self.offset    )]

       super().__init__(
           __UpperCAmelCase					,   tokenizer_file=__UpperCAmelCase					,   pad_token=__UpperCAmelCase					,   eos_token=__UpperCAmelCase					,   unk_token=__UpperCAmelCase					,   mask_token=__UpperCAmelCase					,   mask_token_sent=__UpperCAmelCase					,   offset=__UpperCAmelCase					,   additional_special_tokens=__UpperCAmelCase					,   **__UpperCAmelCase					,   )
       a						    = vocab_file
       a						    = False if not self.vocab_file else True







      def  __lowerCAmelCase   ( self :		Optional[Any]					,   __UpperCAmelCase :		int    )       ->int:




       """simple docstring"""


       a						    = set(self.all_special_ids    )  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

       if all_special_ids != set(range(len(self.additional_special_tokens    ) + 3    )    ):
        raise ValueError(
            '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
            F""" {len(self.additional_special_tokens    )} additional_special_tokens, but got {all_special_ids}"""    )

       return [1 if x in all_special_ids else 0 for x in seq]







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		List					,   __UpperCAmelCase :		Optional[List] = None					,   __UpperCAmelCase :		bool = False    )       ->List[int]:




       """simple docstring"""


       if already_has_special_tokens:
        return self._special_token_mask(__UpperCAmelCase    )
       elif token_ids_a is None:
        return self._special_token_mask(__UpperCAmelCase    ) + [1]
       else:
        return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		Optional[int]=None    )       ->List[int]:




       """simple docstring"""


       if token_ids_a is None:
        return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[str] = None    )       ->Tuple[str]:




       """simple docstring"""


       if not self.can_save_slow_tokenizer:
        raise ValueError(
            '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
            '''tokenizer.'''    )

       if not os.path.isdir(__UpperCAmelCase    ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
        return
       a						    = os.path.join(
           __UpperCAmelCase					,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

       if os.path.abspath(self.vocab_file    ) != os.path.abspath(__UpperCAmelCase    ):
        copyfile(self.vocab_file					,   __UpperCAmelCase    )

       return (out_vocab_file,)


