





'''simple docstring'''





import inspect
import unittest

import numpy as np

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

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


if is_flax_available():
    import jax

    from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
    from PIL import Image

    from transformers import BeitImageProcessor




class 	__A				(			unittest.TestCase     ):



    def __init__(self   :   Tuple       ,      __a   :   Optional[int]       ,      __a   :   Any=100       ,      __a   :   Optional[Any]=13       ,      __a   :   Optional[int]=30       ,      __a   :   Dict=2       ,      __a   :   Tuple=3       ,      __a   :   Any=True       ,      __a   :   Any=True       ,      __a   :   Any=32       ,      __a   :   str=5       ,      __a   :   Optional[int]=4       ,      __a   :   Dict=37       ,      __a   :   Union[str, Any]="gelu"       ,      __a   :   str=0.1       ,      __a   :   Dict=0.1       ,      __a   :   Any=10       ,      __a   :   Tuple=0.02       ,      __a   :   Optional[int]=3       ,      ):
         UpperCAmelCase_	   =       parent
         UpperCAmelCase_	   =       vocab_size
         UpperCAmelCase_	   =       batch_size
         UpperCAmelCase_	   =       image_size
         UpperCAmelCase_	   =       patch_size
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       is_training
         UpperCAmelCase_	   =       use_labels
         UpperCAmelCase_	   =       hidden_size
         UpperCAmelCase_	   =       num_hidden_layers
         UpperCAmelCase_	   =       num_attention_heads
         UpperCAmelCase_	   =       intermediate_size
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       hidden_dropout_prob
         UpperCAmelCase_	   =       attention_probs_dropout_prob
         UpperCAmelCase_	   =       type_sequence_label_size
         UpperCAmelCase_	   =       initializer_range

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_	   =       (image_size // patch_size) ** 2
         UpperCAmelCase_	   =       num_patches + 1



    def  _lowercase						(self   :   int			):
         UpperCAmelCase_	   =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

         UpperCAmelCase_	   =       None
         if self.use_labels:
              UpperCAmelCase_	   =       ids_tensor([self.batch_size]       ,      self.type_sequence_label_size			)

         UpperCAmelCase_	   =       BeitConfig(
             vocab_size=self.vocab_size       ,      image_size=self.image_size       ,      patch_size=self.patch_size       ,      num_channels=self.num_channels       ,      hidden_size=self.hidden_size       ,      num_hidden_layers=self.num_hidden_layers       ,      num_attention_heads=self.num_attention_heads       ,      intermediate_size=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      hidden_dropout_prob=self.hidden_dropout_prob       ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,      is_decoder=__a       ,      initializer_range=self.initializer_range       ,      )

         return config, pixel_values, labels



    def  _lowercase						(self   :   Tuple       ,      __a   :   List[Any]       ,      __a   :   List[Any]       ,      __a   :   int			):
         UpperCAmelCase_	   =       FlaxBeitModel(config=__a			)
         UpperCAmelCase_	   =       model(__a			)
         self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)			)



    def  _lowercase						(self   :   Optional[int]       ,      __a   :   List[Any]       ,      __a   :   List[str]       ,      __a   :   Tuple			):
         UpperCAmelCase_	   =       FlaxBeitForMaskedImageModeling(config=__a			)
         UpperCAmelCase_	   =       model(__a			)
         self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length - 1, self.vocab_size)			)



    def  _lowercase						(self   :   int       ,      __a   :   Any       ,      __a   :   Tuple       ,      __a   :   int			):
         UpperCAmelCase_	   =       self.type_sequence_label_size
         UpperCAmelCase_	   =       FlaxBeitForImageClassification(config=__a			)
         UpperCAmelCase_	   =       model(__a			)
         self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.type_sequence_label_size)			)

         # test greyscale images
         UpperCAmelCase_	   =       1
         UpperCAmelCase_	   =       FlaxBeitForImageClassification(__a			)

         UpperCAmelCase_	   =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
         UpperCAmelCase_	   =       model(__a			)






    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_			
         )   ,			(
             UpperCAmelCase_			
         )   ,			(
             UpperCAmelCase_			
         )   ,						
         )	   =       config_and_inputs
         UpperCAmelCase_	   =       {"pixel_values": pixel_values}
         return config, inputs_dict




@require_flax
class 	__A				(			UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						Dict	       =  (
        (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
    )



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       FlaxBeitModelTester(self			)
         UpperCAmelCase_	   =       ConfigTester(self       ,      config_class=__a       ,      has_text_modality=__a       ,      hidden_size=37			)



    def  _lowercase						(self   :   List[Any]			):
         self.config_tester.run_common_tests()



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase_	   =       ["pixel_values"]
              self.assertListEqual(arg_names[:1]       ,      __a			)



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__			):
                   UpperCAmelCase_	   =       self._prepare_for_class(__a       ,      __a			)
                   UpperCAmelCase_	   =       model_class(__a			)

                   @jax.jit
                   def model_jitted(__a   :   Union[str, Any]       ,      **__a   :   Tuple			):
                        return model(pixel_values=__a       ,      **__a			)

                   with self.subTest("JIT Enabled"			):
                        UpperCAmelCase_	   =       model_jitted(**__a			).to_tuple()

                   with self.subTest("JIT Disabled"			):
                        with jax.disable_jit():
                             UpperCAmelCase_	   =       model_jitted(**__a			).to_tuple()

                   self.assertEqual(len(__a			)       ,      len(__a			)			)
                   for jitted_output, output in zip(__a       ,      __a			):
                        self.assertEqual(jitted_output.shape       ,      output.shape			)



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a			)



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*__a			)



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__a			)






    @slow
    def  _lowercase						(self   :   Dict			):
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_	   =       model_class_name.from_pretrained("microsoft/beit-base-patch16-224"			)
              UpperCAmelCase_	   =       model(np.ones((1, 3, 224, 224)			)			)
              self.assertIsNotNone(__a			)





def      lowerCAmelCase_    ( )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
     return image






@require_vision
@require_flax
class 	__A				(			unittest.TestCase     ):



    @cached_property
    def  _lowercase						(self   :   List[Any]			):
         return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"			) if is_vision_available() else None



    @slow
    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"			)

         UpperCAmelCase_	   =       self.default_image_processor
         UpperCAmelCase_	   =       prepare_img()
         UpperCAmelCase_	   =       image_processor(images=__a       ,      return_tensors="np"			).pixel_values

         # prepare bool_masked_pos
         UpperCAmelCase_	   =       np.ones((1, 196)       ,      dtype=__a			)

         # forward pass
         UpperCAmelCase_	   =       model(pixel_values=__a       ,      bool_masked_pos=__a			)
         UpperCAmelCase_	   =       outputs.logits

         # verify the logits
         UpperCAmelCase_	   =       (1, 196, 8192)
         self.assertEqual(logits.shape       ,      __a			)

         UpperCAmelCase_	   =       np.array(
             [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]			)

         self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]       ,      __a       ,      atol=1E-2			)			)



    @slow
    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"			)

         UpperCAmelCase_	   =       self.default_image_processor
         UpperCAmelCase_	   =       prepare_img()
         UpperCAmelCase_	   =       image_processor(images=__a       ,      return_tensors="np"			)

         # forward pass
         UpperCAmelCase_	   =       model(**__a			)
         UpperCAmelCase_	   =       outputs.logits

         # verify the logits
         UpperCAmelCase_	   =       (1, 1000)
         self.assertEqual(logits.shape       ,      __a			)

         UpperCAmelCase_	   =       np.array([-1.23_85, -1.09_87, -1.01_08]			)

         self.assertTrue(np.allclose(logits[0, :3]       ,      __a       ,      atol=1E-4			)			)

         UpperCAmelCase_	   =       281
         self.assertEqual(logits.argmax(-1			).item()       ,      __a			)






    @slow
    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"			)

         UpperCAmelCase_	   =       self.default_image_processor
         UpperCAmelCase_	   =       prepare_img()
         UpperCAmelCase_	   =       image_processor(images=__a       ,      return_tensors="np"			)

         # forward pass
         UpperCAmelCase_	   =       model(**__a			)
         UpperCAmelCase_	   =       outputs.logits

         # verify the logits
         UpperCAmelCase_	   =       (1, 21841)
         self.assertEqual(logits.shape       ,      __a			)

         UpperCAmelCase_	   =       np.array([1.68_81, -0.27_87, 0.59_01]			)

         self.assertTrue(np.allclose(logits[0, :3]       ,      __a       ,      atol=1E-4			)			)

         UpperCAmelCase_	   =       2396
         self.assertEqual(logits.argmax(-1			).item()       ,      __a			)





'''simple docstring'''





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[Any]  ={
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}




class 	__A				(			UpperCamelCase__     ):
    a__	:						Dict	       =  """t5"""
    a__	:						List[str]	       =  ["""past_key_values"""]
    a__	:						Union[str, Any]	       =  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



    def __init__(self   :   List[str]       ,      __a   :   Any=32128       ,      __a   :   Optional[int]=512       ,      __a   :   Union[str, Any]=64       ,      __a   :   List[str]=2048       ,      __a   :   Optional[int]=6       ,      __a   :   Union[str, Any]=None       ,      __a   :   Union[str, Any]=8       ,      __a   :   Dict=32       ,      __a   :   List[Any]=128       ,      __a   :   Optional[Any]=0.1       ,      __a   :   List[Any]=1E-6       ,      __a   :   str=1.0       ,      __a   :   Dict="relu"       ,      __a   :   int=True       ,      __a   :   int=True       ,      __a   :   Optional[Any]=0       ,      __a   :   Dict=1       ,      **__a   :   List[Any]       ,      ):
         UpperCAmelCase_	   =       vocab_size
         UpperCAmelCase_	   =       d_model
         UpperCAmelCase_	   =       d_kv
         UpperCAmelCase_	   =       d_ff
         UpperCAmelCase_	   =       num_layers
         UpperCAmelCase_	   =       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         UpperCAmelCase_	   =       num_heads
         UpperCAmelCase_	   =       relative_attention_num_buckets
         UpperCAmelCase_	   =       relative_attention_max_distance
         UpperCAmelCase_	   =       dropout_rate
         UpperCAmelCase_	   =       layer_norm_epsilon
         UpperCAmelCase_	   =       initializer_factor
         UpperCAmelCase_	   =       feed_forward_proj
         UpperCAmelCase_	   =       use_cache

         UpperCAmelCase_	   =       self.feed_forward_proj.split("-"			)
         UpperCAmelCase_	   =       act_info[-1]
         UpperCAmelCase_	   =       act_info[0] == "gated"

         if len(__a			) > 1 and act_info[0] != "gated" or len(__a			) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                  "'gated-gelu' or 'relu'"			)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              UpperCAmelCase_	   =       "gelu_new"

         super().__init__(
             pad_token_id=__a       ,      eos_token_id=__a       ,      is_encoder_decoder=__a       ,      **__a       ,      )




class 	__A				(			UpperCamelCase__     ):



    @property
    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       {
             "input_ids": {0: "batch", 1: "encoder_sequence"},
             "attention_mask": {0: "batch", 1: "encoder_sequence"},
         }
         if self.use_past:
              UpperCAmelCase_	   =       "past_encoder_sequence + sequence"
              UpperCAmelCase_	   =       {0: "batch"}
              UpperCAmelCase_	   =       {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
              UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}
              UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}

         if self.use_past:
              self.fill_with_past_key_values_(__a       ,      direction="inputs"			)

         return common_inputs






    @property
    def  _lowercase						(self   :   Optional[int]			):
         return 13




