'''simple docstring'''





import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
    from transformers.models.mobilevitva.modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        make_divisible,
    )


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor




class 	__A				(			UpperCamelCase__     ):



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       self.config_class(**self.inputs_dict			)
         self.parent.assertTrue(hasattr(__a       ,      "width_multiplier"			)			)




class 	__A				:



    def __init__(self   :   str       ,      __a   :   int       ,      __a   :   str=13       ,      __a   :   Optional[int]=64       ,      __a   :   Optional[int]=2       ,      __a   :   Any=3       ,      __a   :   int="swish"       ,      __a   :   Union[str, Any]=3       ,      __a   :   List[str]=32       ,      __a   :   Tuple=0.1       ,      __a   :   str=0.02       ,      __a   :   Optional[int]=True       ,      __a   :   List[Any]=True       ,      __a   :   Union[str, Any]=10       ,      __a   :   int=None       ,      __a   :   List[Any]=0.25       ,      __a   :   Optional[Any]=0.0       ,      __a   :   Union[str, Any]=0.0       ,      ):
         UpperCAmelCase_	   =       parent
         UpperCAmelCase_	   =       batch_size
         UpperCAmelCase_	   =       image_size
         UpperCAmelCase_	   =       patch_size
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       make_divisible(512 * width_multiplier       ,      divisor=8			)
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       conv_kernel_size
         UpperCAmelCase_	   =       output_stride
         UpperCAmelCase_	   =       classifier_dropout_prob
         UpperCAmelCase_	   =       use_labels
         UpperCAmelCase_	   =       is_training
         UpperCAmelCase_	   =       num_labels
         UpperCAmelCase_	   =       initializer_range
         UpperCAmelCase_	   =       scope
         UpperCAmelCase_	   =       width_multiplier
         UpperCAmelCase_	   =       ffn_dropout
         UpperCAmelCase_	   =       attn_dropout



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

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

         UpperCAmelCase_	   =       self.get_config()

         return config, pixel_values, labels, pixel_labels



    def  _lowercase						(self   :   Any			):
         return MobileViTVaConfig(
             image_size=self.image_size       ,      patch_size=self.patch_size       ,      num_channels=self.num_channels       ,      hidden_act=self.hidden_act       ,      conv_kernel_size=self.conv_kernel_size       ,      output_stride=self.output_stride       ,      classifier_dropout_prob=self.classifier_dropout_prob       ,      initializer_range=self.initializer_range       ,      width_multiplier=self.width_multiplier       ,      ffn_dropout=self.ffn_dropout_prob       ,      attn_dropout=self.attn_dropout_prob       ,      )



    def  _lowercase						(self   :   str       ,      __a   :   Union[str, Any]       ,      __a   :   Union[str, Any]       ,      __a   :   Any       ,      __a   :   Tuple			):
         UpperCAmelCase_	   =       MobileViTVaModel(config=__a			)
         model.to(__a			)
         model.eval()
         UpperCAmelCase_	   =       model(__a			)
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,      (
                 self.batch_size,
                 self.last_hidden_size,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )       ,      )



    def  _lowercase						(self   :   int       ,      __a   :   Optional[Any]       ,      __a   :   List[Any]       ,      __a   :   int       ,      __a   :   Any			):
         UpperCAmelCase_	   =       self.num_labels
         UpperCAmelCase_	   =       MobileViTVaForImageClassification(__a			)
         model.to(__a			)
         model.eval()
         UpperCAmelCase_	   =       model(__a       ,      labels=__a			)
         self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)			)



    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   List[str]       ,      __a   :   Any       ,      __a   :   Any       ,      __a   :   Optional[Any]			):
         UpperCAmelCase_	   =       self.num_labels
         UpperCAmelCase_	   =       MobileViTVaForSemanticSegmentation(__a			)
         model.to(__a			)
         model.eval()
         UpperCAmelCase_	   =       model(__a			)
         self.parent.assertEqual(
             result.logits.shape       ,      (
                 self.batch_size,
                 self.num_labels,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )       ,      )
         UpperCAmelCase_	   =       model(__a       ,      labels=__a			)
         self.parent.assertEqual(
             result.logits.shape       ,      (
                 self.batch_size,
                 self.num_labels,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )       ,      )






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




@require_torch
class 	__A				(			UpperCamelCase__		,     UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						str	       =  (
        (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
        if is_torch_available()
        else ()
    )

    a__	:						Any	       =  (
        {
            """feature-extraction""": MobileViTVaModel,
            """image-classification""": MobileViTVaForImageClassification,
            """image-segmentation""": MobileViTVaForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    a__	:						List[Any]	       =  False
    a__	:						str	       =  False
    a__	:						Union[str, Any]	       =  False
    a__	:						int	       =  False



    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       MobileViTVaModelTester(self			)
         UpperCAmelCase_	   =       MobileViTVaConfigTester(self       ,      config_class=__a       ,      has_text_modality=__a			)



    def  _lowercase						(self   :   Any			):
         self.config_tester.run_common_tests()



    @unittest.skip(reason="MobileViTV2 does not use inputs_embeds"			)
    def  _lowercase						(self   :   str			):
         pass



    @unittest.skip(reason="MobileViTV2 does not support input and output embeddings"			)
    def  _lowercase						(self   :   Optional[Any]			):
         pass



    @unittest.skip(reason="MobileViTV2 does not output attentions"			)
    def  _lowercase						(self   :   Any			):
         pass



    @require_torch_multi_gpu
    @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."			)
    def  _lowercase						(self   :   List[str]			):
         pass



    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."			)
    def  _lowercase						(self   :   Union[str, Any]			):
         pass



    def  _lowercase						(self   :   List[str]			):
         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.forward			)
              # 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[Any]			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a			)



    def  _lowercase						(self   :   int			):
         def check_hidden_states_output(__a   :   List[Any]       ,      __a   :   List[Any]       ,      __a   :   Optional[int]			):
              UpperCAmelCase_	   =       model_class(__a			)
              model.to(__a			)
              model.eval()

              with torch.no_grad():
                   UpperCAmelCase_	   =       model(**self._prepare_for_class(__a       ,      __a			)			)

              UpperCAmelCase_	   =       outputs.hidden_states

              UpperCAmelCase_	   =       5
              self.assertEqual(len(__a			)       ,      __a			)

              # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
              # with the width and height being successively divided by 2.
              UpperCAmelCase_	   =       2
              for i in range(len(__a			)			):
                   self.assertListEqual(
                       list(hidden_states[i].shape[-2:]			)       ,      [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]       ,      )
                   divisor *= 2

              self.assertEqual(self.model_tester.output_stride       ,      divisor // 2			)

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_	   =       True
              check_hidden_states_output(__a       ,      __a       ,      __a			)

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

              check_hidden_states_output(__a       ,      __a       ,      __a			)



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



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






    @slow
    def  _lowercase						(self   :   Any			):
         for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_	   =       MobileViTVaModel.from_pretrained(__a			)
              self.assertIsNotNone(__a			)





def      lowerCAmelCase_    ( )   ->				int:


     '''simple docstring'''




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






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



    @cached_property
    def  _lowercase						(self   :   Union[str, Any]			):
         return (
             MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"			)
             if is_vision_available()
             else None
         )



    @slow
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"			).to(
             __a			)

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

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_	   =       model(**__a			)

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

         UpperCAmelCase_	   =       torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]			).to(__a			)

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



    @slow
    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"			)
         UpperCAmelCase_	   =       model.to(__a			)

         UpperCAmelCase_	   =       MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"			)

         UpperCAmelCase_	   =       prepare_img()
         UpperCAmelCase_	   =       image_processor(images=__a       ,      return_tensors="pt"			).to(__a			)

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_	   =       model(**__a			)
         UpperCAmelCase_	   =       outputs.logits

         # verify the logits
         UpperCAmelCase_	   =       torch.Size((1, 21, 32, 32)			)
         self.assertEqual(logits.shape       ,      __a			)

         UpperCAmelCase_	   =       torch.tensor(
             [
                 [[7.08_63, 7.15_25, 6.82_01], [6.69_31, 6.87_70, 6.89_33], [6.29_78, 7.03_66, 6.96_36]],
                 [[-3.71_34, -3.67_12, -3.66_75], [-3.58_25, -3.35_49, -3.47_77], [-3.34_35, -3.39_79, -3.28_57]],
                 [[-2.93_29, -2.80_03, -2.73_69], [-3.05_64, -2.47_80, -2.02_07], [-2.68_89, -1.92_98, -1.76_40]],
             ]       ,      device=__a       ,      )

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






    @slow
    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"			)
         UpperCAmelCase_	   =       model.to(__a			)

         UpperCAmelCase_	   =       MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"			)

         UpperCAmelCase_	   =       prepare_img()
         UpperCAmelCase_	   =       image_processor(images=__a       ,      return_tensors="pt"			).to(__a			)

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_	   =       model(**__a			)

         UpperCAmelCase_	   =       outputs.logits.detach().cpu()

         UpperCAmelCase_	   =       image_processor.post_process_semantic_segmentation(outputs=__a       ,      target_sizes=[(50, 60)]			)
         UpperCAmelCase_	   =       torch.Size((50, 60)			)
         self.assertEqual(segmentation[0].shape       ,      __a			)

         UpperCAmelCase_	   =       image_processor.post_process_semantic_segmentation(outputs=__a			)
         UpperCAmelCase_	   =       torch.Size((32, 32)			)
         self.assertEqual(segmentation[0].shape       ,      __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




