


"""simple docstring"""






import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
					import torch

					from transformers import BlipTextModel
					from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=12	,					lowerCamelCase__=7	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=99	,					lowerCamelCase__=32	,					lowerCamelCase__=32	,					lowerCamelCase__=5	,					lowerCamelCase__=4	,					lowerCamelCase__=37	,					lowerCamelCase__=0.1	,					lowerCamelCase__=0.1	,					lowerCamelCase__=512	,					lowerCamelCase__=0.02	,					lowerCamelCase__=0	,					lowerCamelCase__=None	,					)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      parent
								snake_case__  :						str		=      batch_size
								snake_case__  :						Optional[int]		=      seq_length
								snake_case__  :						Optional[int]		=      is_training
								snake_case__  :						str		=      use_input_mask
								snake_case__  :						Any		=      use_labels
								snake_case__  :						Tuple		=      vocab_size
								snake_case__  :						Optional[Any]		=      hidden_size
								snake_case__  :						List[str]		=      projection_dim
								snake_case__  :						List[Any]		=      num_hidden_layers
								snake_case__  :						List[str]		=      num_attention_heads
								snake_case__  :						Optional[Any]		=      intermediate_size
								snake_case__  :						Any		=      dropout
								snake_case__  :						Optional[int]		=      attention_dropout
								snake_case__  :						List[Any]		=      max_position_embeddings
								snake_case__  :						Optional[Any]		=      initializer_range
								snake_case__  :						Tuple		=      scope
								snake_case__  :						int		=      bos_token_id




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      ids_tensor([self.batch_size, self.seq_length]	,					self.vocab_size)

								snake_case__  :						List[str]		=      None
								if self.use_input_mask:
									snake_case__  :						List[Any]		=      random_attention_mask([self.batch_size, self.seq_length])

								if input_mask is not None:
									snake_case__,					snake_case__  :						str		=      input_mask.shape
									snake_case__  :						List[Any]		=      np.random.randint(1	,					seq_length - 1	,					size=(batch_size,))
									for batch_idx, start_index in enumerate(lowerCamelCase__):
										snake_case__  :						Tuple		=      1
										snake_case__  :						List[str]		=      0

								snake_case__  :						str		=      self.get_config()

								return config, input_ids, input_mask




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								return BlipTextConfig(
								    vocab_size=self.vocab_size	,					hidden_size=self.hidden_size	,					projection_dim=self.projection_dim	,					num_hidden_layers=self.num_hidden_layers	,					num_attention_heads=self.num_attention_heads	,					intermediate_size=self.intermediate_size	,					dropout=self.dropout	,					attention_dropout=self.attention_dropout	,					max_position_embeddings=self.max_position_embeddings	,					initializer_range=self.initializer_range	,					bos_token_id=self.bos_token_id	,					)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      BlipTextModel(config=lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								with torch.no_grad():
									snake_case__  :						Union[str, Any]		=      model(lowerCamelCase__	,					attention_mask=lowerCamelCase__)
									snake_case__  :						Optional[int]		=      model(lowerCamelCase__)
								self.parent.assertEqual(result.last_hidden_state.shape	,					(self.batch_size, self.seq_length, self.hidden_size))
								self.parent.assertEqual(result.pooler_output.shape	,					(self.batch_size, self.hidden_size))







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







								'''simple docstring'''




								snake_case__  :						Any		=      self.prepare_config_and_inputs()
								snake_case__,					snake_case__,					snake_case__  :						Tuple		=      config_and_inputs
								snake_case__  :						Dict		=      {"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_torch
class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Tuple				  =  (BlipTextModel,) if is_torch_available() else ()
							__magic_name__					:     Tuple				  =  False
							__magic_name__					:     Tuple				  =  False
							__magic_name__					:     int				  =  False




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      BlipTextModelTester(self)
								snake_case__  :						Any		=      ConfigTester(self	,					config_class=lowerCamelCase__	,					hidden_size=37)




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								self.config_tester.run_common_tests()




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase__)




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







								'''simple docstring'''




								pass




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







								'''simple docstring'''




								pass




							@unittest.skip(reason="Blip does not use inputs_embeds")
							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								pass




							@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								pass




							@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								pass




							@slow
							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									snake_case__  :						Optional[int]		=      BlipTextModel.from_pretrained(lowerCamelCase__)
									self.assertIsNotNone(lowerCamelCase__)







							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCamelCase__)






'''simple docstring'''
import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import NatBackbone, NatForImageClassification, NatModel
 from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class _lowerCAmelCase			:







      def __init__(  self  ,    lowercase  ,    lowercase=13  ,    lowercase=64  ,    lowercase=4  ,    lowercase=3  ,    lowercase=16  ,    lowercase=[1, 2, 1]  ,    lowercase=[2, 4, 8]  ,    lowercase=3  ,    lowercase=2.0  ,    lowercase=True  ,    lowercase=0.0  ,    lowercase=0.0  ,    lowercase=0.1  ,    lowercase="gelu"  ,    lowercase=True  ,    lowercase=0.02  ,    lowercase=1e-5  ,    lowercase=True  ,    lowercase=None  ,    lowercase=True  ,    lowercase=10  ,    lowercase=["stage1", "stage2"]  ,    lowercase=[1, 2]  ,    ):
        lowercase				:							Any									=   parent
        lowercase				:							Optional[Any]									=   batch_size
        lowercase				:							Tuple									=   image_size
        lowercase				:							List[str]									=   patch_size
        lowercase				:							Optional[Any]									=   num_channels
        lowercase				:							str									=   embed_dim
        lowercase				:							List[Any]									=   depths
        lowercase				:							Any									=   num_heads
        lowercase				:							str									=   kernel_size
        lowercase				:							Any									=   mlp_ratio
        lowercase				:							str									=   qkv_bias
        lowercase				:							List[Any]									=   hidden_dropout_prob
        lowercase				:							List[str]									=   attention_probs_dropout_prob
        lowercase				:							Tuple									=   drop_path_rate
        lowercase				:							Dict									=   hidden_act
        lowercase				:							Union[str, Any]									=   patch_norm
        lowercase				:							int									=   layer_norm_eps
        lowercase				:							Any									=   initializer_range
        lowercase				:							Tuple									=   is_training
        lowercase				:							int									=   scope
        lowercase				:							Any									=   use_labels
        lowercase				:							Any									=   num_labels
        lowercase				:							List[Any]									=   out_features
        lowercase				:							List[Any]									=   out_indices







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[Any]									=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        lowercase				:							Any									=   None
        if self.use_labels:
          lowercase				:							Tuple									=   ids_tensor([self.batch_size]  ,    self.num_labels     )

        lowercase				:							List[str]									=   self.get_config()

        return config, pixel_values, labels







      def      lowerCamelCase__      (  self     ):
        return NatConfig(
            num_labels=self.num_labels  ,    image_size=self.image_size  ,    patch_size=self.patch_size  ,    num_channels=self.num_channels  ,    embed_dim=self.embed_dim  ,    depths=self.depths  ,    num_heads=self.num_heads  ,    kernel_size=self.kernel_size  ,    mlp_ratio=self.mlp_ratio  ,    qkv_bias=self.qkv_bias  ,    hidden_dropout_prob=self.hidden_dropout_prob  ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,    drop_path_rate=self.drop_path_rate  ,    hidden_act=self.hidden_act  ,    patch_norm=self.patch_norm  ,    layer_norm_eps=self.layer_norm_eps  ,    initializer_range=self.initializer_range  ,    out_features=self.out_features  ,    out_indices=self.out_indices  ,    )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Any									=   NatModel(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							int									=   model(lowercase     )

        lowercase				:							List[str]									=   (config.image_size // config.patch_size) // (2 ** (len(config.depths     ) - 1))
        lowercase				:							Optional[int]									=   int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

        self.parent.assertEqual(
            result.last_hidden_state.shape  ,    (self.batch_size, expected_height, expected_width, expected_dim)     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Optional[int]									=   NatForImageClassification(lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							Dict									=   model(lowercase  ,    labels=lowercase     )
        self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.num_labels)     )

        # test greyscale images
        lowercase				:							Dict									=   1
        lowercase				:							Optional[Any]									=   NatForImageClassification(lowercase     )
        model.to(lowercase     )
        model.eval()

        lowercase				:							Tuple									=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
        lowercase				:							Dict									=   model(lowercase     )
        self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.num_labels)     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							int									=   NatBackbone(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							List[Any]									=   model(lowercase     )

        # verify hidden states
        self.parent.assertEqual(len(result.feature_maps     )  ,    len(config.out_features     )     )
        self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,    [self.batch_size, model.channels[0], 16, 16]     )

        # verify channels
        self.parent.assertEqual(len(model.channels     )  ,    len(config.out_features     )     )

        # verify backbone works with out_features=None
        lowercase				:							List[Any]									=   None
        lowercase				:							int									=   NatBackbone(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							Optional[int]									=   model(lowercase     )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps     )  ,    1     )
        self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,    [self.batch_size, model.channels[-1], 4, 4]     )

        # verify channels
        self.parent.assertEqual(len(model.channels     )  ,    1     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							int									=   self.prepare_config_and_inputs()
        lowercase				,      lowercase				,      lowercase				:							Optional[int]									=   config_and_inputs
        lowercase				:							List[Any]									=   {'''pixel_values''': pixel_values}
        return config, inputs_dict





@require_natten
@require_torch
class _lowerCAmelCase			(	snake_case							,   snake_case							,   unittest.TestCase					):
      UpperCAmelCase_	:						List[str]	 =       (
          (
              NatModel,
              NatForImageClassification,
              NatBackbone,
          )
          if is_torch_available()
          else ()
      )
      UpperCAmelCase_	:						List[str]	 =       (
          {"feature-extraction": NatModel, "image-classification": NatForImageClassification}
          if is_torch_available()
          else {}
      )
      UpperCAmelCase_	:						List[str]	 =       False

      UpperCAmelCase_	:						str	 =       False
      UpperCAmelCase_	:						Any	 =       False
      UpperCAmelCase_	:						Dict	 =       False
      UpperCAmelCase_	:						Optional[Any]	 =       False







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[int]									=   NatModelTester(self     )
        lowercase				:							Union[str, Any]									=   ConfigTester(self  ,    config_class=lowercase  ,    embed_dim=37     )







      def      lowerCamelCase__      (  self     ):
        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     ):
        return







      def      lowerCamelCase__      (  self     ):
        lowercase				:							List[str]									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Tuple									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Union[str, Any]									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowercase     )







      @unittest.skip(reason='''Nat does not use inputs_embeds'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      @unittest.skip(reason='''Nat does not use feedforward chunking'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							List[str]									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowercase				:							str									=   model_class(lowercase     )
          self.assertIsInstance(model.get_input_embeddings()  ,    (nn.Module)     )
          lowercase				:							Any									=   model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(lowercase  ,    nn.Linear     )     )







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							List[Any]									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowercase				:							int									=   model_class(lowercase     )
          lowercase				:							List[Any]									=   inspect.signature(model.forward     )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowercase				:							str									=   [*signature.parameters.keys()]

          lowercase				:							Tuple									=   ['''pixel_values''']
          self.assertListEqual(arg_names[:1]  ,    lowercase     )







      def      lowerCamelCase__      (  self     ):
        self.skipTest('''Nat\'s attention operation is handled entirely by NATTEN.'''     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Dict									=   model_class(lowercase     )
        model.to(lowercase     )
        model.eval()

        with torch.no_grad():
          lowercase				:							Tuple									=   model(**self._prepare_for_class(lowercase  ,    lowercase     )     )

        lowercase				:							List[Any]									=   outputs.hidden_states

        lowercase				:							Tuple									=   getattr(
            self.model_tester  ,    '''expected_num_hidden_layers'''  ,    len(self.model_tester.depths     ) + 1     )
        self.assertEqual(len(lowercase     )  ,    lowercase     )

        # Nat has a different seq_length
        lowercase				:							Optional[Any]									=   (
            config.patch_size
            if isinstance(config.patch_size  ,    collections.abc.Iterable     )
            else (config.patch_size, config.patch_size)
        )

        lowercase				:							Union[str, Any]									=   image_size[0] // patch_size[0]
        lowercase				:							List[str]									=   image_size[1] // patch_size[1]

        self.assertListEqual(
            list(hidden_states[0].shape[-3:]     )  ,    [height, width, self.model_tester.embed_dim]  ,    )

        if model_class.__name__ != "NatBackbone":
          lowercase				:							str									=   outputs.reshaped_hidden_states
          self.assertEqual(len(lowercase     )  ,    lowercase     )

          lowercase				,      lowercase				,      lowercase				,      lowercase				:							Any									=   reshaped_hidden_states[0].shape
          lowercase				:							Tuple									=   (
              reshaped_hidden_states[0].view(lowercase  ,    lowercase  ,    lowercase  ,    lowercase     ).permute(0  ,    2  ,    3  ,    1     )
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-3:]     )  ,    [height, width, self.model_tester.embed_dim]  ,    )







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							Optional[Any]									=   self.model_tester.prepare_config_and_inputs_for_common()

        lowercase				:							Any									=   (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size  ,    collections.abc.Iterable     )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes:
          lowercase				:							Union[str, Any]									=   True
          self.check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase  ,    lowercase     )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowercase				:							Union[str, Any]									=   True

          self.check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase  ,    lowercase     )







      @slow
      def      lowerCamelCase__      (  self     ):
        for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase				:							Any									=   NatModel.from_pretrained(lowercase     )
          self.assertIsNotNone(lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							Tuple									=   self.model_tester.prepare_config_and_inputs_for_common()

        lowercase				:							Dict									=   _config_zero_init(lowercase     )
        for model_class in self.all_model_classes:
          lowercase				:							int									=   model_class(config=lowercase     )
          for name, param in model.named_parameters():
            if "embeddings" not in name and param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9).round() / 1e9).item()  ,    [0.0, 1.0]  ,    msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""  ,    )





@require_natten
@require_vision
@require_torch
class _lowerCAmelCase			(	unittest.TestCase					):







      @cached_property
      def      lowerCamelCase__      (  self     ):
        return AutoImageProcessor.from_pretrained('''shi-labs/nat-mini-in1k-224'''     ) if is_vision_available() else None







      @slow
      def      lowerCamelCase__      (  self     ):
        lowercase				:							int									=   NatForImageClassification.from_pretrained('''shi-labs/nat-mini-in1k-224'''     ).to(lowercase     )
        lowercase				:							Union[str, Any]									=   self.default_image_processor

        lowercase				:							Optional[int]									=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
        lowercase				:							str									=   image_processor(images=lowercase  ,    return_tensors='''pt'''     ).to(lowercase     )

        # forward pass
        with torch.no_grad():
          lowercase				:							Dict									=   model(**lowercase     )

        # verify the logits
        lowercase				:							List[Any]									=   torch.Size((1, 1000)     )
        self.assertEqual(outputs.logits.shape  ,    lowercase     )
        lowercase				:							Optional[int]									=   torch.tensor([0.3805, -0.8676, -0.3912]     ).to(lowercase     )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,    lowercase  ,    atol=1e-4     )     )





@require_torch
@require_natten
class _lowerCAmelCase			(	unittest.TestCase							,   snake_case					):
      UpperCAmelCase_	:						Union[str, Any]	 =       (NatBackbone,) if is_torch_available() else ()
      UpperCAmelCase_	:						Optional[int]	 =       NatConfig







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[int]									=   NatModelTester(self     )