



import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, 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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
   from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


class 	lowerCamelCase__	:



 """simple docstring"""

 def __init__(    self					:					str			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					List[Any]=13			,						__lowerCAmelCase					:					Tuple=32			,						__lowerCAmelCase					:					Optional[Any]=2			,						__lowerCAmelCase					:					Optional[Any]=3			,						__lowerCAmelCase					:					int=16			,						__lowerCAmelCase					:					int=[1, 2, 1]			,						__lowerCAmelCase					:					int=[2, 2, 4]			,						__lowerCAmelCase					:					Optional[int]=2			,						__lowerCAmelCase					:					int=2.0			,						__lowerCAmelCase					:					Optional[int]=True			,						__lowerCAmelCase					:					Optional[int]=0.0			,						__lowerCAmelCase					:					Dict=0.0			,						__lowerCAmelCase					:					Any=0.1			,						__lowerCAmelCase					:					Tuple="gelu"			,						__lowerCAmelCase					:					Tuple=False			,						__lowerCAmelCase					:					Any=True			,						__lowerCAmelCase					:					int=0.02			,						__lowerCAmelCase					:					List[str]=1E-5			,						__lowerCAmelCase					:					Optional[int]=True			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Union[str, Any]=True			,						__lowerCAmelCase					:					List[str]=10			,						__lowerCAmelCase					:					Dict=8			,						)   ->	Optional[int]:
    _A      =       parent
    _A      =       batch_size
    _A      =       image_size
    _A      =       patch_size
    _A      =       num_channels
    _A      =       embed_dim
    _A      =       depths
    _A      =       num_heads
    _A      =       window_size
    _A      =       mlp_ratio
    _A      =       qkv_bias
    _A      =       hidden_dropout_prob
    _A      =       attention_probs_dropout_prob
    _A      =       drop_path_rate
    _A      =       hidden_act
    _A      =       use_absolute_embeddings
    _A      =       patch_norm
    _A      =       layer_norm_eps
    _A      =       initializer_range
    _A      =       is_training
    _A      =       scope
    _A      =       use_labels
    _A      =       type_sequence_label_size
    _A      =       encoder_stride

 def     snake_case_      (    self					:					List[str]      )   ->	str:
    _A      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

    _A      =       self.get_config()

    return config, pixel_values, labels

 def     snake_case_      (    self					:					int      )   ->	List[Any]:
    return SwinvaConfig(
        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			,						window_size=self.window_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			,						use_absolute_embeddings=self.use_absolute_embeddings			,						path_norm=self.patch_norm			,						layer_norm_eps=self.layer_norm_eps			,						initializer_range=self.initializer_range			,						encoder_stride=self.encoder_stride			,						)

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					Dict      )   ->	Union[str, Any]:
    _A      =       SwinvaModel(config=__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(__lowerCAmelCase      )

    _A      =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
    _A      =       int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

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

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Optional[int]:
    _A      =       SwinvaForMaskedImageModeling(config=__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(__lowerCAmelCase      )
    self.parent.assertEqual(
        result.logits.shape			,						(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

    # test greyscale images
    _A      =       1
    _A      =       SwinvaForMaskedImageModeling(__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()

    _A      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
    _A      =       model(__lowerCAmelCase      )
    self.parent.assertEqual(result.logits.shape			,						(self.batch_size, 1, self.image_size, self.image_size)      )

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Any:
    _A      =       self.type_sequence_label_size
    _A      =       SwinvaForImageClassification(__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(__lowerCAmelCase			,						labels=__lowerCAmelCase      )
    self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.type_sequence_label_size)      )

 def     snake_case_      (    self					:					Tuple      )   ->	Tuple:
    _A      =       self.prepare_config_and_inputs()
    _A  ,    _A  ,    _A      =       config_and_inputs
    _A      =       {'''pixel_values''': pixel_values}
    return config, inputs_dict


@require_torch
class 	lowerCamelCase__	(       _A							,  _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: int =  (
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 a__					: Any =  (
     {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

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

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Union[str, Any]:
    _A      =       SwinvaModelTester(self      )
    _A      =       ConfigTester(self			,						config_class=__lowerCAmelCase			,						embed_dim=37      )

 def     snake_case_      (    self					:					Tuple      )   ->	Any:
    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     snake_case_      (    self					:					Dict      )   ->	Tuple:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase      )

 @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''      )
 def     snake_case_      (    self					:					Optional[int]      )   ->	Union[str, Any]:
    pass

 @unittest.skip(reason='''Swinv2 does not use inputs_embeds'''      )
 def     snake_case_      (    self					:					str      )   ->	str:
    pass

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[int]:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

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

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Tuple:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

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

       _A      =       ['''pixel_values''']
       self.assertListEqual(arg_names[:1]			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Dict      )   ->	Any:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()
    _A      =       True

    for model_class in self.all_model_classes:
       _A      =       True
       _A      =       False
       _A      =       True
       _A      =       model_class(__lowerCAmelCase      )
       model.to(__lowerCAmelCase      )
       model.eval()
       with torch.no_grad():
          _A      =       model(**self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase      )      )
       _A      =       outputs.attentions
       _A      =       len(self.model_tester.depths      )
       self.assertEqual(len(__lowerCAmelCase      )			,						__lowerCAmelCase      )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       _A      =       True
       _A      =       config.window_size**2
       _A      =       model_class(__lowerCAmelCase      )
       model.to(__lowerCAmelCase      )
       model.eval()
       with torch.no_grad():
          _A      =       model(**self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase      )      )
       _A      =       outputs.attentions
       self.assertEqual(len(__lowerCAmelCase      )			,						__lowerCAmelCase      )

       self.assertListEqual(
           list(attentions[0].shape[-3:]      )			,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,						)
       _A      =       len(__lowerCAmelCase      )

       # Check attention is always last and order is fine
       _A      =       True
       _A      =       True
       _A      =       model_class(__lowerCAmelCase      )
       model.to(__lowerCAmelCase      )
       model.eval()
       with torch.no_grad():
          _A      =       model(**self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase      )      )

       if hasattr(self.model_tester			,						'''num_hidden_states_types'''      ):
          _A      =       self.model_tester.num_hidden_states_types
       else:
          # also another +1 for reshaped_hidden_states
          _A      =       2
       self.assertEqual(out_len + added_hidden_states			,						len(__lowerCAmelCase      )      )

       _A      =       outputs.attentions

       self.assertEqual(len(__lowerCAmelCase      )			,						__lowerCAmelCase      )

       self.assertListEqual(
           list(self_attentions[0].shape[-3:]      )			,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,						)

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Optional[Any]:
    _A      =       model_class(__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()

    with torch.no_grad():
       _A      =       model(**self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase      )      )

    _A      =       outputs.hidden_states

    _A      =       getattr(
        self.model_tester			,						'''expected_num_hidden_layers'''			,						len(self.model_tester.depths      ) + 1      )
    self.assertEqual(len(__lowerCAmelCase      )			,						__lowerCAmelCase      )

    # Swinv2 has a different seq_length
    _A      =       (
        config.patch_size
        if isinstance(config.patch_size			,						collections.abc.Iterable      )
        else (config.patch_size, config.patch_size)
    )

    _A      =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

    self.assertListEqual(
        list(hidden_states[0].shape[-2:]      )			,						[num_patches, self.model_tester.embed_dim]			,						)

    _A      =       outputs.reshaped_hidden_states
    self.assertEqual(len(__lowerCAmelCase      )			,						__lowerCAmelCase      )

    _A  ,    _A  ,    _A  ,    _A      =       reshaped_hidden_states[0].shape
    _A      =       (
        reshaped_hidden_states[0].view(__lowerCAmelCase			,						__lowerCAmelCase			,						height * width      ).permute(0			,						2			,						1      )
    )
    self.assertListEqual(
        list(reshaped_hidden_states.shape[-2:]      )			,						[num_patches, self.model_tester.embed_dim]			,						)

 def     snake_case_      (    self					:					int      )   ->	Tuple:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

    _A      =       (
        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:
       _A      =       True
       self.check_hidden_states_output(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

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

       self.check_hidden_states_output(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[int]      )   ->	Optional[int]:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()
    _A      =       3

    _A      =       (
        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)
    )
    _A      =       (
        config.patch_size
        if isinstance(config.patch_size			,						collections.abc.Iterable      )
        else (config.patch_size, config.patch_size)
    )

    _A      =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
    _A      =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

    for model_class in self.all_model_classes:
       _A      =       True
       self.check_hidden_states_output(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						(padded_height, padded_width)      )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _A      =       True
       self.check_hidden_states_output(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						(padded_height, padded_width)      )

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[int]:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple      )   ->	List[str]:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase      )

 @slow
 def     snake_case_      (    self					:					Optional[Any]      )   ->	Tuple:
    for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _A      =       SwinvaModel.from_pretrained(__lowerCAmelCase      )
       self.assertIsNotNone(__lowerCAmelCase      )

 def     snake_case_      (    self					:					str      )   ->	Optional[Any]:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

    _A      =       _config_zero_init(__lowerCAmelCase      )
    for model_class in self.all_model_classes:
       _A      =       model_class(config=__lowerCAmelCase      )
       for name, param in model.named_parameters():
          if "embeddings" not in name and "logit_scale" 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_vision
@require_torch
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 @cached_property
 def     snake_case_      (    self					:					List[Any]      )   ->	int:
    return (
        AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''      )
        if is_vision_available()
        else None
    )

 @slow
 def     snake_case_      (    self					:					str      )   ->	Dict:
    _A      =       SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''      ).to(
        __lowerCAmelCase      )
    _A      =       self.default_image_processor

    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       image_processor(images=__lowerCAmelCase			,						return_tensors='''pt'''      ).to(__lowerCAmelCase      )

    # forward pass
    with torch.no_grad():
       _A      =       model(**__lowerCAmelCase      )

    # verify the logits
    _A      =       torch.Size((1, 10_00)      )
    self.assertEqual(outputs.logits.shape			,						__lowerCAmelCase      )
    _A      =       torch.tensor([-0.3947, -0.4306, 0.0026]      ).to(__lowerCAmelCase      )
    self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						__lowerCAmelCase			,						atol=1E-4      )      )








from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

UpperCAmelCase_		 =					logging.get_logger(__name__)


@add_end_docstrings(_A)
class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						*__lowerCAmelCase					:					List[Any]			,						**__lowerCAmelCase					:					List[str]      )   ->	List[str]:
    super().__init__(*__lowerCAmelCase			,						**__lowerCAmelCase      )
    requires_backends(self			,						'''vision'''      )
    self.check_model_type(
        TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING      )

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Optional[Any]=None      )   ->	int:
    _A      =       {}
    _A      =       {}

    if prompt is not None:
       _A      =       prompt

    if generate_kwargs is not None:
       _A      =       generate_kwargs
    if max_new_tokens is not None:
       if "generate_kwargs" not in forward_kwargs:
          _A      =       {}
       if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
          raise ValueError(
              '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
              ''' please use only one'''      )
       _A      =       max_new_tokens
    return preprocess_params, forward_kwargs, {}

 def __call__(    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, List[str], "Image.Image", List["Image.Image"]]			,						**__lowerCAmelCase					:					Union[str, Any]      )   ->	Union[str, Any]:
    return super().__call__(__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Union[str, Any]=None      )   ->	int:
    _A      =       load_image(__lowerCAmelCase      )

    if prompt is not None:
       if not isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
          raise ValueError(
              f'''Received an invalid text input, got - {type(__lowerCAmelCase      )} - but expected a single string. '''
              '''Note also that one single text can be provided for conditional image to text generation.'''      )

       _A      =       self.model.config.model_type

       if model_type == "git":
          _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )
          _A      =       self.tokenizer(text=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase      ).input_ids
          _A      =       [self.tokenizer.cls_token_id] + input_ids
          _A      =       torch.tensor(__lowerCAmelCase      ).unsqueeze(0      )
          model_inputs.update({'''input_ids''': input_ids}      )

       elif model_type == "pix2struct":
          _A      =       self.image_processor(images=__lowerCAmelCase			,						header_text=__lowerCAmelCase			,						return_tensors=self.framework      )

       elif model_type != "vision-encoder-decoder":
          # vision-encoder-decoder does not support conditional generation
          _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )
          _A      =       self.tokenizer(__lowerCAmelCase			,						return_tensors=self.framework      )
          model_inputs.update(__lowerCAmelCase      )

       else:
          raise ValueError(f'''Model type {model_type} does not support conditional text generation'''      )

    else:
       _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )

    if self.model.config.model_type == "git" and prompt is None:
       _A      =       None

    return model_inputs

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Dict=None      )   ->	str:
    # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
    # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
    if (
        "input_ids" in model_inputs
        and isinstance(model_inputs['''input_ids''']			,						__lowerCAmelCase      )
        and all(x is None for x in model_inputs['''input_ids''']      )
    ):
       _A      =       None

    if generate_kwargs is None:
       _A      =       {}
    # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
    #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
    #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
    #  in the `_prepare_model_inputs` method.
    _A      =       model_inputs.pop(self.model.main_input_name      )
    _A      =       self.model.generate(__lowerCAmelCase			,						**__lowerCAmelCase			,						**__lowerCAmelCase      )
    return model_outputs

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    _A      =       []
    for output_ids in model_outputs:
       _A      =       {
           '''generated_text''': self.tokenizer.decode(
               __lowerCAmelCase			,						skip_special_tokens=__lowerCAmelCase			,						)
       }
       records.append(__lowerCAmelCase      )
    return records



