

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import MobileViTImageProcessor






class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			def __init__(			self							: Any      ,      a							: List[Any]      ,      a							: Tuple=7      ,      a							: List[str]=3      ,      a							: Optional[int]=18      ,      a							: List[str]=30      ,      a							: Dict=400      ,      a							: Dict=True      ,      a							: int=None      ,      a							: List[Any]=True      ,      a							: Dict=None      ,      a							: List[str]=True      ,      )							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       size if size is not None else {"shortest_edge": 20}
				SCREAMING_SNAKE_CASE   :      Any				     =       crop_size if crop_size is not None else {"height": 18, "width": 18}
				SCREAMING_SNAKE_CASE   :      Dict				     =       parent
				SCREAMING_SNAKE_CASE   :      Dict				     =       batch_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      Dict				     =       image_size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       min_resolution
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       max_resolution
				SCREAMING_SNAKE_CASE   :      Dict				     =       do_resize
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       do_center_crop
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       crop_size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       do_flip_channel_order






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

				"""simple docstring"""

				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_center_crop": self.do_center_crop,
				    "crop_size": self.crop_size,
				    "do_flip_channel_order": self.do_flip_channel_order,
				}






@require_torch
@require_vision
class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =MobileViTImageProcessor if is_vision_available() else None
			def 							__UpperCamelCase (			self							: Any	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       MobileViTImageProcessingTester(self	)
			@property
			def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(a      ,      "do_resize"	)	)
				self.assertTrue(hasattr(a      ,      "size"	)	)
				self.assertTrue(hasattr(a      ,      "do_center_crop"	)	)
				self.assertTrue(hasattr(a      ,      "center_crop"	)	)
				self.assertTrue(hasattr(a      ,      "do_flip_channel_order"	)	)
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.image_processing_class.from_dict(self.image_processor_dict	)
				self.assertEqual(image_processor.size      ,      {"shortest_edge": 20}	)
				self.assertEqual(image_processor.crop_size      ,      {"height": 18, "width": 18}	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42      ,      crop_size=84	)
				self.assertEqual(image_processor.size      ,      {"shortest_edge": 42}	)
				self.assertEqual(image_processor.crop_size      ,      {"height": 84, "width": 84}	)
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       List[Any]:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				SCREAMING_SNAKE_CASE   :      Dict				     =       prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=a	)
				for image in image_inputs:
					self.assertIsInstance(a      ,      Image.Image	)

				# Test not batched input
				SCREAMING_SNAKE_CASE   :      Dict				     =       image_processing(image_inputs[0]      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )

				# Test batched
				SCREAMING_SNAKE_CASE   :      int				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )
			def 							__UpperCamelCase (			self							: Any	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=a      ,      numpify=a	)
				for image in image_inputs:
					self.assertIsInstance(a      ,      np.ndarray	)

				# Test not batched input
				SCREAMING_SNAKE_CASE   :      Any				     =       image_processing(image_inputs[0]      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )

				# Test batched
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )






			def 							__UpperCamelCase (			self							: Dict	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE   :      Tuple				     =       prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=a      ,      torchify=a	)
				for image in image_inputs:
					self.assertIsInstance(a      ,      torch.Tensor	)

				# Test not batched input
				SCREAMING_SNAKE_CASE   :      Any				     =       image_processing(image_inputs[0]      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )

				# Test batched
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )


from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@register_to_config
			def __init__(			self							: str      ,      a							: int = 16      ,      a							: int = 88      ,      a							: Optional[int] = None      ,      a							: Optional[int] = None      ,      a							: int = 1      ,      a							: float = 0.0      ,      a							: int = 32      ,      a							: Optional[int] = None      ,      a							: bool = False      ,      a							: Optional[int] = None      ,      a							: str = "geglu"      ,      a							: bool = True      ,      a							: bool = True      ,      )							->       str:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE   :      str				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      Tuple				     =       attention_head_dim
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_attention_heads * attention_head_dim

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       in_channels

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.nn.GroupNorm(num_groups=a      ,      num_channels=a      ,      eps=1e-6      ,      affine=a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       nn.Linear(a      ,      a	)

				# 3. Define transformers blocks
				SCREAMING_SNAKE_CASE   :      List[str]				     =       nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            a      ,      a      ,      a      ,      dropout=a      ,      cross_attention_dim=a      ,      activation_fn=a      ,      attention_bias=a      ,      double_self_attention=a      ,      norm_elementwise_affine=a      ,      )
				        for d in range(a	)
				    ]	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       nn.Linear(a      ,      a	)






			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: Optional[int]=None      ,      a							: List[str]=None      ,      a							: List[str]=None      ,      a							: Union[str, Any]=1      ,      a							: Any=None      ,      a							: bool = True      ,      )							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       hidden_states.shape
				SCREAMING_SNAKE_CASE   :      Any				     =       batch_frames // num_frames

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hidden_states

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       hidden_states[None, :].reshape(a      ,      a      ,      a      ,      a      ,      a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hidden_states.permute(0      ,      2      ,      1      ,      3      ,      4	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.norm(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hidden_states.permute(0      ,      3      ,      4      ,      2      ,      1	).reshape(batch_size * height * width      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.proj_in(a	)

				# 2. Blocks
				for block in self.transformer_blocks:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       block(
					    a      ,      encoder_hidden_states=a      ,      timestep=a      ,      cross_attention_kwargs=a      ,      class_labels=a      ,      )

				# 3. Output
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.proj_out(a	)
				SCREAMING_SNAKE_CASE   :      int				     =       (
				    hidden_states[None, None, :]
				    .reshape(a      ,      a      ,      a      ,      a      ,      a	)
				    .permute(0      ,      3      ,      4      ,      1      ,      2	)
				    .contiguous()
				)
				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_states.reshape(a      ,      a      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      int				     =       hidden_states + residual

				if not return_dict:
					return (output,)

				return TransformerTemporalModelOutput(sample=a	)