


'''simple docstring'''





import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin

class 						A__							(		__a   ,     __a  ):




						@register_to_config
						def __init__(       self					:      Union[str, Any]				,    _a					:      int = 128				,    _a					:      int = 256				,    _a					:      float = 2000.0				,    _a					:      int = 768				,    _a					:      int = 12				,    _a					:      int = 12				,    _a					:      int = 64				,    _a					:      int = 2048				,    _a					:      float = 0.1				,    )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__()

										_SCREAMING_SNAKE_CASE			  =nn.Sequential(
										    nn.Linear(snake_case__				,    d_model * 4				,    bias=snake_case__  )				,    nn.SiLU()				,    nn.Linear(d_model * 4				,    d_model * 4				,    bias=snake_case__  )				,    nn.SiLU()				,    )

										_SCREAMING_SNAKE_CASE			  =nn.Embedding(snake_case__				,    snake_case__  )
										_SCREAMING_SNAKE_CASE			  =False

										_SCREAMING_SNAKE_CASE			  =nn.Linear(snake_case__				,    snake_case__				,    bias=snake_case__  )

										_SCREAMING_SNAKE_CASE			  =nn.Dropout(p=snake_case__  )

										_SCREAMING_SNAKE_CASE			  =nn.ModuleList()
										for lyr_num in range(snake_case__  ):
														# FiLM conditional T5 decoder
														_SCREAMING_SNAKE_CASE			  =DecoderLayer(d_model=snake_case__				,    d_kv=snake_case__				,    num_heads=snake_case__				,    d_ff=snake_case__				,    dropout_rate=snake_case__  )
														self.decoders.append(snake_case__  )

										_SCREAMING_SNAKE_CASE			  =TaLayerNorm(snake_case__  )

										_SCREAMING_SNAKE_CASE			  =nn.Dropout(p=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =nn.Linear(snake_case__				,    snake_case__				,    bias=snake_case__  )




						def 							__UpperCamelCase				(       self					:      Any				,    _a					:      List[str]				,    _a					:      List[str]  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =torch.mul(query_input.unsqueeze(-1  )				,    key_input.unsqueeze(-2  )  )
										return mask.unsqueeze(-3  )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      Union[str, Any]				,    _a					:      Optional[int]				,    _a					:      str  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =decoder_input_tokens.shape
										assert decoder_noise_time.shape == (batch,)

										# decoder_noise_time is in [0, 1), so rescale to expected timing range.
										_SCREAMING_SNAKE_CASE			  =get_timestep_embedding(
										    decoder_noise_time * self.config.max_decoder_noise_time				,    embedding_dim=self.config.d_model				,    max_period=self.config.max_decoder_noise_time				,    ).to(dtype=self.dtype  )

										_SCREAMING_SNAKE_CASE			  =self.conditioning_emb(snake_case__  ).unsqueeze(1  )

										assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

										_SCREAMING_SNAKE_CASE			  =decoder_input_tokens.shape[1]

										# If we want to use relative positions for audio context, we can just offset
										# this sequence by the length of encodings_and_masks.
										_SCREAMING_SNAKE_CASE			  =torch.broadcast_to(
										    torch.arange(snake_case__				,    device=decoder_input_tokens.device  )				,    (batch, seq_length)				,    )

										_SCREAMING_SNAKE_CASE			  =self.position_encoding(snake_case__  )

										_SCREAMING_SNAKE_CASE			  =self.continuous_inputs_projection(snake_case__  )
										inputs += position_encodings
										_SCREAMING_SNAKE_CASE			  =self.dropout(snake_case__  )

										# decoder: No padding present.
										_SCREAMING_SNAKE_CASE			  =torch.ones(
										    decoder_input_tokens.shape[:2]				,    device=decoder_input_tokens.device				,    dtype=inputs.dtype  )

										# Translate encoding masks to encoder-decoder masks.
										_SCREAMING_SNAKE_CASE			  =[(x, self.encoder_decoder_mask(snake_case__				,    snake_case__  )) for x, y in encodings_and_masks]

										# cross attend style: concat encodings
										_SCREAMING_SNAKE_CASE			  =torch.cat([x[0] for x in encodings_and_encdec_masks]				,    dim=1  )
										_SCREAMING_SNAKE_CASE			  =torch.cat([x[1] for x in encodings_and_encdec_masks]				,    dim=-1  )

										for lyr in self.decoders:
														_SCREAMING_SNAKE_CASE			  =lyr(
														    snake_case__				,    conditioning_emb=snake_case__				,    encoder_hidden_states=snake_case__				,    encoder_attention_mask=snake_case__				,    )[0]

										_SCREAMING_SNAKE_CASE			  =self.decoder_norm(snake_case__  )
										_SCREAMING_SNAKE_CASE			  =self.post_dropout(snake_case__  )

										_SCREAMING_SNAKE_CASE			  =self.spec_out(snake_case__  )
										return spec_out

class 						A__							(		nn.Module  ):




						def __init__(       self					:      Any				,    _a					:      Tuple				,    _a					:      Union[str, Any]				,    _a					:      Dict				,    _a					:      int				,    _a					:      Union[str, Any]				,    _a					:      str=1E-6  )							->     Dict:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =nn.ModuleList()

										# cond self attention: layer 0
										self.layer.append(
										    TaLayerSelfAttentionCond(d_model=snake_case__				,    d_kv=snake_case__				,    num_heads=snake_case__				,    dropout_rate=snake_case__  )  )

										# cross attention: layer 1
										self.layer.append(
										    TaLayerCrossAttention(
										        d_model=snake_case__				,    d_kv=snake_case__				,    num_heads=snake_case__				,    dropout_rate=snake_case__				,    layer_norm_epsilon=snake_case__				,    )  )

										# Film Cond MLP + dropout: last layer
										self.layer.append(
										    TaLayerFFCond(d_model=snake_case__				,    d_ff=snake_case__				,    dropout_rate=snake_case__				,    layer_norm_epsilon=snake_case__  )  )




						def 							__UpperCamelCase				(       self					:      str				,    _a					:      int				,    _a					:      Tuple=None				,    _a					:      int=None				,    _a					:      Optional[Any]=None				,    _a					:      int=None				,    _a					:      List[Any]=None				,    )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.layer[0](
										    snake_case__				,    conditioning_emb=snake_case__				,    attention_mask=snake_case__				,    )

										if encoder_hidden_states is not None:
														_SCREAMING_SNAKE_CASE			  =torch.where(encoder_attention_mask > 0				,    0				,    -1E10  ).to(
														    encoder_hidden_states.dtype  )

														_SCREAMING_SNAKE_CASE			  =self.layer[1](
														    snake_case__				,    key_value_states=snake_case__				,    attention_mask=snake_case__				,    )

										# Apply Film Conditional Feed Forward layer
										_SCREAMING_SNAKE_CASE			  =self.layer[-1](snake_case__				,    snake_case__  )

										return (hidden_states,)

class 						A__							(		nn.Module  ):




						def __init__(       self					:      Optional[Any]				,    _a					:      List[str]				,    _a					:      str				,    _a					:      Any				,    _a					:      int  )							->     str:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =TaLayerNorm(snake_case__  )
										_SCREAMING_SNAKE_CASE			  =TaFiLMLayer(in_features=d_model * 4				,    out_features=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =Attention(query_dim=snake_case__				,    heads=snake_case__				,    dim_head=snake_case__				,    out_bias=snake_case__				,    scale_qk=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =nn.Dropout(snake_case__  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      Dict				,    _a					:      int=None				,    _a					:      str=None				,    )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.layer_norm(snake_case__  )

										if conditioning_emb is not None:
														_SCREAMING_SNAKE_CASE			  =self.FiLMLayer(snake_case__				,    snake_case__  )

										# Self-attention block
										_SCREAMING_SNAKE_CASE			  =self.attention(snake_case__  )

										_SCREAMING_SNAKE_CASE			  =hidden_states + self.dropout(snake_case__  )

										return hidden_states

class 						A__							(		nn.Module  ):




						def __init__(       self					:      Optional[Any]				,    _a					:      Tuple				,    _a					:      Tuple				,    _a					:      List[Any]				,    _a					:      Optional[Any]				,    _a					:      Tuple  )							->     List[str]:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =Attention(query_dim=snake_case__				,    heads=snake_case__				,    dim_head=snake_case__				,    out_bias=snake_case__				,    scale_qk=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =TaLayerNorm(snake_case__				,    eps=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =nn.Dropout(snake_case__  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      Any				,    _a					:      List[str]=None				,    _a					:      Dict=None				,    )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.layer_norm(snake_case__  )
										_SCREAMING_SNAKE_CASE			  =self.attention(
										    snake_case__				,    encoder_hidden_states=snake_case__				,    attention_mask=attention_mask.squeeze(1  )				,    )
										_SCREAMING_SNAKE_CASE			  =hidden_states + self.dropout(snake_case__  )
										return layer_output

class 						A__							(		nn.Module  ):




						def __init__(       self					:      Dict				,    _a					:      int				,    _a					:      Any				,    _a					:      Optional[int]				,    _a					:      Any  )							->     Tuple:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =TaDenseGatedActDense(d_model=snake_case__				,    d_ff=snake_case__				,    dropout_rate=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =TaFiLMLayer(in_features=d_model * 4				,    out_features=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =TaLayerNorm(snake_case__				,    eps=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =nn.Dropout(snake_case__  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      int				,    _a					:      Any=None  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.layer_norm(snake_case__  )
										if conditioning_emb is not None:
														_SCREAMING_SNAKE_CASE			  =self.film(snake_case__				,    snake_case__  )

										_SCREAMING_SNAKE_CASE			  =self.DenseReluDense(snake_case__  )
										_SCREAMING_SNAKE_CASE			  =hidden_states + self.dropout(snake_case__  )
										return hidden_states

class 						A__							(		nn.Module  ):




						def __init__(       self					:      int				,    _a					:      Any				,    _a					:      List[Any]				,    _a					:      Any  )							->     int:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =nn.Linear(snake_case__				,    snake_case__				,    bias=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =nn.Linear(snake_case__				,    snake_case__				,    bias=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =nn.Linear(snake_case__				,    snake_case__				,    bias=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =nn.Dropout(snake_case__  )
										_SCREAMING_SNAKE_CASE			  =NewGELUActivation()




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      Optional[Any]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.act(self.wi_a(snake_case__  )  )
										_SCREAMING_SNAKE_CASE			  =self.wi_a(snake_case__  )
										_SCREAMING_SNAKE_CASE			  =hidden_gelu * hidden_linear
										_SCREAMING_SNAKE_CASE			  =self.dropout(snake_case__  )

										_SCREAMING_SNAKE_CASE			  =self.wo(snake_case__  )
										return hidden_states

class 						A__							(		nn.Module  ):




						def __init__(       self					:      int				,    _a					:      List[str]				,    _a					:      Optional[int]=1E-6  )							->     int:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =nn.Parameter(torch.ones(snake_case__  )  )
										_SCREAMING_SNAKE_CASE			  =eps




						def 							__UpperCamelCase				(       self					:      Union[str, Any]				,    _a					:      List[str]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =hidden_states.to(torch.floataa  ).pow(2  ).mean(-1				,    keepdim=snake_case__  )
										_SCREAMING_SNAKE_CASE			  =hidden_states * torch.rsqrt(variance + self.variance_epsilon  )

										# convert into half-precision if necessary
										if self.weight.dtype in [torch.floataa, torch.bfloataa]:
														_SCREAMING_SNAKE_CASE			  =hidden_states.to(self.weight.dtype  )

										return self.weight * hidden_states

class 						A__							(		nn.Module  ):




						def 							__UpperCamelCase				(       self					:      Optional[int]				,    _a					:      torch.Tensor  )							->     Union[str, Any]:


										"""simple docstring"""





										return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi  ) * (input + 0.04_47_15 * torch.pow(snake_case__				,    3.0  ))  ))

class 						A__							(		nn.Module  ):




						def __init__(       self					:      Optional[Any]				,    _a					:      Optional[int]				,    _a					:      List[str]  )							->     Dict:


										"""simple docstring"""





										super().__init__()
										_SCREAMING_SNAKE_CASE			  =nn.Linear(snake_case__				,    out_features * 2				,    bias=snake_case__  )




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      List[str]				,    _a					:      Tuple  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scale_bias(snake_case__  )
										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =torch.chunk(snake_case__				,    2				,    -1  )
										_SCREAMING_SNAKE_CASE			  =x * (1 + scale) + shift
										return x



import unittest

import numpy as np
import requests

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

							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							snake_case_						:			Any													=							False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor

class 						A__							(		unittest.TestCase  ):




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =size if size is not None else {'''height''': 20, '''width''': 20}
										_SCREAMING_SNAKE_CASE			  =parent
										_SCREAMING_SNAKE_CASE			  =batch_size
										_SCREAMING_SNAKE_CASE			  =num_channels
										_SCREAMING_SNAKE_CASE			  =image_size
										_SCREAMING_SNAKE_CASE			  =min_resolution
										_SCREAMING_SNAKE_CASE			  =max_resolution
										_SCREAMING_SNAKE_CASE			  =size
										_SCREAMING_SNAKE_CASE			  =do_normalize
										_SCREAMING_SNAKE_CASE			  =do_convert_rgb
										_SCREAMING_SNAKE_CASE			  =[512, 1024, 2048, 4096]
										_SCREAMING_SNAKE_CASE			  =patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}




						def 							__UpperCamelCase				(       self					:      int  )							->     Any:


										"""simple docstring"""





										return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
										_SCREAMING_SNAKE_CASE			  =Image.open(requests.get(_a				,    stream=_a  ).raw  ).convert('''RGB'''  )
										return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,     reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   ,     )
@require_torch
@require_vision
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						PixaStructImageProcessor if is_vision_available() else None




						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =PixaStructImageProcessingTester(self  )




						@property
						def 							__UpperCamelCase				(       self					:      int  )							->     Any:


										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




						def 							__UpperCamelCase				(       self					:      str  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										self.assertTrue(hasattr(_a				,    '''do_normalize'''  )  )
										self.assertTrue(hasattr(_a				,    '''do_convert_rgb'''  )  )




						def 							__UpperCamelCase				(       self					:      int  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processor_tester.prepare_dummy_image()

										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										_SCREAMING_SNAKE_CASE			  =2048

										_SCREAMING_SNAKE_CASE			  =image_processor(_a				,    return_tensors='''pt'''				,    max_patches=_a  )
										self.assertTrue(torch.allclose(inputs.flattened_patches.mean()				,    torch.tensor(0.06_06  )				,    atol=1E-3				,    rtol=1E-3  )  )




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =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			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =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			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										_SCREAMING_SNAKE_CASE			  =True

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														with self.assertRaises(_a  ):
																		_SCREAMING_SNAKE_CASE			  =image_processor(
																		    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches

														_SCREAMING_SNAKE_CASE			  ='''Hello'''

														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a				,    header_text=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a				,    header_text=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random numpy tensors
										_SCREAMING_SNAKE_CASE			  =prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_a				,    numpify=_a  )
										for image in image_inputs:
														self.assertIsInstance(_a				,    np.ndarray  )

										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PyTorch tensors
										_SCREAMING_SNAKE_CASE			  =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			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,     reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   ,     )
@require_torch
@require_vision
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						PixaStructImageProcessor if is_vision_available() else None




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =PixaStructImageProcessingTester(self				,    num_channels=4  )
										_SCREAMING_SNAKE_CASE			  =3




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										self.assertTrue(hasattr(_a				,    '''do_normalize'''  )  )
										self.assertTrue(hasattr(_a				,    '''do_convert_rgb'''  )  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =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			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * (self.image_processor_tester.num_channels - 1)
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )