




'''simple docstring'''






import json
import os
import tempfile

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


if is_torch_available():
				import numpy as np
				import torch

if is_vision_available():
				from PIL import Image

def      __lowerCAmelCase   (       snake_case__   , snake_case__=False   , snake_case__=False   , snake_case__=False		):
			assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

			__UpperCamelCase :     Any										=  []
			for i in range(image_processor_tester.batch_size		):
						if equal_resolution:
									__UpperCamelCase :     Tuple										=  image_processor_tester.max_resolution
						else:
									# To avoid getting image width/height 0
									__UpperCamelCase :     int										=  image_processor_tester.min_resolution
									if getattr(snake_case__   , "size_divisor"   , snake_case__		):
												# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
												__UpperCamelCase :     Dict										=  max(image_processor_tester.size_divisor   , snake_case__		)
									__UpperCamelCase						,   __UpperCamelCase :     Any										=  np.random.choice(np.arange(snake_case__   , image_processor_tester.max_resolution		)   , 2		)
						image_inputs.append(
						    np.random.randint(255   , size=(image_processor_tester.num_channels, width, height)   , dtype=np.uinta		)		)

			if not numpify and not torchify:
						# PIL expects the channel dimension as last dimension
						__UpperCamelCase :     str										=  [Image.fromarray(np.moveaxis(snake_case__   , 0   , -1		)		) for image in image_inputs]

			if torchify:
						__UpperCamelCase :     str										=  [torch.from_numpy(snake_case__		) for image in image_inputs]

			return image_inputs

def      __lowerCAmelCase   (       snake_case__   , snake_case__=10   , snake_case__=10   , snake_case__=False   , snake_case__=False		):
			__UpperCamelCase :     Optional[Any]										=  []
			for i in range(image_processor_tester.num_frames		):
						video.append(np.random.randint(255   , size=(image_processor_tester.num_channels, width, height)   , dtype=np.uinta		)		)

			if not numpify and not torchify:
						# PIL expects the channel dimension as last dimension
						__UpperCamelCase :     List[str]										=  [Image.fromarray(np.moveaxis(snake_case__   , 0   , -1		)		) for frame in video]

			if torchify:
						__UpperCamelCase :     int										=  [torch.from_numpy(snake_case__		) for frame in video]

			return video

def      __lowerCAmelCase   (       snake_case__   , snake_case__=False   , snake_case__=False   , snake_case__=False		):
			assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

			__UpperCamelCase :     str										=  []
			for i in range(image_processor_tester.batch_size		):
						if equal_resolution:
									__UpperCamelCase :     List[str]										=  image_processor_tester.max_resolution
						else:
									__UpperCamelCase						,   __UpperCamelCase :     str										=  np.random.choice(
									    np.arange(image_processor_tester.min_resolution   , image_processor_tester.max_resolution		)   , 2		)
									__UpperCamelCase :     Dict										=  prepare_video(
									    image_processor_tester=snake_case__   , width=snake_case__   , height=snake_case__   , numpify=snake_case__   , torchify=snake_case__   , )
						video_inputs.append(snake_case__		)

			return video_inputs




class    A						:




				'''simple docstring'''





				A       =							None
				def 			a_			(self     )      ->			Optional[int]:
							__UpperCamelCase :     List[Any]										=  self.image_processing_class(**self.image_processor_dict     )
							__UpperCamelCase :     List[Any]										=  json.loads(image_processor.to_json_string()     )
							for key, value in self.image_processor_dict.items():
										self.assertEqual(obj[key]					,    _UpperCAmelCase     )
				def 			a_			(self     )      ->			Union[str, Any]:
							__UpperCamelCase :     Any										=  self.image_processing_class(**self.image_processor_dict     )

							with tempfile.TemporaryDirectory() as tmpdirname:
										__UpperCamelCase :     Union[str, Any]										=  os.path.join(_UpperCAmelCase					,    "image_processor.json"     )
										image_processor_first.to_json_file(_UpperCAmelCase     )
										__UpperCamelCase :     int										=  self.image_processing_class.from_json_file(_UpperCAmelCase     )

							self.assertEqual(image_processor_second.to_dict()					,    image_processor_first.to_dict()     )
				def 			a_			(self     )      ->			Union[str, Any]:
							__UpperCamelCase :     Any										=  self.image_processing_class(**self.image_processor_dict     )

							with tempfile.TemporaryDirectory() as tmpdirname:
										__UpperCamelCase :     Union[str, Any]										=  image_processor_first.save_pretrained(_UpperCAmelCase     )[0]
										check_json_file_has_correct_format(_UpperCAmelCase     )
										__UpperCamelCase :     Union[str, Any]										=  self.image_processing_class.from_pretrained(_UpperCAmelCase     )

							self.assertEqual(image_processor_second.to_dict()					,    image_processor_first.to_dict()     )
				def 			a_			(self     )      ->			Optional[Any]:
							__UpperCamelCase :     List[Any]										=  self.image_processing_class()
							self.assertIsNotNone(_UpperCAmelCase     )





				@require_torch
				@require_vision
				def 			a_			(self     )      ->			Union[str, Any]:
							if self.test_cast_dtype is not None:
										# Initialize image_processor
										__UpperCamelCase :     Tuple										=  self.image_processing_class(**self.image_processor_dict     )

										# create random PyTorch tensors
										__UpperCamelCase :     Dict										=  prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_UpperCAmelCase					,    torchify=_UpperCAmelCase     )

										__UpperCamelCase :     List[str]										=  image_processor(_UpperCAmelCase					,    return_tensors="pt"     )
										# for layoutLM compatiblity
										self.assertEqual(encoding.pixel_values.device					,    torch.device("cpu"     )     )
										self.assertEqual(encoding.pixel_values.dtype					,    torch.floataa     )

										__UpperCamelCase :     str										=  image_processor(_UpperCAmelCase					,    return_tensors="pt"     ).to(torch.floataa     )
										self.assertEqual(encoding.pixel_values.device					,    torch.device("cpu"     )     )
										self.assertEqual(encoding.pixel_values.dtype					,    torch.floataa     )

										__UpperCamelCase :     Union[str, Any]										=  image_processor(_UpperCAmelCase					,    return_tensors="pt"     ).to("cpu"					,    torch.bfloataa     )
										self.assertEqual(encoding.pixel_values.device					,    torch.device("cpu"     )     )
										self.assertEqual(encoding.pixel_values.dtype					,    torch.bfloataa     )

										with self.assertRaises(_UpperCAmelCase     ):
													__UpperCamelCase :     Optional[Any]										=  image_processor(_UpperCAmelCase					,    return_tensors="pt"     ).to(torch.bfloataa					,    "cpu"     )

										# Try with text + image feature
										__UpperCamelCase :     str										=  image_processor(_UpperCAmelCase					,    return_tensors="pt"     )
										encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]     )}     )
										__UpperCamelCase :     int										=  encoding.to(torch.floataa     )

										self.assertEqual(encoding.pixel_values.device					,    torch.device("cpu"     )     )
										self.assertEqual(encoding.pixel_values.dtype					,    torch.floataa     )
										self.assertEqual(encoding.input_ids.dtype					,    torch.long     )









'''simple docstring'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class    A						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				@register_to_config
				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = False					,    )      ->			Tuple:
							super().__init__()

							__UpperCamelCase :     Optional[int]										=  nn.Linear(_UpperCAmelCase					,    _UpperCAmelCase					,    bias=_UpperCAmelCase     )

							__UpperCamelCase :     str										=  nn.Embedding(_UpperCAmelCase					,    _UpperCAmelCase     )
							__UpperCamelCase :     Any										=  False

							__UpperCamelCase :     Union[str, Any]										=  nn.Dropout(p=_UpperCAmelCase     )

							__UpperCamelCase :     Dict										=  TaConfig(
							    d_model=_UpperCAmelCase					,    num_heads=_UpperCAmelCase					,    d_kv=_UpperCAmelCase					,    d_ff=_UpperCAmelCase					,    feed_forward_proj=_UpperCAmelCase					,    dropout_rate=_UpperCAmelCase					,    is_decoder=_UpperCAmelCase					,    is_encoder_decoder=_UpperCAmelCase					,    )
							__UpperCamelCase :     Tuple										=  nn.ModuleList()
							for lyr_num in range(_UpperCAmelCase     ):
										__UpperCamelCase :     List[str]										=  TaBlock(_UpperCAmelCase     )
										self.encoders.append(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  TaLayerNorm(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  nn.Dropout(p=_UpperCAmelCase     )





				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Any										=  self.input_proj(_UpperCAmelCase     )

							# terminal relative positional encodings
							__UpperCamelCase :     Optional[Any]										=  encoder_inputs.shape[1]
							__UpperCamelCase :     Optional[int]										=  torch.arange(_UpperCAmelCase					,    device=encoder_inputs.device     )

							__UpperCamelCase :     Tuple										=  encoder_inputs_mask.sum(-1     )
							__UpperCamelCase :     Optional[Any]										=  torch.roll(input_positions.unsqueeze(0     )					,    tuple(seq_lens.tolist()     )					,    dims=0     )
							x += self.position_encoding(_UpperCAmelCase     )

							__UpperCamelCase :     Any										=  self.dropout_pre(_UpperCAmelCase     )

							# inverted the attention mask
							__UpperCamelCase :     Union[str, Any]										=  encoder_inputs.size()
							__UpperCamelCase :     Optional[Any]										=  self.get_extended_attention_mask(_UpperCAmelCase					,    _UpperCAmelCase     )

							for lyr in self.encoders:
										__UpperCamelCase :     List[Any]										=  lyr(_UpperCAmelCase					,    _UpperCAmelCase     )[0]
							__UpperCamelCase :     Union[str, Any]										=  self.layer_norm(_UpperCAmelCase     )

							return self.dropout_post(_UpperCAmelCase     ), encoder_inputs_mask



