



"""simple docstring"""
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 ViTImageProcessor
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					def __init__(	self:							int						,					snake_case:							List[str]						,					snake_case:							List[str]=7						,					snake_case:							Union[str, Any]=3						,					snake_case:							List[str]=18						,					snake_case:							Any=30						,					snake_case:							Any=400						,					snake_case:							Any=True						,					snake_case:							List[str]=None						,					snake_case:							Union[str, Any]=True						,					snake_case:							int=[0.5, 0.5, 0.5]						,					snake_case:							Tuple=[0.5, 0.5, 0.5]						,					)       ->     int:
											snake_case_			:Dict						=      size if size is not None else {"""height""": 18, """width""": 18}
											snake_case_			:Optional[Any]						=      parent
											snake_case_			:Tuple						=      batch_size
											snake_case_			:Tuple						=      num_channels
											snake_case_			:Optional[int]						=      image_size
											snake_case_			:Tuple						=      min_resolution
											snake_case_			:str						=      max_resolution
											snake_case_			:Any						=      do_resize
											snake_case_			:Union[str, Any]						=      size
											snake_case_			:Tuple						=      do_normalize
											snake_case_			:int						=      image_mean
											snake_case_			:List[Any]						=      image_std






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[Any]:
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											}

@require_torch
@require_vision
class     lowerCamelCase  (      _lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					ViTImageProcessor if is_vision_available() else None






					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[Any]:
											snake_case_			:Dict						=      ViTImageProcessingTester(self						)






					@property
					def        lowerCAmelCase_    (	self:							Tuple						)       ->     Optional[Any]:
											return self.image_processor_tester.prepare_image_processor_dict()






					def        lowerCAmelCase_    (	self:							str						)       ->     List[str]:
											snake_case_			:List[str]						=      self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(snake_case						,					"""image_mean"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""image_std"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""do_normalize"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""do_resize"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""size"""						)						)






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size						,					{"""height""": 18, """width""": 18}						)

											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						)
											self.assertEqual(image_processor.size						,					{"""height""": 42, """width""": 42}						)






					def        lowerCAmelCase_    (	self:							Tuple						)       ->     int:
											pass






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											# Initialize image_processing
											snake_case_			:Any						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											snake_case_			:str						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					Image.Image						)

											# Test not batched input
											snake_case_			:List[str]						=      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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)

											# Test batched
											snake_case_			:Dict						=      image_processing(snake_case						,					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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)






					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Tuple:
											# Initialize image_processing
											snake_case_			:int						=      self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											snake_case_			:str						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						,					numpify=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					np.ndarray						)

											# Test not batched input
											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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)

											# Test batched
											snake_case_			:Any						=      image_processing(snake_case						,					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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Optional[int]:
											# Initialize image_processing
											snake_case_			:Union[str, Any]						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											snake_case_			:List[Any]						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						,					torchify=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					torch.Tensor						)

											# Test not batched input
											snake_case_			:Optional[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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)

											# Test batched
											snake_case_			:List[Any]						=      image_processing(snake_case						,					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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)





"""simple docstring"""
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__a     =    logging.get_logger(__name__)

__a     =    {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      List[Any]						=					"""longt5"""
					_A		:      Dict						=					["""past_key_values"""]
					_A		:      int						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






					def __init__(	self:							str						,					snake_case:							List[Any]=32_128						,					snake_case:							Optional[int]=512						,					snake_case:							Union[str, Any]=64						,					snake_case:							str=2_048						,					snake_case:							Any=6						,					snake_case:							Optional[Any]=None						,					snake_case:							str=8						,					snake_case:							Tuple=127						,					snake_case:							Any=16						,					snake_case:							int=32						,					snake_case:							int=128						,					snake_case:							int=0.1						,					snake_case:							Optional[int]=1E-6						,					snake_case:							List[Any]=1.0						,					snake_case:							Optional[int]="relu"						,					snake_case:							str=True						,					snake_case:							Optional[int]="local"						,					snake_case:							Any=True						,					snake_case:							Tuple=0						,					snake_case:							Any=1						,					**snake_case:							Any						,					)       ->     List[str]:
											snake_case_			:Any						=      vocab_size
											snake_case_			:List[Any]						=      d_model
											snake_case_			:Any						=      d_kv
											snake_case_			:int						=      d_ff
											snake_case_			:Tuple						=      num_layers
											# default = symmetry
											snake_case_			:str						=      num_decoder_layers if num_decoder_layers is not None else self.num_layers
											snake_case_			:Any						=      num_heads
											snake_case_			:str						=      local_radius
											snake_case_			:Optional[int]						=      global_block_size
											snake_case_			:Union[str, Any]						=      relative_attention_num_buckets
											snake_case_			:List[str]						=      relative_attention_max_distance
											snake_case_			:Dict						=      dropout_rate
											snake_case_			:str						=      layer_norm_epsilon
											snake_case_			:str						=      initializer_factor
											snake_case_			:Any						=      feed_forward_proj
											snake_case_			:List[str]						=      encoder_attention_type
											snake_case_			:List[str]						=      use_cache

											snake_case_			:Tuple						=      self.feed_forward_proj.split("""-"""						)
											snake_case_			:Any						=      act_info[-1]
											snake_case_			:Union[str, Any]						=      act_info[0] == """gated"""

											if len(snake_case						) > 1 and act_info[0] != "gated" or len(snake_case						) > 2:
																	raise ValueError(
																	    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																	    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
																	    """'gated-gelu' or 'relu'"""						)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																	snake_case_			:Optional[int]						=      """gelu_new"""

											super().__init__(
											    pad_token_id=snake_case						,					eos_token_id=snake_case						,					is_encoder_decoder=snake_case						,					**snake_case						,					)

class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					@property
					def        lowerCAmelCase_    (	self:							Dict						)       ->     Mapping[str, Mapping[int, str]]:
											snake_case_			:Dict						=      {
											    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
											    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
											}
											if self.use_past:
																	snake_case_			:List[Any]						=      """past_encoder_sequence + sequence"""
																	snake_case_			:List[Any]						=      {0: """batch"""}
																	snake_case_			:List[Any]						=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
																	snake_case_			:List[str]						=      {0: """batch""", 1: """decoder_sequence"""}
																	snake_case_			:List[Any]						=      {0: """batch""", 1: """decoder_sequence"""}

											if self.use_past:
																	self.fill_with_past_key_values_(snake_case						,					direction="""inputs"""						)

											return common_inputs






					@property
					def        lowerCAmelCase_    (	self:							List[str]						)       ->     int:
											return 13
