




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 		__A      (			unittest.TestCase			):

						def __init__(		self   :List[Any]    , __snake_case   :Dict    , __snake_case   :List[str]=13    , __snake_case   :str=3    , __snake_case   :int=2_24    , __snake_case   :int=30    , __snake_case   :Any=4_00    , __snake_case   :Dict=True    , __snake_case   :Any=None    , __snake_case   :Optional[int]=True    , __snake_case   :Optional[Any]=[0.5, 0.5, 0.5]    , __snake_case   :Optional[int]=[0.5, 0.5, 0.5]    , ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=size if size is not None else {"""height""": 18, """width""": 18}
										__magic_name__	:	List[Any]								=parent
										__magic_name__	:	Optional[int]								=batch_size
										__magic_name__	:	int								=num_channels
										__magic_name__	:	str								=image_size
										__magic_name__	:	Union[str, Any]								=min_resolution
										__magic_name__	:	Union[str, Any]								=max_resolution
										__magic_name__	:	int								=do_resize
										__magic_name__	:	int								=size
										__magic_name__	:	Tuple								=do_normalize
										__magic_name__	:	Tuple								=image_mean
										__magic_name__	:	Optional[Any]								=image_std


						def     A__						(		self   :int ):
										'''simple docstring'''

										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 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					ViTImageProcessor if is_vision_available() else None

						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=EfficientFormerImageProcessorTester(self )

						@property
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										return self.image_proc_tester.prepare_image_processor_dict()

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								=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     A__						(		self   :Tuple ):
										'''simple docstring'''

										pass

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										__magic_name__	:	Tuple								=prepare_image_inputs(self.image_proc_tester    , equal_resolution=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , Image.Image )

										# Test not batched input
										__magic_name__	:	int								=image_processor(image_inputs[0]    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )    , )

										# Test batched
										__magic_name__	:	Optional[int]								=image_processor(__snake_case    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )    , )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Dict								=self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										__magic_name__	:	str								=prepare_image_inputs(self.image_proc_tester    , equal_resolution=__snake_case    , numpify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , np.ndarray )

										# Test not batched input
										__magic_name__	:	Tuple								=image_processor(image_inputs[0]    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )    , )

										# Test batched
										__magic_name__	:	List[Any]								=image_processor(__snake_case    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )    , )


						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	int								=self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										__magic_name__	:	Any								=prepare_image_inputs(self.image_proc_tester    , equal_resolution=__snake_case    , torchify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , torch.Tensor )

										# Test not batched input
										__magic_name__	:	Any								=image_processor(image_inputs[0]    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )    , )

										# Test batched
										__magic_name__	:	Tuple								=image_processor(__snake_case    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )    , )










from typing import Mapping

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


UpperCAmelCase_		:				str									=     logging.get_logger(__name__)

UpperCAmelCase_		:				Tuple									=     {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""t5"""
						UpperCamelCase     						=					["""past_key_values"""]
						UpperCamelCase     						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

						def __init__(		self   :Optional[Any]    , __snake_case   :Optional[Any]=3_21_28    , __snake_case   :Optional[int]=5_12    , __snake_case   :str=64    , __snake_case   :int=20_48    , __snake_case   :Union[str, Any]=6    , __snake_case   :Union[str, Any]=None    , __snake_case   :Union[str, Any]=8    , __snake_case   :List[str]=32    , __snake_case   :Dict=1_28    , __snake_case   :List[Any]=0.1    , __snake_case   :int=1E-6    , __snake_case   :List[str]=1.0    , __snake_case   :Optional[int]="relu"    , __snake_case   :List[str]=True    , __snake_case   :Union[str, Any]=True    , __snake_case   :Union[str, Any]=0    , __snake_case   :str=1    , **__snake_case   :int    , ):
										'''simple docstring'''

										__magic_name__	:	str								=vocab_size
										__magic_name__	:	str								=d_model
										__magic_name__	:	Optional[Any]								=d_kv
										__magic_name__	:	int								=d_ff
										__magic_name__	:	Union[str, Any]								=num_layers
										__magic_name__	:	Tuple								=(
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										__magic_name__	:	List[str]								=num_heads
										__magic_name__	:	Optional[Any]								=relative_attention_num_buckets
										__magic_name__	:	Union[str, Any]								=relative_attention_max_distance
										__magic_name__	:	Dict								=dropout_rate
										__magic_name__	:	Dict								=layer_norm_epsilon
										__magic_name__	:	List[str]								=initializer_factor
										__magic_name__	:	Any								=feed_forward_proj
										__magic_name__	:	Optional[int]								=use_cache

										__magic_name__	:	Tuple								=self.feed_forward_proj.split("""-""" )
										__magic_name__	:	int								=act_info[-1]
										__magic_name__	:	int								=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":
														__magic_name__	:	str								="""gelu_new"""

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




class 		__A      (			UpperCamelCase__			):

						@property
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								={
										    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
										    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
										}
										if self.use_past:
														__magic_name__	:	List[str]								="""past_encoder_sequence + sequence"""
														__magic_name__	:	List[Any]								={0: """batch"""}
														__magic_name__	:	Union[str, Any]								={0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
														__magic_name__	:	int								={0: """batch""", 1: """decoder_sequence"""}
														__magic_name__	:	Tuple								={0: """batch""", 1: """decoder_sequence"""}

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

										return common_inputs


						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										return 13




