


'''simple docstring'''
from typing import Mapping

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


lowerCAmelCase__  =     logging.get_logger(__name__)







class 					_A						(						UpperCamelCase							):



   '''simple docstring'''






   _lowercase    						= "mt5"
   _lowercase    						= ["past_key_values"]




   def __init__(	self	:					Any					, lowerCamelCase	:					Tuple=250_112					, lowerCamelCase	:					Any=512					, lowerCamelCase	:					Optional[Any]=64					, lowerCamelCase	:					Optional[int]=1_024					, lowerCamelCase	:					int=8					, lowerCamelCase	:					Any=None					, lowerCamelCase	:					List[Any]=6					, lowerCamelCase	:					Tuple=32					, lowerCamelCase	:					Union[str, Any]=128					, lowerCamelCase	:					List[str]=0.1					, lowerCamelCase	:					Dict=1e-6					, lowerCamelCase	:					Any=1.0					, lowerCamelCase	:					List[Any]="gated-gelu"					, lowerCamelCase	:					Any=True					, lowerCamelCase	:					Optional[int]=True					, lowerCamelCase	:					Optional[Any]="T5Tokenizer"					, lowerCamelCase	:					Dict=False					, lowerCamelCase	:					Union[str, Any]=0					, lowerCamelCase	:					str=1					, lowerCamelCase	:					Optional[int]=0					, **lowerCamelCase	:					List[str]					, )->	Dict:
          super().__init__(
              is_encoder_decoder=lowerCamelCase					, tokenizer_class=lowerCamelCase					, tie_word_embeddings=lowerCamelCase					, pad_token_id=lowerCamelCase					, eos_token_id=lowerCamelCase					, decoder_start_token_id=lowerCamelCase					, **lowerCamelCase					, )
          snake_case__					:	Tuple          =					vocab_size
          snake_case__					:	Optional[int]          =					d_model
          snake_case__					:	List[Any]          =					d_kv
          snake_case__					:	int          =					d_ff
          snake_case__					:	Tuple          =					num_layers
          snake_case__					:	Any          =					(
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          snake_case__					:	Union[str, Any]          =					num_heads
          snake_case__					:	List[Any]          =					relative_attention_num_buckets
          snake_case__					:	Tuple          =					relative_attention_max_distance
          snake_case__					:	int          =					dropout_rate
          snake_case__					:	int          =					layer_norm_epsilon
          snake_case__					:	int          =					initializer_factor
          snake_case__					:	Tuple          =					feed_forward_proj
          snake_case__					:	str          =					use_cache

          snake_case__					:	Any          =					self.feed_forward_proj.split("""-"""						)
          snake_case__					:	Dict          =					act_info[-1]
          snake_case__					:	List[Any]          =					act_info[0] == """gated"""

          if len(lowerCamelCase						) > 1 and act_info[0] != "gated" or len(lowerCamelCase						) > 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[Any]          =					"""gelu_new"""




   @property
   def   __lowerCAmelCase						(	self	:					Dict						)->	List[Any]:
          return self.d_model




   @property
   def   __lowerCAmelCase						(	self	:					List[str]						)->	Union[str, Any]:
          return self.num_heads




   @property
   def   __lowerCAmelCase						(	self	:					Optional[Any]						)->	List[str]:
          return self.num_layers







class 					_A						(						UpperCamelCase							):



   '''simple docstring'''






   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def   __lowerCAmelCase						(	self	:					Any						)->	Mapping[str, Mapping[int, str]]:
          snake_case__					:	Union[str, Any]          =					{
              """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
              """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
          }
          if self.use_past:
                 snake_case__					:	Any          =					"""past_encoder_sequence + sequence"""
                 snake_case__					:	Dict          =					{0: """batch"""}
                 snake_case__					:	List[Any]          =					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
          else:
                 snake_case__					:	int          =					{0: """batch""", 1: """decoder_sequence"""}
                 snake_case__					:	Optional[Any]          =					{0: """batch""", 1: """decoder_sequence"""}

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

          return common_inputs




   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def   __lowerCAmelCase						(	self	:					Optional[Any]						)->	int:
          return 13




   @property
   def   __lowerCAmelCase						(	self	:					Tuple						)->	float:
          return 5e-4












'''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 EfficientNetImageProcessor
class  _SCREAMING_SNAKE_CASE				(		unittest.TestCase						):


			'''simple docstring'''





			def __init__( self				:    List[str]			,   lowercase				:    Optional[Any]			,   lowercase				:    int=1_3			,   lowercase				:    List[Any]=3			,   lowercase				:    int=1_8			,   lowercase				:    List[Any]=3_0			,   lowercase				:    Optional[int]=4_0_0			,   lowercase				:    Any=True			,   lowercase				:    int=None			,   lowercase				:    Any=True			,   lowercase				:    Optional[int]=[0.5, 0.5, 0.5]			,   lowercase				:    List[Any]=[0.5, 0.5, 0.5]			,   )				->     List[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			size if size is not None else {"""height""": 1_8, """width""": 1_8}
									UpperCamelCase__       =			parent
									UpperCamelCase__       =			batch_size
									UpperCamelCase__       =			num_channels
									UpperCamelCase__       =			image_size
									UpperCamelCase__       =			min_resolution
									UpperCamelCase__       =			max_resolution
									UpperCamelCase__       =			do_resize
									UpperCamelCase__       =			size
									UpperCamelCase__       =			do_normalize
									UpperCamelCase__       =			image_mean
									UpperCamelCase__       =			image_std







			def 					A   ( self				:    Optional[Any]      )				->     Any:

									'''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  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE							,unittest.TestCase						):


			'''simple docstring'''





			__a				:				List[Any]         =       EfficientNetImageProcessor if is_vision_available() else None






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

									'''simple docstring'''







									UpperCamelCase__       =			EfficientNetImageProcessorTester(self      )






			@property
			def 					A   ( self				:    Optional[int]      )				->     Optional[Any]:

									'''simple docstring'''







									return self.image_processor_tester.prepare_image_processor_dict()






			def 					A   ( self				:    Dict      )				->     str:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(lowercase			,   """image_mean"""      )      )
									self.assertTrue(hasattr(lowercase			,   """image_std"""      )      )
									self.assertTrue(hasattr(lowercase			,   """do_normalize"""      )      )
									self.assertTrue(hasattr(lowercase			,   """do_resize"""      )      )
									self.assertTrue(hasattr(lowercase			,   """size"""      )      )






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

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class.from_dict(self.image_processor_dict      )
									self.assertEqual(image_processor.size			,   {"""height""": 1_8, """width""": 1_8}      )

									UpperCamelCase__       =			self.image_processing_class.from_dict(self.image_processor_dict			,   size=4_2      )
									self.assertEqual(image_processor.size			,   {"""height""": 4_2, """width""": 4_2}      )






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

									'''simple docstring'''







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

									# Test not batched input
									UpperCamelCase__       =			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
									UpperCamelCase__       =			image_processing(lowercase			,   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 					A   ( self				:    Dict      )				->     Optional[int]:

									'''simple docstring'''







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

									# Test not batched input
									UpperCamelCase__       =			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
									UpperCamelCase__       =			image_processing(lowercase			,   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 					A   ( self				:    Optional[int]      )				->     List[str]:

									'''simple docstring'''







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

									# Test not batched input
									UpperCamelCase__       =			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
									UpperCamelCase__       =			image_processing(lowercase			,   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"""],
									    )			,   )
