



from typing import Mapping

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


lowercase_								=     logging.get_logger(__name__)

lowercase_								=     {
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}
class 				_snake_case			(					lowercase__):
    UpperCamelCase__				:					Tuple     		="""umt5"""
    UpperCamelCase__				:					Optional[int]     		=["""past_key_values"""]




    def __init__(						self					:						Dict,					__lowercase					:						str=25_0112,					__lowercase					:						Tuple=512,					__lowercase					:						str=64,					__lowercase					:						List[Any]=1024,					__lowercase					:						Dict=8,					__lowercase					:						List[str]=None,					__lowercase					:						List[str]=6,					__lowercase					:						Dict=32,					__lowercase					:						Optional[int]=128,					__lowercase					:						int=0.1,					__lowercase					:						Dict=1e-6,					__lowercase					:						List[str]=1.0,					__lowercase					:						int="gated-gelu",					__lowercase					:						List[str]=True,					__lowercase					:						Optional[int]=True,					__lowercase					:						Optional[int]="T5Tokenizer",					__lowercase					:						List[str]=True,					__lowercase					:						Any=0,					__lowercase					:						int=1,					__lowercase					:						List[Any]=0,					**__lowercase					:						List[Any],					):
       super().__init__(
           is_encoder_decoder=__lowercase,					tokenizer_class=__lowercase,					tie_word_embeddings=__lowercase,					pad_token_id=__lowercase,					eos_token_id=__lowercase,					decoder_start_token_id=__lowercase,					**__lowercase,					)
       lowercase__					  =				vocab_size
       lowercase__					  =				d_model
       lowercase__					  =				d_kv
       lowercase__					  =				d_ff
       lowercase__					  =				num_layers
       lowercase__					  =				(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       lowercase__					  =				num_heads
       lowercase__					  =				relative_attention_num_buckets
       lowercase__					  =				relative_attention_max_distance
       lowercase__					  =				dropout_rate
       lowercase__					  =				layer_norm_epsilon
       lowercase__					  =				initializer_factor
       lowercase__					  =				feed_forward_proj
       lowercase__					  =				use_cache

       lowercase__					  =				self.feed_forward_proj.split("-"						)
       lowercase__					  =				act_info[-1]
       lowercase__					  =				act_info[0] == "gated"

       if len(__lowercase						) > 1 and act_info[0] != "gated" or len(__lowercase						) > 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'"						)

       if feed_forward_proj == "gated-gelu":
          lowercase__					  =				"gelu_new"




    @property
    def 							A__      (						self					:						int						):
       return self.d_model




    @property
    def 							A__      (						self					:						int						):
       return self.num_heads




    @property
    def 							A__      (						self					:						str						):
       return self.num_layers
class 				_snake_case			(					lowercase__):




    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def 							A__      (						self					:						Dict						):
       lowercase__					  =				{
           "input_ids": {0: "batch", 1: "encoder_sequence"},
           "attention_mask": {0: "batch", 1: "encoder_sequence"},
       }
       if self.use_past:
          lowercase__					  =				"past_encoder_sequence + sequence"
          lowercase__					  =				{0: "batch"}
          lowercase__					  =				{0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
          lowercase__					  =				{0: "batch", 1: "decoder_sequence"}
          lowercase__					  =				{0: "batch", 1: "decoder_sequence"}

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

       return common_inputs




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




    @property
    def 							A__      (						self					:						Optional[int]						):
       return 5e-4









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class 				_snake_case			(					unittest.TestCase):




				def __init__(						self					:						Dict,					__lowercase					:						int,					__lowercase					:						Union[str, Any]=7,					__lowercase					:						Union[str, Any]=3,					__lowercase					:						Any=18,					__lowercase					:						Union[str, Any]=30,					__lowercase					:						Any=400,					__lowercase					:						List[str]=True,					__lowercase					:						Dict=None,					__lowercase					:						List[str]=True,					__lowercase					:						int=False,					__lowercase					:						Union[str, Any]=True,					__lowercase					:						str=True,					__lowercase					:						Optional[int]=[0.5, 0.5, 0.5],					__lowercase					:						List[Any]=[0.5, 0.5, 0.5],					):
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				num_channels
							lowercase__					  =				image_size
							lowercase__					  =				min_resolution
							lowercase__					  =				max_resolution
							lowercase__					  =				do_resize
							lowercase__					  =				size if size is not None else {"height": 18, "width": 20}
							lowercase__					  =				do_thumbnail
							lowercase__					  =				do_align_axis
							lowercase__					  =				do_pad
							lowercase__					  =				do_normalize
							lowercase__					  =				image_mean
							lowercase__					  =				image_std




				def 							A__      (						self					:						Optional[Any]						):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_thumbnail": self.do_thumbnail,
							    "do_align_long_axis": self.do_align_axis,
							    "do_pad": self.do_pad,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}
@require_torch
@require_vision
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Optional[int]     		=DonutImageProcessor if is_vision_available() else None




				def 							A__      (						self					:						str						):
							lowercase__					  =				DonutImageProcessingTester(self						)




				@property
				def 							A__      (						self					:						List[str]						):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__lowercase,					"do_resize"						)						)
							self.assertTrue(hasattr(__lowercase,					"size"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_thumbnail"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_align_long_axis"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_pad"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_normalize"						)						)
							self.assertTrue(hasattr(__lowercase,					"image_mean"						)						)
							self.assertTrue(hasattr(__lowercase,					"image_std"						)						)




				def 							A__      (						self					:						str						):
							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size,					{"height": 18, "width": 20}						)

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

							# Previous config had dimensions in (width, height) order
							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict,					size=(42, 84)						)
							self.assertEqual(image_processor.size,					{"height": 84, "width": 42}						)




				def 							A__      (						self					:						List[str]						):
							pass




				@is_flaky()
				def 							A__      (						self					:						Dict						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					Image.Image						)

							# Test not batched input
							lowercase__					  =				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
							lowercase__					  =				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"],
							    ),					)




				@is_flaky()
				def 							A__      (						self					:						Optional[Any]						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							lowercase__					  =				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
							lowercase__					  =				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
							lowercase__					  =				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"],
							    ),					)




				@is_flaky()
				def 							A__      (						self					:						Tuple						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							lowercase__					  =				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
							lowercase__					  =				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
							lowercase__					  =				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"],
							    ),					)




