


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 PoolFormerImageProcessor

class 						__a (							unittest.TestCase     ):
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=7     ,       _SCREAMING_SNAKE_CASE=3     ,       _SCREAMING_SNAKE_CASE=30     ,       _SCREAMING_SNAKE_CASE=400     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=0.9     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,       _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,       )					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     size if size is not None else {"shortest_edge": 30}
									_UpperCAmelCase											=     crop_size if crop_size is not None else {"height": 30, "width": 30}
									_UpperCAmelCase											=     parent
									_UpperCAmelCase											=     batch_size
									_UpperCAmelCase											=     num_channels
									_UpperCAmelCase											=     min_resolution
									_UpperCAmelCase											=     max_resolution
									_UpperCAmelCase											=     do_resize_and_center_crop
									_UpperCAmelCase											=     size
									_UpperCAmelCase											=     crop_pct
									_UpperCAmelCase											=     crop_size
									_UpperCAmelCase											=     do_normalize
									_UpperCAmelCase											=     image_mean
									_UpperCAmelCase											=     image_std






		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									return {
									    "size": self.size,
									    "do_resize_and_center_crop": self.do_resize_and_center_crop,
									    "crop_pct": self.crop_pct,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}



@require_torch
@require_vision
class 						__a (							snake_case_       , unittest.TestCase     ):
		_a      :    Optional[int]           =      PoolFormerImageProcessor if is_vision_available() else None
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     PoolFormerImageProcessingTester(self		)
		@property
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(UpperCamelCase__     ,       'do_resize_and_center_crop'		)		)
									self.assertTrue(hasattr(UpperCamelCase__     ,       'size'		)		)
									self.assertTrue(hasattr(UpperCamelCase__     ,       'crop_pct'		)		)
									self.assertTrue(hasattr(UpperCamelCase__     ,       'do_normalize'		)		)
									self.assertTrue(hasattr(UpperCamelCase__     ,       'image_mean'		)		)
									self.assertTrue(hasattr(UpperCamelCase__     ,       'image_std'		)		)
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     self.image_processing_class.from_dict(self.image_processor_dict		)
									self.assertEqual(image_processor.size     ,       {'shortest_edge': 30}		)
									self.assertEqual(image_processor.crop_size     ,       {'height': 30, 'width': 30}		)

									_UpperCAmelCase											=     self.image_processing_class.from_dict(self.image_processor_dict     ,       size=42     ,       crop_size=84		)
									self.assertEqual(image_processor.size     ,       {'shortest_edge': 42}		)
									self.assertEqual(image_processor.crop_size     ,       {'height': 84, 'width': 84}		)
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									pass
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""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=UpperCamelCase__		)
									for image in image_inputs:
																self.assertIsInstance(UpperCamelCase__     ,       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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,       )

									# Test batched
									_UpperCAmelCase											=     image_processing(UpperCamelCase__     ,       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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,       )
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""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=UpperCamelCase__     ,       numpify=UpperCamelCase__		)
									for image in image_inputs:
																self.assertIsInstance(UpperCamelCase__     ,       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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,       )

									# Test batched
									_UpperCAmelCase											=     image_processing(UpperCamelCase__     ,       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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,       )






		def       UpperCAmelCase__							(     self		)					->			Any:




									"""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=UpperCamelCase__     ,       torchify=UpperCamelCase__		)
									for image in image_inputs:
																self.assertIsInstance(UpperCamelCase__     ,       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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,       )

									# Test batched
									_UpperCAmelCase											=     image_processing(UpperCamelCase__     ,       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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )     ,       )




"""simple docstring"""



from typing import Mapping

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


__snake_case       =       logging.get_logger(__name__)

__snake_case       =       {
    """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 _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict				    =    '''umt5'''
   __UpperCAmelCase					:			str				    =    ['''past_key_values''']







   def __init__(      self		,      UpperCamelCase__=25_0112		,      UpperCamelCase__=512		,      UpperCamelCase__=64		,      UpperCamelCase__=1024		,      UpperCamelCase__=8		,      UpperCamelCase__=None		,      UpperCamelCase__=6		,      UpperCamelCase__=32		,      UpperCamelCase__=128		,      UpperCamelCase__=0.1		,      UpperCamelCase__=1e-6		,      UpperCamelCase__=1.0		,      UpperCamelCase__="gated-gelu"		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__="T5Tokenizer"		,      UpperCamelCase__=True		,      UpperCamelCase__=0		,      UpperCamelCase__=1		,      UpperCamelCase__=0		,      **UpperCamelCase__		,      )      ->						Dict:

       '''simple docstring'''




       super().__init__(
           is_encoder_decoder=UpperCamelCase__		,      tokenizer_class=UpperCamelCase__		,      tie_word_embeddings=UpperCamelCase__		,      pad_token_id=UpperCamelCase__		,      eos_token_id=UpperCamelCase__		,      decoder_start_token_id=UpperCamelCase__		,      **UpperCamelCase__		,      )
       snake_case       : Optional[int]    =       vocab_size
       snake_case       : Any    =       d_model
       snake_case       : Optional[Any]    =       d_kv
       snake_case       : Any    =       d_ff
       snake_case       : Optional[Any]    =       num_layers
       snake_case       : Optional[Any]    =       (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       snake_case       : Optional[Any]    =       num_heads
       snake_case       : Optional[Any]    =       relative_attention_num_buckets
       snake_case       : Dict    =       relative_attention_max_distance
       snake_case       : Tuple    =       dropout_rate
       snake_case       : List[Any]    =       layer_norm_epsilon
       snake_case       : Any    =       initializer_factor
       snake_case       : Optional[Any]    =       feed_forward_proj
       snake_case       : Tuple    =       use_cache

       snake_case       : Any    =       self.feed_forward_proj.split("-"		)
       snake_case       : Tuple    =       act_info[-1]
       snake_case       : Optional[int]    =       act_info[0] == "gated"

       if len(UpperCamelCase__		) > 1 and act_info[0] != "gated" or len(UpperCamelCase__		) > 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":
           snake_case       : List[str]    =       "gelu_new"







   @property
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       return self.d_model







   @property
   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       return self.num_heads







   @property
   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       return self.num_layers







class _lowerCAmelCase							( snake_case_      ):







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




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

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

       return common_inputs







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       return 13







   @property
   def 					lowerCamelCase						(      self		)      ->						float:

       '''simple docstring'''




       return 5e-4





