


from typing import Mapping

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


UpperCAmelCase_ :					str       =			logging.get_logger(__name__)

UpperCAmelCase_ :					List[str]       =			{
    '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 					SCREAMING_SNAKE_CASE__    (							lowercase__     ):
      snake_case__							:	Tuple	  =		'umt5'
      snake_case__							:	Optional[int]	  =		['past_key_values']
      def __init__(			self							:			Optional[int]	,				SCREAMING_SNAKE_CASE__							:			List[str]=2_5_0_1_1_2	,				SCREAMING_SNAKE_CASE__							:			Optional[Any]=5_1_2	,				SCREAMING_SNAKE_CASE__							:			Optional[Any]=6_4	,				SCREAMING_SNAKE_CASE__							:			Optional[int]=1_0_2_4	,				SCREAMING_SNAKE_CASE__							:			int=8	,				SCREAMING_SNAKE_CASE__							:			Any=None	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]=6	,				SCREAMING_SNAKE_CASE__							:			str=3_2	,				SCREAMING_SNAKE_CASE__							:			Dict=1_2_8	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]=0.1	,				SCREAMING_SNAKE_CASE__							:			Tuple=1E-6	,				SCREAMING_SNAKE_CASE__							:			Optional[Any]=1.0	,				SCREAMING_SNAKE_CASE__							:			Dict="gated-gelu"	,				SCREAMING_SNAKE_CASE__							:			str=True	,				SCREAMING_SNAKE_CASE__							:			Optional[int]=True	,				SCREAMING_SNAKE_CASE__							:			Any="T5Tokenizer"	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]=True	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]=0	,				SCREAMING_SNAKE_CASE__							:			List[Any]=1	,				SCREAMING_SNAKE_CASE__							:			int=0	,				**SCREAMING_SNAKE_CASE__							:			List[Any]	,				)						->     Any:
             super().__init__(
                 is_encoder_decoder=SCREAMING_SNAKE_CASE__	,				tokenizer_class=SCREAMING_SNAKE_CASE__	,				tie_word_embeddings=SCREAMING_SNAKE_CASE__	,				pad_token_id=SCREAMING_SNAKE_CASE__	,				eos_token_id=SCREAMING_SNAKE_CASE__	,				decoder_start_token_id=SCREAMING_SNAKE_CASE__	,				**SCREAMING_SNAKE_CASE__	,				)
             a_   :						Optional[Any]				=    vocab_size
             a_   :						List[Any]				=    d_model
             a_   :						Any				=    d_kv
             a_   :						List[Any]				=    d_ff
             a_   :						str				=    num_layers
             a_   :						Optional[Any]				=    (
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             a_   :						Optional[int]				=    num_heads
             a_   :						Union[str, Any]				=    relative_attention_num_buckets
             a_   :						Dict				=    relative_attention_max_distance
             a_   :						List[Any]				=    dropout_rate
             a_   :						Any				=    layer_norm_epsilon
             a_   :						List[str]				=    initializer_factor
             a_   :						Any				=    feed_forward_proj
             a_   :						Optional[int]				=    use_cache

             a_   :						Union[str, Any]				=    self.feed_forward_proj.split('-'	)
             a_   :						Any				=    act_info[-1]
             a_   :						Union[str, Any]				=    act_info[0] == 'gated'

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

             if feed_forward_proj == "gated-gelu":
                    a_   :						Optional[int]				=    'gelu_new'
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			str	)						->     Any:
             return self.d_model
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     Tuple:
             return self.num_heads

      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			Union[str, Any]	)						->     List[Any]:
             return self.num_layers
class 					SCREAMING_SNAKE_CASE__    (							lowercase__     ):
      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
      def 			SCREAMING_SNAKE_CASE				(			self							:			Union[str, Any]	)						->     Mapping[str, Mapping[int, str]]:
             a_   :						Optional[int]				=    {
                 'input_ids': {0: 'batch', 1: 'encoder_sequence'},
                 'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
             }
             if self.use_past:
                    a_   :						str				=    'past_encoder_sequence + sequence'
                    a_   :						List[Any]				=    {0: 'batch'}
                    a_   :						Optional[int]				=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
             else:
                    a_   :						str				=    {0: 'batch', 1: 'decoder_sequence'}
                    a_   :						Union[str, Any]				=    {0: 'batch', 1: 'decoder_sequence'}

             if self.use_past:
                    self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__	,				direction='inputs'	)

             return common_inputs
      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     int:
             return 1_3

      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			Dict	)						->     float:
             return 5E-4







'''simple docstring'''





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    min_resolution
      _a           =    max_resolution
      _a           =    do_resize
      _a           =    size
      _a           =    do_normalize
      _a           =    image_mean
      _a           =    image_std



 def    a__    (self			)       ->	Union[str, 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 __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



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


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

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



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


      """simple docstring"""



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

      # Test not batched input
      _a           =    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
      _a           =    image_processing(A					,  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			)       ->	str:


      """simple docstring"""



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

      # Test not batched input
      _a           =    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
      _a           =    image_processing(A					,  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]:


      """simple docstring"""



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

      # Test not batched input
      _a           =    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
      _a           =    image_processing(A					,  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'''],
          )					,  )



