



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 GLPNImageProcessor



class 			__lowerCAmelCase  (						unittest.TestCase ):




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase=7						,					__UpperCAmelCase=3						,					__UpperCAmelCase=18						,					__UpperCAmelCase=30						,					__UpperCAmelCase=400						,					__UpperCAmelCase=True						,					__UpperCAmelCase=32						,					__UpperCAmelCase=True						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     parent
    __lowerCamelCase    				=     batch_size
    __lowerCamelCase    				=     num_channels
    __lowerCamelCase    				=     image_size
    __lowerCamelCase    				=     min_resolution
    __lowerCamelCase    				=     max_resolution
    __lowerCamelCase    				=     do_resize
    __lowerCamelCase    				=     size_divisor
    __lowerCamelCase    				=     do_rescale




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return {
        "do_resize": self.do_resize,
        "size_divisor": self.size_divisor,
        "do_rescale": self.do_rescale,
    }



@require_torch
@require_vision
class 			__lowerCAmelCase  (						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	GLPNImageProcessor if is_vision_available() else None




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     GLPNImageProcessingTester(self					)




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return self.image_processor_tester.prepare_image_processor_dict()




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(__UpperCAmelCase						,					'''do_resize'''					)					)
    self.assertTrue(hasattr(__UpperCAmelCase						,					'''size_divisor'''					)					)
    self.assertTrue(hasattr(__UpperCAmelCase						,					'''resample'''					)					)
    self.assertTrue(hasattr(__UpperCAmelCase						,					'''do_rescale'''					)					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    pass




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Initialize image_processing
    __lowerCamelCase    				=     self.image_processing_class(**self.image_processor_dict					)
    # create random PIL images
    __lowerCamelCase    				=     prepare_image_inputs(self.image_processor_tester						,					equal_resolution=__UpperCAmelCase					)
    for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase						,					Image.Image					)

    # Test not batched input (GLPNImageProcessor doesn't support batching)
    __lowerCamelCase    				=     image_processing(image_inputs[0]						,					return_tensors='''pt'''					).pixel_values
    self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0					)
    self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Initialize image_processing
    __lowerCamelCase    				=     self.image_processing_class(**self.image_processor_dict					)
    # create random numpy tensors
    __lowerCamelCase    				=     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 (GLPNImageProcessor doesn't support batching)
    __lowerCamelCase    				=     image_processing(image_inputs[0]						,					return_tensors='''pt'''					).pixel_values
    self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0					)
    self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Initialize image_processing
    __lowerCamelCase    				=     self.image_processing_class(**self.image_processor_dict					)
    # create random PyTorch tensors
    __lowerCamelCase    				=     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 (GLPNImageProcessor doesn't support batching)
    __lowerCamelCase    				=     image_processing(image_inputs[0]						,					return_tensors='''pt'''					).pixel_values
    self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0					)
    self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0					)











from typing import Mapping

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


a_								=       logging.get_logger(__name__)

a_								=       {
    """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 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""t5"""
 lowerCAmelCase__							       =	["""past_key_values"""]
 lowerCAmelCase__							       =	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




 def __init__(    self						,					__UpperCAmelCase=32128						,					__UpperCAmelCase=512						,					__UpperCAmelCase=64						,					__UpperCAmelCase=2048						,					__UpperCAmelCase=6						,					__UpperCAmelCase=None						,					__UpperCAmelCase=8						,					__UpperCAmelCase=32						,					__UpperCAmelCase=128						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=1E-6						,					__UpperCAmelCase=1.0						,					__UpperCAmelCase="relu"						,					__UpperCAmelCase=True						,					__UpperCAmelCase=True						,					__UpperCAmelCase=0						,					__UpperCAmelCase=1						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     vocab_size
    __lowerCamelCase    				=     d_model
    __lowerCamelCase    				=     d_kv
    __lowerCamelCase    				=     d_ff
    __lowerCamelCase    				=     num_layers
    __lowerCamelCase    				=     (
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    __lowerCamelCase    				=     num_heads
    __lowerCamelCase    				=     relative_attention_num_buckets
    __lowerCamelCase    				=     relative_attention_max_distance
    __lowerCamelCase    				=     dropout_rate
    __lowerCamelCase    				=     layer_norm_epsilon
    __lowerCamelCase    				=     initializer_factor
    __lowerCamelCase    				=     feed_forward_proj
    __lowerCamelCase    				=     use_cache

    __lowerCamelCase    				=     self.feed_forward_proj.split('''-'''					)
    __lowerCamelCase    				=     act_info[-1]
    __lowerCamelCase    				=     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\''''					)

    # for backwards compatibility
    if feed_forward_proj == "gated-gelu":
       __lowerCamelCase    				=     '''gelu_new'''

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



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     {
        '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
        '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
    }
    if self.use_past:
       __lowerCamelCase    				=     '''past_encoder_sequence + sequence'''
       __lowerCamelCase    				=     {0: '''batch'''}
       __lowerCamelCase    				=     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    else:
       __lowerCamelCase    				=     {0: '''batch''', 1: '''decoder_sequence'''}
       __lowerCamelCase    				=     {0: '''batch''', 1: '''decoder_sequence'''}

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

    return common_inputs




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return 13






