



import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput







@dataclass
class       _snake_case				(     lowerCamelCase_ ):






   '''simple docstring'''






   A__      : List[str]       =						42







class       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






   def       A__    (    self:     Optional[Any]							)	->      Optional[Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					CustomOutput(images=np.random.rand(1  ,3  ,4  ,4							)							)

       # check every way of getting the attribute
       assert isinstance(outputs.images  ,np.ndarray							)
       assert outputs.images.shape == (1, 3, 4, 4)
       assert isinstance(outputs["""images"""]  ,np.ndarray							)
       assert outputs["images"].shape == (1, 3, 4, 4)
       assert isinstance(outputs[0]  ,np.ndarray							)
       assert outputs[0].shape == (1, 3, 4, 4)

       # test with a non-tensor attribute
       UpperCAmelCase_     :       List[Any]  		=					CustomOutput(images=[PIL.Image.new("""RGB"""  ,(4, 4)							)]							)

       # check every way of getting the attribute
       assert isinstance(outputs.images  ,lowerCAmelCase_							)
       assert isinstance(outputs.images[0]  ,PIL.Image.Image							)
       assert isinstance(outputs["""images"""]  ,lowerCAmelCase_							)
       assert isinstance(outputs["""images"""][0]  ,PIL.Image.Image							)
       assert isinstance(outputs[0]  ,lowerCAmelCase_							)
       assert isinstance(outputs[0][0]  ,PIL.Image.Image							)






   def       A__    (    self:     List[str]							)	->      Tuple:
       # test output reinitialization with a `dict` for compatibility with `accelerate`
       UpperCAmelCase_     :       List[Any]  		=					CustomOutput({"""images""": np.random.rand(1  ,3  ,4  ,4							)}							)

       # check every way of getting the attribute
       assert isinstance(outputs.images  ,np.ndarray							)
       assert outputs.images.shape == (1, 3, 4, 4)
       assert isinstance(outputs["""images"""]  ,np.ndarray							)
       assert outputs["images"].shape == (1, 3, 4, 4)
       assert isinstance(outputs[0]  ,np.ndarray							)
       assert outputs[0].shape == (1, 3, 4, 4)

       # test with a non-tensor attribute
       UpperCAmelCase_     :       int  		=					CustomOutput({"""images""": [PIL.Image.new("""RGB"""  ,(4, 4)							)]}							)

       # check every way of getting the attribute
       assert isinstance(outputs.images  ,lowerCAmelCase_							)
       assert isinstance(outputs.images[0]  ,PIL.Image.Image							)
       assert isinstance(outputs["""images"""]  ,lowerCAmelCase_							)
       assert isinstance(outputs["""images"""][0]  ,PIL.Image.Image							)
       assert isinstance(outputs[0]  ,lowerCAmelCase_							)
       assert isinstance(outputs[0][0]  ,PIL.Image.Image							)

from typing import Mapping

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


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'longt5'
	__SCREAMING_SNAKE_CASE              =					['past_key_values']
	__SCREAMING_SNAKE_CASE              =					{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


	def __init__(   self       ,  lowerCAmelCase_=32128       ,  lowerCAmelCase_=512       ,  lowerCAmelCase_=64       ,  lowerCAmelCase_=2048       ,  lowerCAmelCase_=6       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=8       ,  lowerCAmelCase_=127       ,  lowerCAmelCase_=16       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=128       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=1e-6       ,  lowerCAmelCase_=1.0       ,  lowerCAmelCase_="relu"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_="local"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=0       ,  lowerCAmelCase_=1       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =vocab_size
		_lowercase   =d_model
		_lowercase   =d_kv
		_lowercase   =d_ff
		_lowercase   =num_layers
		# default = symmetry
		_lowercase   =num_decoder_layers if num_decoder_layers is not None else self.num_layers
		_lowercase   =num_heads
		_lowercase   =local_radius
		_lowercase   =global_block_size
		_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   =encoder_attention_type
		_lowercase   =use_cache

		_lowercase   =self.feed_forward_proj.split("-"       )
		_lowercase   =act_info[-1]
		_lowercase   =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":
			_lowercase   ="gelu_new"

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

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	@property
	def __lowerCAmelCase      (   self       ):
		_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_(lowerCAmelCase_       ,  direction="inputs"       )

		return common_inputs


	@property
	def __lowerCAmelCase      (   self       ):
		return 13




