
import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory







def 						_snake_case(  )			->   str:
       raise RuntimeError("""CUDA out of memory."""    )

class __snake_case		(				nn.Module					):
    def __init__(  self				):



           '''simple docstring'''







           super().__init__()
           lowercase							:					Union[str, Any]	     =				nn.Linear(3		,4				)
           lowercase							:					str	     =				nn.BatchNormad(4				)
           lowercase							:					str	     =				nn.Linear(4		,5				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           return self.lineara(self.batchnorm(self.lineara(snake_case				)				)				)






class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				[]

           @find_executable_batch_size(starting_batch_size=128				)
           def mock_training_loop_function(snake_case				):
                  nonlocal batch_sizes
                  batch_sizes.append(snake_case				)
                  if batch_size != 8:
                         raise_fake_out_of_memory()

           mock_training_loop_function()
           self.assertListEqual(snake_case		,[128, 64, 32, 16, 8]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				[]

           @find_executable_batch_size(starting_batch_size=128				)
           def mock_training_loop_function(snake_case		,snake_case				):
                  nonlocal batch_sizes
                  batch_sizes.append(snake_case				)
                  if batch_size != 8:
                         raise_fake_out_of_memory()
                  return batch_size, arga

           lowercase     ,      lowercase							:					Union[str, Any]	     =				mock_training_loop_function("""hello"""				)
           self.assertListEqual(snake_case		,[128, 64, 32, 16, 8]				)
           self.assertListEqual([bs, arga]		,[8, """hello"""]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           @find_executable_batch_size(starting_batch_size=0				)
           def mock_training_loop_function(snake_case				):
                  pass

           with self.assertRaises(snake_case				) as cm:
                  mock_training_loop_function()
                  self.assertIn("""No executable batch size found, reached zero."""		,cm.exception.args[0]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           @find_executable_batch_size(starting_batch_size=16				)
           def mock_training_loop_function(snake_case				):
                  if batch_size > 0:
                         raise_fake_out_of_memory()
                  pass

           with self.assertRaises(snake_case				) as cm:
                  mock_training_loop_function()
                  self.assertIn("""No executable batch size found, reached zero."""		,cm.exception.args[0]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           @find_executable_batch_size(starting_batch_size=128				)
           def mock_training_loop_function(snake_case		,snake_case		,snake_case				):
                  if batch_size != 8:
                         raise raise_fake_out_of_memory()

           with self.assertRaises(snake_case				) as cm:
                  mock_training_loop_function(128		,"""hello"""		,"""world"""				)
                  self.assertIn("""Batch size was passed into `f`"""		,cm.exception.args[0]				)
                  self.assertIn("""`f(arg1='hello', arg2='world')"""		,cm.exception.args[0]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           @find_executable_batch_size(starting_batch_size=16				)
           def mock_training_loop_function(snake_case				):
                  raise ValueError("""Oops, we had an error!"""				)

           with self.assertRaises(snake_case				) as cm:
                  mock_training_loop_function()
                  self.assertIn("""Oops, we had an error!"""		,cm.exception.args[0]				)
    @require_cuda
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				torch.cuda.memory_allocated()
           lowercase							:					Optional[int]	     =				ModelForTest()
           model.cuda()
           self.assertGreater(torch.cuda.memory_allocated()		,snake_case				)
           lowercase							:					List[str]	     =				release_memory(snake_case				)
           self.assertEqual(torch.cuda.memory_allocated()		,snake_case				)


from typing import Mapping

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


lowercase  : List[str]						   =							logging.get_logger(__name__)

lowercase  : Tuple						   =							{
    """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		(				lowerCAmelCase					):
    _a		:						List[str]=    "umt5"
    _a		:						Optional[Any]=    ["past_key_values"]
    def __init__(  self		,snake_case=250112		,snake_case=512		,snake_case=64		,snake_case=1024		,snake_case=8		,snake_case=None		,snake_case=6		,snake_case=32		,snake_case=128		,snake_case=0.1		,snake_case=1e-6		,snake_case=1.0		,snake_case="gated-gelu"		,snake_case=True		,snake_case=True		,snake_case="T5Tokenizer"		,snake_case=True		,snake_case=0		,snake_case=1		,snake_case=0		,**snake_case		,):



           '''simple docstring'''







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

           lowercase							:					Dict	     =				self.feed_forward_proj.split("""-"""				)
           lowercase							:					List[Any]	     =				act_info[-1]
           lowercase							:					Any	     =				act_info[0] == """gated"""

           if len(snake_case				) > 1 and act_info[0] != "gated" or len(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":
                  lowercase							:					int	     =				"""gelu_new"""
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.d_model
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_heads
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_layers






class __snake_case		(				lowerCAmelCase					):
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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

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

           return common_inputs
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 13
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 5e-4
