





'''simple docstring'''

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType






class    UpperCAmelCase	(       __lowerCamelCase				):
 def __init__(				self      :Optional[int]						,       lowercase_      :str							)->							Optional[Any]:
     A__				=			data



 def __iter__(				self      :Any							)->							int:
     for element in self.data:
         yield element





def 						UpperCamelCase				(							_lowerCamelCase					:					int=True       ):
    A__				=			Accelerator(even_batches=__SCREAMING_SNAKE_CASE       )
    assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
    return accelerator





def 						UpperCamelCase				(							_lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					Optional[Any] = False       ):
    if iterable:
        A__				=			DummyIterableDataset(torch.as_tensor(range(__SCREAMING_SNAKE_CASE       )       )       )
    else:
        A__				=			TensorDataset(torch.as_tensor(range(__SCREAMING_SNAKE_CASE       )       )       )

    A__				=			DataLoader(__SCREAMING_SNAKE_CASE						,       batch_size=__SCREAMING_SNAKE_CASE       )
    A__				=			accelerator.prepare(__SCREAMING_SNAKE_CASE       )

    return dl





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					Tuple						,       ):
    A__				=			create_dataloader(accelerator=__SCREAMING_SNAKE_CASE						,       dataset_size=__SCREAMING_SNAKE_CASE						,       batch_size=__SCREAMING_SNAKE_CASE       )

    A__				=			[len(batch[0]       ) for batch in dl]

    if accelerator.process_index == 0:
        assert batch_sizes == process_0_expected_batch_sizes
    elif accelerator.process_index == 1:
        assert batch_sizes == process_1_expected_batch_sizes





def 						UpperCamelCase				(							):
    A__				=			create_accelerator()

    # without padding, we would expect a different number of batches
    verify_dataloader_batch_sizes(
        __SCREAMING_SNAKE_CASE						,       dataset_size=3						,       batch_size=1						,       process_0_expected_batch_sizes=[1, 1]						,       process_1_expected_batch_sizes=[1, 1]						,       )

    # without padding, we would expect the same number of batches, but different sizes
    verify_dataloader_batch_sizes(
        __SCREAMING_SNAKE_CASE						,       dataset_size=7						,       batch_size=2						,       process_0_expected_batch_sizes=[2, 2]						,       process_1_expected_batch_sizes=[2, 2]						,       )





def 						UpperCamelCase				(							):
    A__				=			create_accelerator(even_batches=__SCREAMING_SNAKE_CASE       )

    verify_dataloader_batch_sizes(
        __SCREAMING_SNAKE_CASE						,       dataset_size=3						,       batch_size=1						,       process_0_expected_batch_sizes=[1, 1]						,       process_1_expected_batch_sizes=[1]						,       )

    verify_dataloader_batch_sizes(
        __SCREAMING_SNAKE_CASE						,       dataset_size=7						,       batch_size=2						,       process_0_expected_batch_sizes=[2, 2]						,       process_1_expected_batch_sizes=[2, 1]						,       )





def 						UpperCamelCase				(							):
    A__				=			create_accelerator(even_batches=__SCREAMING_SNAKE_CASE       )

    A__				=			torch.nn.Linear(1						,       1       )
    A__				=			accelerator.prepare(__SCREAMING_SNAKE_CASE       )

    A__				=			create_dataloader(__SCREAMING_SNAKE_CASE						,       dataset_size=3						,       batch_size=1       )

    A__				=			[]
    with accelerator.join_uneven_inputs([ddp_model]       ):
        for batch_idx, batch in enumerate(__SCREAMING_SNAKE_CASE       ):
            A__				=			ddp_model(batch[0].float()       )
            A__				=			output.sum()
            loss.backward()
            batch_idxs.append(__SCREAMING_SNAKE_CASE       )

    accelerator.wait_for_everyone()

    if accelerator.process_index == 0:
        assert batch_idxs == [0, 1]
    elif accelerator.process_index == 1:
        assert batch_idxs == [0]





def 						UpperCamelCase				(							_lowerCamelCase					:					int       ):
    with warnings.catch_warnings(record=__SCREAMING_SNAKE_CASE       ) as w:
        with accelerator.join_uneven_inputs([Mock()]       ):
            pass

        assert issubclass(w[-1].category						,       __SCREAMING_SNAKE_CASE       )
        assert "only supported for multi-GPU" in str(w[-1].message       )





def 						UpperCamelCase				(							):
    A__				=			True
    A__				=			False
    A__				=			create_accelerator(even_batches=__SCREAMING_SNAKE_CASE       )
    A__				=			torch.nn.Linear(1						,       1       )
    A__				=			accelerator.prepare(__SCREAMING_SNAKE_CASE       )
    A__				=			create_dataloader(__SCREAMING_SNAKE_CASE						,       dataset_size=3						,       batch_size=1       )
    A__				=			create_dataloader(__SCREAMING_SNAKE_CASE						,       dataset_size=3						,       batch_size=1       )

    with accelerator.join_uneven_inputs([ddp_model]						,       even_batches=__SCREAMING_SNAKE_CASE       ):
        A__				=			train_dl.batch_sampler.even_batches
        A__				=			valid_dl.batch_sampler.even_batches

    assert train_dl_overridden_value == overridden_even_batches
    assert valid_dl_overridden_value == overridden_even_batches
    assert train_dl.batch_sampler.even_batches == default_even_batches
    assert valid_dl.batch_sampler.even_batches == default_even_batches





def 						UpperCamelCase				(							):
    A__				=			True
    A__				=			False
    A__				=			create_accelerator(even_batches=__SCREAMING_SNAKE_CASE       )
    A__				=			torch.nn.Linear(1						,       1       )
    A__				=			accelerator.prepare(__SCREAMING_SNAKE_CASE       )
    create_dataloader(__SCREAMING_SNAKE_CASE						,       dataset_size=3						,       batch_size=1						,       iterable=__SCREAMING_SNAKE_CASE       )
    A__				=			create_dataloader(__SCREAMING_SNAKE_CASE						,       dataset_size=3						,       batch_size=1       )

    with warnings.catch_warnings():
        warnings.filterwarnings("ignore"       )
        try:
            with accelerator.join_uneven_inputs([ddp_model]						,       even_batches=__SCREAMING_SNAKE_CASE       ):
                A__				=			batch_dl.batch_sampler.even_batches
        except AttributeError:
            # ensure attribute error is not raised when processing iterable dl
            raise AssertionError

    assert batch_dl_overridden_value == overridden_even_batches
    assert batch_dl.batch_sampler.even_batches == default_even_batches





def 						UpperCamelCase				(							):
    A__				=			create_accelerator()
    A__				=			torch.nn.Linear(1						,       1       )
    A__				=			accelerator.prepare(__SCREAMING_SNAKE_CASE       )
    create_dataloader(__SCREAMING_SNAKE_CASE						,       dataset_size=3						,       batch_size=1						,       iterable=__SCREAMING_SNAKE_CASE       )

    with warnings.catch_warnings(record=__SCREAMING_SNAKE_CASE       ) as w:
        with accelerator.join_uneven_inputs([ddp_model]						,       even_batches=__SCREAMING_SNAKE_CASE       ):
            pass

        assert issubclass(w[-1].category						,       __SCREAMING_SNAKE_CASE       )
        assert "only supported for map-style datasets" in str(w[-1].message       )





def 						UpperCamelCase				(							):
    A__				=			create_accelerator()

    accelerator.print("Test that even_batches variable ensures uniform batches across processes"       )
    test_default_ensures_even_batch_sizes()

    accelerator.print("Run tests with even_batches disabled"       )
    test_can_disable_even_batches()

    accelerator.print("Test joining uneven inputs"       )
    test_can_join_uneven_inputs()

    accelerator.print("Test overriding even_batches when joining uneven inputs"       )
    test_join_can_override_even_batches()

    accelerator.print("Test overriding even_batches for mixed dataloader types"       )
    test_join_can_override_for_mixed_type_dataloaders()

    accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders"       )
    test_join_raises_warning_for_iterable_when_overriding_even_batches()

    accelerator.print("Test join with non DDP distributed raises warning"       )
    A__				=			accelerator.state.distributed_type
    A__				=			DistributedType.FSDP
    test_join_raises_warning_for_non_ddp_distributed(__SCREAMING_SNAKE_CASE       )
    A__				=			original_state


if __name__ == "__main__":
  main()





import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)  # pylint: disable=invalid-name







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=768       ):
		super().__init__(snake_case       )
		lowercase       =    proj_size

		lowercase       =    CLIPVisionModel(snake_case       )
		lowercase       =    PaintByExampleMapper(snake_case       )
		lowercase       =    nn.LayerNorm(config.hidden_size       )
		lowercase       =    nn.Linear(config.hidden_size		,    self.proj_size       )

		# uncondition for scaling
		lowercase       =    nn.Parameter(torch.randn((1, 1, self.proj_size)       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=False       ):
		lowercase       =    self.model(pixel_values=snake_case       )
		lowercase       =    clip_output.pooler_output
		lowercase       =    self.mapper(latent_states[:, None]       )
		lowercase       =    self.final_layer_norm(snake_case       )
		lowercase       =    self.proj_out(snake_case       )
		if return_uncond_vector:
			return latent_states, self.uncond_vector

		return latent_states







class        A_   (			nn.Module   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		super().__init__()
		lowercase       =    (config.num_hidden_layers + 1) // 5
		lowercase       =    config.hidden_size
		lowercase       =    1
		lowercase       =    nn.ModuleList(
		    [
		        BasicTransformerBlock(snake_case		,    snake_case		,    snake_case		,    activation_fn='gelu'		,    attention_bias=snake_case       )
		        for _ in range(snake_case       )
		    ]       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		for block in self.blocks:
			lowercase       =    block(snake_case       )

		return hidden_states

