# 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      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__       ):
            _A=	data





     def __iter__(					self       ):
            for element in self.data:
                   yield element


def 							UpperCamelCase				(    lowerCAmelCase_=True   )		->						Optional[int]:




       '''simple docstring'''






       _A=	Accelerator(even_batches=lowerCAmelCase_   )
       assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
       return accelerator

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_ = False   )		->						Union[str, Any]:




       '''simple docstring'''






       if iterable:
              _A=	DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase_   )   )   )
       else:
              _A=	TensorDataset(torch.as_tensor(range(lowerCAmelCase_   )   )   )

       _A=	DataLoader(lowerCAmelCase_       ,					batch_size=lowerCAmelCase_   )
       _A=	accelerator.prepare(lowerCAmelCase_   )

       return dl

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					)		->						Union[str, Any]:




       '''simple docstring'''






       _A=	create_dataloader(accelerator=lowerCAmelCase_       ,					dataset_size=lowerCAmelCase_       ,					batch_size=lowerCAmelCase_   )

       _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				(    )		->						List[Any]:




       '''simple docstring'''






       _A=	create_accelerator()

       # without padding, we would expect a different number of batches
       verify_dataloader_batch_sizes(
           lowerCAmelCase_       ,					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(
           lowerCAmelCase_       ,					dataset_size=7       ,					batch_size=2       ,					process_0_expected_batch_sizes=[2, 2]       ,					process_1_expected_batch_sizes=[2, 2]       ,					)

def 							UpperCamelCase				(    )		->						List[str]:




       '''simple docstring'''






       _A=	create_accelerator(even_batches=lowerCAmelCase_   )

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

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

def 							UpperCamelCase				(    )		->						List[str]:




       '''simple docstring'''






       _A=	create_accelerator(even_batches=lowerCAmelCase_   )

       _A=	torch.nn.Linear(1       ,					1   )
       _A=	accelerator.prepare(lowerCAmelCase_   )

       _A=	create_dataloader(lowerCAmelCase_       ,					dataset_size=3       ,					batch_size=1   )

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

       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_   )		->						str:




       '''simple docstring'''






       with warnings.catch_warnings(record=lowerCAmelCase_   ) as w:
              with accelerator.join_uneven_inputs([Mock()]   ):
                     pass

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

def 							UpperCamelCase				(    )		->						Union[str, Any]:




       '''simple docstring'''






       _A=	True
       _A=	False
       _A=	create_accelerator(even_batches=lowerCAmelCase_   )
       _A=	torch.nn.Linear(1       ,					1   )
       _A=	accelerator.prepare(lowerCAmelCase_   )
       _A=	create_dataloader(lowerCAmelCase_       ,					dataset_size=3       ,					batch_size=1   )
       _A=	create_dataloader(lowerCAmelCase_       ,					dataset_size=3       ,					batch_size=1   )

       with accelerator.join_uneven_inputs([ddp_model]       ,					even_batches=lowerCAmelCase_   ):
              _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				(    )		->						Optional[Any]:




       '''simple docstring'''






       _A=	True
       _A=	False
       _A=	create_accelerator(even_batches=lowerCAmelCase_   )
       _A=	torch.nn.Linear(1       ,					1   )
       _A=	accelerator.prepare(lowerCAmelCase_   )
       create_dataloader(lowerCAmelCase_       ,					dataset_size=3       ,					batch_size=1       ,					iterable=lowerCAmelCase_   )
       _A=	create_dataloader(lowerCAmelCase_       ,					dataset_size=3       ,					batch_size=1   )

       with warnings.catch_warnings():
              warnings.filterwarnings('ignore'   )
              try:
                     with accelerator.join_uneven_inputs([ddp_model]       ,					even_batches=lowerCAmelCase_   ):
                            _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				(    )		->						Optional[int]:




       '''simple docstring'''






       _A=	create_accelerator()
       _A=	torch.nn.Linear(1       ,					1   )
       _A=	accelerator.prepare(lowerCAmelCase_   )
       create_dataloader(lowerCAmelCase_       ,					dataset_size=3       ,					batch_size=1       ,					iterable=lowerCAmelCase_   )

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

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

def 							UpperCamelCase				(    )		->						str:




       '''simple docstring'''






       _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(lowerCAmelCase_   )
       _A=	original_state


if __name__ == "__main__":
      main()
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		="""bridgetower_vision_model"""



     def __init__(					self				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=3				,      lowerCAmelCase__=16				,      lowerCAmelCase__=288				,      lowerCAmelCase__=1				,      lowerCAmelCase__=1E-05				,      lowerCAmelCase__=False				,      lowerCAmelCase__=True				,      lowerCAmelCase__=False				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_channels
            _A=	patch_size
            _A=	image_size
            _A=	initializer_factor
            _A=	layer_norm_eps
            _A=	stop_gradient
            _A=	share_layernorm
            _A=	remove_last_layer





     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A,			_A=	cls.get_config_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )

            if config_dict.get('model_type'       ) == "bridgetower":
                   _A=	config_dict['text_config']

            if "model_type" in config_dict and hasattr(cls				,      'model_type'       ) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."       )

            return cls.from_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		="""bridgetower_text_model"""



     def __init__(					self				,      lowerCAmelCase__=50265				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=12				,      lowerCAmelCase__=1				,      lowerCAmelCase__=3072				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=514				,      lowerCAmelCase__=1				,      lowerCAmelCase__=1E-05				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__="absolute"				,      lowerCAmelCase__=True				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )

            _A=	vocab_size
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_attention_heads
            _A=	hidden_act
            _A=	initializer_factor
            _A=	intermediate_size
            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	max_position_embeddings
            _A=	type_vocab_size
            _A=	layer_norm_eps
            _A=	position_embedding_type
            _A=	use_cache
            _A=	pad_token_id
            _A=	bos_token_id
            _A=	eos_token_id





     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A,			_A=	cls.get_config_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )

            if config_dict.get('model_type'       ) == "bridgetower":
                   _A=	config_dict['text_config']

            if "model_type" in config_dict and hasattr(cls				,      'model_type'       ) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."       )

            return cls.from_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Dict		="""bridgetower"""



     def __init__(					self				,      lowerCAmelCase__=True				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=768				,      lowerCAmelCase__=1				,      lowerCAmelCase__=1E-05				,      lowerCAmelCase__=False				,      lowerCAmelCase__="add"				,      lowerCAmelCase__=12				,      lowerCAmelCase__=6				,      lowerCAmelCase__=False				,      lowerCAmelCase__=False				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      **lowerCAmelCase__				,      ):
            # TODO: remove this once the Hub files are updated.
            _A=	kwargs.pop('text_config_dict'				,      lowerCAmelCase__       )
            _A=	kwargs.pop('vision_config_dict'				,      lowerCAmelCase__       )

            super().__init__(**lowerCAmelCase__       )
            _A=	share_cross_modal_transformer_layers
            _A=	hidden_act
            _A=	hidden_size
            _A=	initializer_factor
            _A=	layer_norm_eps
            _A=	share_link_tower_layers
            _A=	link_tower_type
            _A=	num_attention_heads
            _A=	num_hidden_layers
            _A=	tie_word_embeddings
            _A=	init_layernorm_from_vision_encoder

            if text_config is None:
                   _A=	{}
                   logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'       )

            if vision_config is None:
                   _A=	{}
                   logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'       )

            _A=	BridgeTowerTextConfig(**lowerCAmelCase__       )
            _A=	BridgeTowerVisionConfig(**lowerCAmelCase__       )



     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return cls(text_config=text_config.to_dict()				,      vision_config=vision_config.to_dict()				,      **lowerCAmelCase__       )





     def 			a__  (					self       ):
            _A=	copy.deepcopy(self.__dict__       )
            _A=	self.text_config.to_dict()
            _A=	self.vision_config.to_dict()
            _A=	self.__class__.model_type
            return output