



'''simple docstring'''


from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
						from .deepspeed import (
						    DeepSpeedEngineWrapper,
						    DeepSpeedOptimizerWrapper,
						    DeepSpeedSchedulerWrapper,
						    DummyOptim,
						    DummyScheduler,
						    HfDeepSpeedConfig,
						)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers







'''simple docstring'''


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowercase_      :				Union[str, Any]   					=    logging.get_logger(__name__)




@add_end_docstrings(_UpperCAmelCase			)
class      __UpperCamelCase       (_UpperCAmelCase			):







	def __init__(    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Dict:
							'''simple docstring'''






							super().__init__(*_lowerCAmelCase ,				**_lowerCAmelCase						)
							requires_backends(self ,				"""vision"""						)
							self.check_model_type(
							    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING						)







	def   _a   (    self ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None						)    ->    str:
							'''simple docstring'''






							lowercase        =							{}
							lowercase        =							{}

							if prompt is not None:
													lowercase        =							prompt

							if generate_kwargs is not None:
													lowercase        =							generate_kwargs
							if max_new_tokens is not None:
													if "generate_kwargs" not in forward_kwargs:
																			lowercase        =							{}
													if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																			raise ValueError(
																			    """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
																			    """ please use only one"""						)
													lowercase        =							max_new_tokens
							return preprocess_params, forward_kwargs, {}







	def __call__(    self ,				_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






							return super().__call__(_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							load_image(_lowerCAmelCase						)

							if prompt is not None:
													if not isinstance(_lowerCAmelCase ,				_lowerCAmelCase						):
																			raise ValueError(
																			    F"""Received an invalid text input, got - {type(_lowerCAmelCase						)} - but expected a single string. """
																			    """Note also that one single text can be provided for conditional image to text generation."""						)

													lowercase        =							self.model.config.model_type

													if model_type == "git":
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)
																			lowercase        =							self.tokenizer(text=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						).input_ids
																			lowercase        =							[self.tokenizer.cls_token_id] + input_ids
																			lowercase        =							torch.tensor(_lowerCAmelCase						).unsqueeze(0						)
																			model_inputs.update({"""input_ids""": input_ids}						)

													elif model_type == "pix2struct":
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				header_text=_lowerCAmelCase ,				return_tensors=self.framework						)

													elif model_type != "vision-encoder-decoder":
																			# vision-encoder-decoder does not support conditional generation
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)
																			lowercase        =							self.tokenizer(_lowerCAmelCase ,				return_tensors=self.framework						)
																			model_inputs.update(_lowerCAmelCase						)

													else:
																			raise ValueError(F"""Model type {model_type} does not support conditional text generation"""						)

							else:
													lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)

							if self.model.config.model_type == "git" and prompt is None:
													lowercase        =							None

							return model_inputs







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    Union[str, Any]:
							'''simple docstring'''






							if (
							    "input_ids" in model_inputs
							    and isinstance(model_inputs["""input_ids"""] ,				_lowerCAmelCase						)
							    and all(x is None for x in model_inputs["""input_ids"""]						)
							):
													lowercase        =							None

							if generate_kwargs is None:
													lowercase        =							{}
							# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
							#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
							#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
							#  in the `_prepare_model_inputs` method.
							lowercase        =							model_inputs.pop(self.model.main_input_name						)
							lowercase        =							self.model.generate(_lowerCAmelCase ,				**_lowerCAmelCase ,				**_lowerCAmelCase						)
							return model_outputs







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							[]
							for output_ids in model_outputs:
													lowercase        =							{
													    """generated_text""": self.tokenizer.decode(
													        _lowerCAmelCase ,				skip_special_tokens=_lowerCAmelCase ,				)
													}
													records.append(_lowerCAmelCase						)
							return records


