



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

A__  :  Any								=					logging.get_logger(__name__)



@add_end_docstrings(UpperCamelCase_						)
class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Any				,				*A_     :       Dict				,				**A_     :       Optional[Any]):
									super().__init__(*A_				,				**A_)
									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    UpperCAmelCase__			(       self     :       int				,				A_     :       Any=None				,				A_     :       Dict=None				,				A_     :       List[Any]=None):
									lowerCAmelCase_			:     int									= {}
									lowerCAmelCase_			:     Dict									= {}

									if prompt is not None:
											lowerCAmelCase_			:     Any									= prompt

									if generate_kwargs is not None:
											lowerCAmelCase_			:     Union[str, Any]									= generate_kwargs
									if max_new_tokens is not None:
											if "generate_kwargs" not in forward_kwargs:
													lowerCAmelCase_			:     Any									= {}
											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''')
											lowerCAmelCase_			:     int									= max_new_tokens
									return preprocess_params, forward_kwargs, {}




							def __call__(       self     :       str				,				A_     :       Union[str, List[str], "Image.Image", List["Image.Image"]]				,				**A_     :       Union[str, Any]):
									return super().__call__(A_				,				**A_)




							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Any				,				A_     :       str=None):
									lowerCAmelCase_			:     Tuple									= load_image(A_)

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

											lowerCAmelCase_			:     Tuple									= self.model.config.model_type

											if model_type == "git":
													lowerCAmelCase_			:     Tuple									= self.image_processor(images=A_				,				return_tensors=self.framework)
													lowerCAmelCase_			:     List[str]									= self.tokenizer(text=A_				,				add_special_tokens=A_).input_ids
													lowerCAmelCase_			:     int									= [self.tokenizer.cls_token_id] + input_ids
													lowerCAmelCase_			:     Union[str, Any]									= torch.tensor(A_).unsqueeze(0)
													model_inputs.update({'''input_ids''': input_ids})

											elif model_type == "pix2struct":
													lowerCAmelCase_			:     Dict									= self.image_processor(images=A_				,				header_text=A_				,				return_tensors=self.framework)

											elif model_type != "vision-encoder-decoder":
													# vision-encoder-decoder does not support conditional generation
													lowerCAmelCase_			:     Any									= self.image_processor(images=A_				,				return_tensors=self.framework)
													lowerCAmelCase_			:     Optional[Any]									= self.tokenizer(A_				,				return_tensors=self.framework)
													model_inputs.update(A_)

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

									else:
											lowerCAmelCase_			:     str									= self.image_processor(images=A_				,				return_tensors=self.framework)

									if self.model.config.model_type == "git" and prompt is None:
											lowerCAmelCase_			:     List[Any]									= None

									return model_inputs




							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Union[str, Any]				,				A_     :       Optional[int]=None):
									# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
									# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
									if (
									    "input_ids" in model_inputs
									    and isinstance(model_inputs['''input_ids''']				,				A_)
									    and all(x is None for x in model_inputs['''input_ids'''])
									):
											lowerCAmelCase_			:     Optional[Any]									= None

									if generate_kwargs is None:
											lowerCAmelCase_			:     List[Any]									= {}
									# 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.
									lowerCAmelCase_			:     Any									= model_inputs.pop(self.model.main_input_name)
									lowerCAmelCase_			:     Tuple									= self.model.generate(A_				,				**A_				,				**A_)
									return model_outputs







							def    UpperCAmelCase__			(       self     :       int				,				A_     :       int):
									lowerCAmelCase_			:     Tuple									= []
									for output_ids in model_outputs:
											lowerCAmelCase_			:     Optional[int]									= {
											    '''generated_text''': self.tokenizer.decode(
											        A_				,				skip_special_tokens=A_				,				)
											}
											records.append(A_)
									return records













import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}



class   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							Dict:
							logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'   )
							UpperCAmelCase_    :      Any            =						model
							UpperCAmelCase_    :      int            =						kwargs.get('model_save_dir'				,		_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						kwargs.get('latest_model_name'				,		_UpperCamelCase   )



		def __call__(							self				,		**_UpperCamelCase   )	->							str:
							UpperCAmelCase_    :      Optional[int]            =						{k: np.array(_UpperCamelCase   ) for k, v in kwargs.items()}
							return self.model.run(_UpperCamelCase				,		_UpperCamelCase   )



		@staticmethod
		def 					__UpperCAmelCase			(							_UpperCamelCase				,		_UpperCamelCase=None				,		_UpperCamelCase=None   )	->							List[Any]:
							if provider is None:
												logger.info('No onnxruntime provider specified, using CPUExecutionProvider'   )
												UpperCAmelCase_    :      List[str]            =						'CPUExecutionProvider'

							return ort.InferenceSession(_UpperCamelCase				,		providers=[provider]				,		sess_options=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      Any            =						file_name if file_name is not None else ONNX_WEIGHTS_NAME

							UpperCAmelCase_    :      Optional[Any]            =						self.model_save_dir.joinpath(self.latest_model_name   )
							UpperCAmelCase_    :      str            =						Path(_UpperCamelCase   ).joinpath(_UpperCamelCase   )
							try:
												shutil.copyfile(_UpperCamelCase				,		_UpperCamelCase   )
							except shutil.SameFileError:
												pass

							# copy external weights (for models >2GB)
							UpperCAmelCase_    :      Optional[Any]            =						self.model_save_dir.joinpath(_UpperCamelCase   )
							if src_path.exists():
												UpperCAmelCase_    :      List[Any]            =						Path(_UpperCamelCase   ).joinpath(_UpperCamelCase   )
												try:
																	shutil.copyfile(_UpperCamelCase				,		_UpperCamelCase   )
												except shutil.SameFileError:
																	pass



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		**_UpperCamelCase				,		)	->							List[str]:
							if os.path.isfile(_UpperCamelCase   ):
												logger.error(f"Provided path ({save_directory}) should be a directory, not a file"   )
												return

							os.makedirs(_UpperCamelCase				,		exist_ok=_UpperCamelCase   )

							# saving model weights/files
							self._save_pretrained(_UpperCamelCase				,		**_UpperCamelCase   )



		@classmethod
		def 					__UpperCAmelCase			(							cls				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = False				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							List[str]:
							UpperCAmelCase_    :      List[str]            =						file_name if file_name is not None else ONNX_WEIGHTS_NAME
							# load model from local directory
							if os.path.isdir(_UpperCamelCase   ):
												UpperCAmelCase_    :      Union[str, Any]            =						OnnxRuntimeModel.load_model(
												    os.path.join(_UpperCamelCase				,		_UpperCamelCase   )				,		provider=_UpperCamelCase				,		sess_options=_UpperCamelCase   )
												UpperCAmelCase_    :      Tuple            =						Path(_UpperCamelCase   )
							# load model from hub
							else:
												# download model
												UpperCAmelCase_    :      List[str]            =						hf_hub_download(
												    repo_id=_UpperCamelCase				,		filename=_UpperCamelCase				,		use_auth_token=_UpperCamelCase				,		revision=_UpperCamelCase				,		cache_dir=_UpperCamelCase				,		force_download=_UpperCamelCase				,		)
												UpperCAmelCase_    :      Union[str, Any]            =						Path(_UpperCamelCase   ).parent
												UpperCAmelCase_    :      List[str]            =						Path(_UpperCamelCase   ).name
												UpperCAmelCase_    :      Union[str, Any]            =						OnnxRuntimeModel.load_model(_UpperCamelCase				,		provider=_UpperCamelCase				,		sess_options=_UpperCamelCase   )
							return cls(model=_UpperCamelCase				,		**_UpperCamelCase   )



		@classmethod
		def 					__UpperCAmelCase			(							cls				,		_UpperCamelCase				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							Optional[int]:
							UpperCAmelCase_    :      List[str]            =						None
							if len(str(_UpperCamelCase   ).split('@'   )   ) == 2:
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						model_id.split('@'   )

							return cls._from_pretrained(
							    model_id=_UpperCamelCase				,		revision=_UpperCamelCase				,		cache_dir=_UpperCamelCase				,		force_download=_UpperCamelCase				,		use_auth_token=_UpperCamelCase				,		**_UpperCamelCase				,		)






