
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   =     logging.get_logger(__name__)





@add_end_docstrings(__a       )
class 	_lowerCAmelCase					(	__a       ):




				def __init__(					self				,				*_UpperCamelCase				,				**_UpperCamelCase     )				->      List[Any]:
							super().__init__(*_UpperCamelCase				,				**_UpperCamelCase     )
							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				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None     )				->      Optional[int]:
							lowerCAmelCase_						=	{}
							lowerCAmelCase_						=	{}

							if prompt is not None:
										lowerCAmelCase_						=	prompt

							if generate_kwargs is not None:
										lowerCAmelCase_						=	generate_kwargs
							if max_new_tokens is not None:
										if "generate_kwargs" not in forward_kwargs:
													lowerCAmelCase_						=	{}
										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_						=	max_new_tokens
							return preprocess_params, forward_kwargs, {}




				def __call__(					self				,				_UpperCamelCase				,				**_UpperCamelCase     )				->      str:
							return super().__call__(_UpperCamelCase				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=None     )				->      List[str]:
							lowerCAmelCase_						=	load_image(_UpperCamelCase     )

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

										lowerCAmelCase_						=	self.model.config.model_type

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

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

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

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

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

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

							return model_inputs




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=None     )				->      Optional[int]:
							# 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"]				,				_UpperCamelCase     )
							    and all(x is None for x in model_inputs["input_ids"]     )
							):
										lowerCAmelCase_						=	None

							if generate_kwargs is None:
										lowerCAmelCase_						=	{}
							# 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_						=	model_inputs.pop(self.model.main_input_name     )
							lowerCAmelCase_						=	self.model.generate(_UpperCamelCase				,				**_UpperCamelCase				,				**_UpperCamelCase     )
							return model_outputs




				def      __a						(					self				,				_UpperCamelCase     )				->      Optional[int]:
							lowerCAmelCase_						=	[]
							for output_ids in model_outputs:
										lowerCAmelCase_						=	{
										    "generated_text": self.tokenizer.decode(
										        _UpperCamelCase				,				skip_special_tokens=_UpperCamelCase				,				)
										}
										records.append(_UpperCamelCase     )
							return records





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


_A   =     logging.get_logger(__name__)

_A   =     {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''megatron-bert'''




				def __init__(					self				,				_UpperCamelCase=29_056				,				_UpperCamelCase=1_024				,				_UpperCamelCase=24				,				_UpperCamelCase=16				,				_UpperCamelCase=4_096				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=512				,				_UpperCamelCase=2				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-1_2				,				_UpperCamelCase=0				,				_UpperCamelCase="absolute"				,				_UpperCamelCase=True				,				**_UpperCamelCase				,				)				->      int:
							super().__init__(pad_token_id=_UpperCamelCase				,				**_UpperCamelCase     )

							lowerCAmelCase_						=	vocab_size
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	hidden_dropout_prob
							lowerCAmelCase_						=	attention_probs_dropout_prob
							lowerCAmelCase_						=	max_position_embeddings
							lowerCAmelCase_						=	type_vocab_size
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	position_embedding_type
							lowerCAmelCase_						=	use_cache



