





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

__UpperCamelCase:	Optional[int] = logging.get_logger(__name__)


@add_end_docstrings(_UpperCamelCase      )
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	def __init__(						self:						Union[str, Any],					*lowerCamelCase_:						int,					**lowerCamelCase_:						int			):
			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    snake_case__(						self:						Optional[Any],					lowerCamelCase_:						Any=None,					lowerCamelCase_:						Optional[int]=None,					lowerCamelCase_:						List[Any]=None			):
			lowercase__					:      List[str]      =		{}
			lowercase__					:      Union[str, Any]      =		{}

			if prompt is not None:
					lowercase__					:      Tuple      =		prompt

			if generate_kwargs is not None:
					lowercase__					:      List[Any]      =		generate_kwargs
			if max_new_tokens is not None:
					if "generate_kwargs" not in forward_kwargs:
							lowercase__					:      List[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'			)
					lowercase__					:      Optional[int]      =		max_new_tokens
			return preprocess_params, forward_kwargs, {}

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

	def    snake_case__(						self:						str,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						List[Any]=None			):
			lowercase__					:      List[str]      =		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__					:      List[str]      =		self.model.config.model_type

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

					elif model_type == "pix2struct":
							lowercase__					:      List[Any]      =		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__					:      str      =		self.image_processor(images=lowerCamelCase_,					return_tensors=self.framework			)
							lowercase__					:      Union[str, Any]      =		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__					:      str      =		self.image_processor(images=lowerCamelCase_,					return_tensors=self.framework			)

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

			return model_inputs

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Tuple=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'],					lowerCamelCase_			)
			    and all(x is None for x in model_inputs['input_ids']			)
			):
					lowercase__					:      Union[str, Any]      =		None

			if generate_kwargs is None:
					lowercase__					:      Tuple      =		{}
			# 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__					:      str      =		model_inputs.pop(self.model.main_input_name			)
			lowercase__					:      Dict      =		self.model.generate(lowerCamelCase_,					**lowerCamelCase_,					**lowerCamelCase_			)
			return model_outputs





	def    snake_case__(						self:						List[str],					lowerCamelCase_:						Any			):
			lowercase__					:      Dict      =		[]
			for output_ids in model_outputs:
					lowercase__					:      Tuple      =		{
					    'generated_text': self.tokenizer.decode(
					        lowerCamelCase_,					skip_special_tokens=lowerCamelCase_,					)
					}
					records.append(lowerCamelCase_			)
			return records









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase:	Optional[int] = logging.get_logger(__name__)

__UpperCamelCase:	Optional[int] = {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"mobilenet_v1"

	def __init__(						self:						Dict,					lowerCamelCase_:						Any=3,					lowerCamelCase_:						Dict=224,					lowerCamelCase_:						int=1.0,					lowerCamelCase_:						List[Any]=8,					lowerCamelCase_:						int="relu6",					lowerCamelCase_:						Optional[int]=True,					lowerCamelCase_:						Any=0.9_9_9,					lowerCamelCase_:						List[str]=0.0_2,					lowerCamelCase_:						Optional[int]=0.0_0_1,					**lowerCamelCase_:						Tuple,					):
			super().__init__(**lowerCamelCase_			)

			if depth_multiplier <= 0:
					raise ValueError('depth_multiplier must be greater than zero.'			)

			lowercase__					:      Optional[int]      =		num_channels
			lowercase__					:      List[Any]      =		image_size
			lowercase__					:      str      =		depth_multiplier
			lowercase__					:      Optional[Any]      =		min_depth
			lowercase__					:      int      =		hidden_act
			lowercase__					:      Dict      =		tf_padding
			lowercase__					:      Optional[Any]      =		classifier_dropout_prob
			lowercase__					:      int      =		initializer_range
			lowercase__					:      Any      =		layer_norm_eps


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				version.parse("1.11"      )

	@property
	def    snake_case__(						self:						Optional[int]			):
			return OrderedDict([('pixel_values', {0: 'batch'})]			)

	@property
	def    snake_case__(						self:						Dict			):
			if self.task == "image-classification":
					return OrderedDict([('logits', {0: 'batch'})]			)
			else:
					return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]			)





	@property
	def    snake_case__(						self:						List[Any]			):
			return 1E-4


