


'''simple docstring'''
from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		import torch

		from transformers.modeling_outputs import BaseModelOutput

		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__snake_case							      =     logging.get_logger(__name__)
@add_end_docstrings(A__					)
class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)

							if self.framework == "tf":
												raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''							)

							requires_backends(self    ,   '''vision'''							)
							self.check_model_type(UpperCamelCase_							)




		def __call__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							if "text_queries" in kwargs:
												UpperCamelCase__   :str												= kwargs.pop('''text_queries'''							)

							if isinstance(UpperCamelCase_    ,   (str, Image.Image)							):
												UpperCamelCase__   :List[Any]												= {'''image''': image, '''candidate_labels''': candidate_labels}
							else:
												UpperCamelCase__   :Any												= image
							UpperCamelCase__   :Optional[int]												= super().__call__(UpperCamelCase_    ,   **UpperCamelCase_							)
							return results




		def     lowerCAmelCase__				(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= {}
							if "threshold" in kwargs:
												UpperCamelCase__   :List[Any]												= kwargs['''threshold''']
							if "top_k" in kwargs:
												UpperCamelCase__   :Optional[Any]												= kwargs['''top_k''']
							return {}, {}, postprocess_params




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :str												= load_image(inputs['''image''']							)
							UpperCamelCase__   :Dict												= inputs['''candidate_labels''']
							if isinstance(UpperCamelCase_    ,   UpperCamelCase_							):
												UpperCamelCase__   :Optional[Any]												= candidate_labels.split(''','''							)

							UpperCamelCase__   :Union[str, Any]												= torch.tensor([[image.height, image.width]]    ,   dtype=torch.intaa							)
							for i, candidate_label in enumerate(UpperCamelCase_							):
												UpperCamelCase__   :List[str]												= self.tokenizer(UpperCamelCase_    ,   return_tensors=self.framework							)
												UpperCamelCase__   :Optional[int]												= self.image_processor(UpperCamelCase_    ,   return_tensors=self.framework							)
												yield {
												    "is_last": i == len(UpperCamelCase_							) - 1,
												    "target_size": target_size,
												    "candidate_label": candidate_label,
												    **text_inputs,
												    **image_features,
												}




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :int												= model_inputs.pop('''target_size'''							)
							UpperCamelCase__   :Optional[int]												= model_inputs.pop('''candidate_label'''							)
							UpperCamelCase__   :List[Any]												= model_inputs.pop('''is_last'''							)

							UpperCamelCase__   :str												= self.model(**UpperCamelCase_							)

							UpperCamelCase__   :str												= {'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
							return model_outputs




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__   :str												= []
							for model_output in model_outputs:
												UpperCamelCase__   :Optional[int]												= model_output['''candidate_label''']
												UpperCamelCase__   :Union[str, Any]												= BaseModelOutput(UpperCamelCase_							)
												UpperCamelCase__   :Tuple												= self.image_processor.post_process_object_detection(
												    outputs=UpperCamelCase_    ,   threshold=UpperCamelCase_    ,   target_sizes=model_output['''target_size''']							)[0]

												for index in outputs["scores"].nonzero():
																	UpperCamelCase__   :Dict												= outputs['''scores'''][index].item()
																	UpperCamelCase__   :int												= self._get_bounding_box(outputs['''boxes'''][index][0]							)

																	UpperCamelCase__   :Optional[int]												= {'''score''': score, '''label''': label, '''box''': box}
																	results.append(UpperCamelCase_							)

							UpperCamelCase__   :Optional[int]												= sorted(UpperCamelCase_    ,   key=lambda UpperCamelCase_							: x["score"]    ,   reverse=UpperCamelCase_							)
							if top_k:
												UpperCamelCase__   :Tuple												= results[:top_k]

							return results




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							if self.framework != "pt":
												raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''							)
							UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__   :List[str]												= box.int().tolist()
							UpperCamelCase__   :Union[str, Any]												= {
							    '''xmin''': xmin,
							    '''ymin''': ymin,
							    '''xmax''': xmax,
							    '''ymax''': ymax,
							}
							return bbox



import math

import flax.linen as nn
import jax.numpy as jnp







def        SCREAMING_SNAKE_CASE_						(			__A :			jnp.ndarray  ,    __A :			int  ,    __A :			float = 1  ,    __A :			float = 1  ,    __A :			float = 1.0e4  ,    __A :			bool = False  ,    __A :			float = 1.0  ,    )	->		jnp.ndarray:

       """simple docstring"""

       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
       a_					:		int							=  float(embedding_dim // 2				)
       a_					:		str							=  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
       a_					:		Optional[int]							=  min_timescale * jnp.exp(jnp.arange(__A  ,    dtype=jnp.floataa				) * -log_timescale_increment				)
       a_					:		Optional[int]							=  jnp.expand_dims(__A  ,    1				) * jnp.expand_dims(__A  ,    0				)

       # scale embeddings
       a_					:		str							=  scale * emb

       if flip_sin_to_cos:
              a_					:		str							=  jnp.concatenate([jnp.cos(__A				), jnp.sin(__A				)]  ,    axis=1				)
       else:
              a_					:		Any							=  jnp.concatenate([jnp.sin(__A				), jnp.cos(__A				)]  ,    axis=1				)
       a_					:		Optional[int]							=  jnp.reshape(__A  ,    [jnp.shape(__A				)[0], embedding_dim]				)
       return signal
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     jnp.dtype        =						jnp.floataa
      @nn.compact
      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Optional[Any]							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_1'     )(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.silu(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_2'     )(SCREAMING_SNAKE_CASE__     )
             return temb
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     bool        =						False
      snake_case__				:     float        =						1
      @nn.compact
      def __call__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Tuple:
             return get_sinusoidal_embeddings(
                 SCREAMING_SNAKE_CASE__     ,							embedding_dim=self.dim     ,							flip_sin_to_cos=self.flip_sin_to_cos     ,							freq_shift=self.freq_shift     )


