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

A__           =							logging.get_logger(__name__)
@add_end_docstrings(__SCREAMING_SNAKE_CASE				)
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							def __init__(				self     :    Dict	,					**__snake_case     :    List[str]		):
										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     :    List[str]	,					__snake_case     :    Union[str, "Image.Image", List[Dict[str, Any]]]	,					__snake_case     :    Union[str, List[str]] = None	,					**__snake_case     :    List[Any]	,					):
										if "text_queries" in kwargs:
													lowerCamelCase						:Any						  =     kwargs.pop('''text_queries'''		)

										if isinstance(__UpperCamelCase	,					(str, Image.Image)		):
													lowerCamelCase						:List[Any]						  =     {'''image''': image, '''candidate_labels''': candidate_labels}
										else:
													lowerCamelCase						:Tuple						  =     image
										lowerCamelCase						:Dict						  =     super().__call__(__UpperCamelCase	,					**__UpperCamelCase		)
										return results







							def 				snake_case  (				self     :    Tuple	,					**__snake_case     :    Union[str, Any]		):
										lowerCamelCase						:Optional[int]						  =     {}
										if "threshold" in kwargs:
													lowerCamelCase						:Tuple						  =     kwargs['''threshold''']
										if "top_k" in kwargs:
													lowerCamelCase						:List[Any]						  =     kwargs['''top_k''']
										return {}, {}, postprocess_params







							def 				snake_case  (				self     :    List[Any]	,					__snake_case     :    str		):
										lowerCamelCase						:Any						  =     load_image(inputs['''image''']		)
										lowerCamelCase						:Dict						  =     inputs['''candidate_labels''']
										if isinstance(__UpperCamelCase	,					__UpperCamelCase		):
													lowerCamelCase						:Any						  =     candidate_labels.split(''','''		)

										lowerCamelCase						:str						  =     torch.tensor([[image.height, image.width]]	,					dtype=torch.intaa		)
										for i, candidate_label in enumerate(__UpperCamelCase		):
													lowerCamelCase						:int						  =     self.tokenizer(__UpperCamelCase	,					return_tensors=self.framework		)
													lowerCamelCase						:Union[str, Any]						  =     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 				snake_case  (				self     :    Dict	,					__snake_case     :    str		):
										lowerCamelCase						:int						  =     model_inputs.pop('''target_size'''		)
										lowerCamelCase						:Any						  =     model_inputs.pop('''candidate_label'''		)
										lowerCamelCase						:int						  =     model_inputs.pop('''is_last'''		)

										lowerCamelCase						:Any						  =     self.model(**__UpperCamelCase		)

										lowerCamelCase						:int						  =     {'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
										return model_outputs







							def 				snake_case  (				self     :    Any	,					__snake_case     :    Dict	,					__snake_case     :    Union[str, Any]=0.1	,					__snake_case     :    int=None		):
										lowerCamelCase						:str						  =     []
										for model_output in model_outputs:
													lowerCamelCase						:List[Any]						  =     model_output['''candidate_label''']
													lowerCamelCase						:str						  =     BaseModelOutput(__UpperCamelCase		)
													lowerCamelCase						:Union[str, Any]						  =     self.image_processor.post_process_object_detection(
													    outputs=__UpperCamelCase	,					threshold=__UpperCamelCase	,					target_sizes=model_output['''target_size''']		)[0]

													for index in outputs["scores"].nonzero():
																lowerCamelCase						:str						  =     outputs['''scores'''][index].item()
																lowerCamelCase						:List[str]						  =     self._get_bounding_box(outputs['''boxes'''][index][0]		)

																lowerCamelCase						:Optional[Any]						  =     {'''score''': score, '''label''': label, '''box''': box}
																results.append(__UpperCamelCase		)

										lowerCamelCase						:List[str]						  =     sorted(__UpperCamelCase	,					key=lambda __snake_case		: x["score"]	,					reverse=__UpperCamelCase		)
										if top_k:
													lowerCamelCase						:Optional[int]						  =     results[:top_k]

										return results







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    "torch.Tensor"		):
										if self.framework != "pt":
													raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''		)
										lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     box.int().tolist()
										lowerCamelCase						:Any						  =     {
										    '''xmin''': xmin,
										    '''ymin''': ymin,
										    '''xmax''': xmax,
										    '''ymax''': ymax,
										}
										return bbox




import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
			import torch

			from transformers import AutoModelForCausalLM
@require_torch
class   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:List[Any]						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[Any]						  =     -1

										lowerCamelCase						:List[str]						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Tuple						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:str						  =     tokenizer.decode(greedy_ids[0]		)

										with CaptureStdout() as cs:
													lowerCamelCase						:str						  =     TextStreamer(__snake_case		)
													model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case	,					streamer=__snake_case		)
										# The greedy text should be printed to stdout, except for the final "\n" in the streamer
										lowerCamelCase						:Optional[int]						  =     cs.out[:-1]

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Tuple						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:int						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     -1

										lowerCamelCase						:Dict						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Tuple						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:List[Any]						  =     tokenizer.decode(greedy_ids[0]		)

										lowerCamelCase						:List[str]						  =     TextIteratorStreamer(__snake_case		)
										lowerCamelCase						:List[str]						  =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
										lowerCamelCase						:Tuple						  =     Thread(target=model.generate	,					kwargs=__snake_case		)
										thread.start()
										lowerCamelCase						:Any						  =     ''''''
										for new_text in streamer:
													streamer_text += new_text

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Dict						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[str]						  =     -1

										lowerCamelCase						:Optional[Any]						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Optional[Any]						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:List[str]						  =     greedy_ids[:, input_ids.shape[1] :]
										lowerCamelCase						:Union[str, Any]						  =     tokenizer.decode(new_greedy_ids[0]		)

										with CaptureStdout() as cs:
													lowerCamelCase						:List[str]						  =     TextStreamer(__snake_case	,					skip_prompt=__snake_case		)
													model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case	,					streamer=__snake_case		)
										# The greedy text should be printed to stdout, except for the final "\n" in the streamer
										lowerCamelCase						:int						  =     cs.out[:-1]

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
										# with actual models -- the dummy models' tokenizers are not aligned with their models, and
										# `skip_special_tokens=True` has no effect on them
										lowerCamelCase						:List[Any]						  =     AutoTokenizer.from_pretrained('''distilgpt2'''		)
										lowerCamelCase						:Union[str, Any]						  =     AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

										lowerCamelCase						:Union[str, Any]						  =     torch.ones((1, 5)	,					device=__snake_case		).long() * model.config.bos_token_id
										with CaptureStdout() as cs:
													lowerCamelCase						:Dict						  =     TextStreamer(__snake_case	,					skip_special_tokens=__snake_case		)
													model.generate(__snake_case	,					max_new_tokens=1	,					do_sample=__snake_case	,					streamer=__snake_case		)

										# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
										# re-tokenized, must only contain one token
										lowerCamelCase						:Tuple						  =     cs.out[:-1]  # Remove the final "\n"
										lowerCamelCase						:int						  =     tokenizer(__snake_case	,					return_tensors='''pt'''		)
										self.assertEqual(streamer_text_tokenized.input_ids.shape	,					(1, 1)		)







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:List[str]						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Optional[int]						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

										lowerCamelCase						:Tuple						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     TextIteratorStreamer(__snake_case	,					timeout=0.0_0_1		)
										lowerCamelCase						:Dict						  =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
										lowerCamelCase						:Tuple						  =     Thread(target=model.generate	,					kwargs=__snake_case		)
										thread.start()

										# The streamer will timeout after 0.001 seconds, so an exception will be raised
										with self.assertRaises(__snake_case		):
													lowerCamelCase						:Dict						  =     ''''''
													for new_text in streamer:
																streamer_text += new_text



