



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    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           __a							=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(__SCREAMING_SNAKE_CASE)
           __a							=    -1

           __a							=    ids_tensor((1, 5)					,		vocab_size=model.config.vocab_size).to(__SCREAMING_SNAKE_CASE)
           __a							=    model.generate(__SCREAMING_SNAKE_CASE					,		max_new_tokens=10					,		do_sample=__SCREAMING_SNAKE_CASE)
           __a							=    tokenizer.decode(greedy_ids[0])

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

           self.assertEqual(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           __a							=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(__SCREAMING_SNAKE_CASE)
           __a							=    -1

           __a							=    ids_tensor((1, 5)					,		vocab_size=model.config.vocab_size).to(__SCREAMING_SNAKE_CASE)
           __a							=    model.generate(__SCREAMING_SNAKE_CASE					,		max_new_tokens=10					,		do_sample=__SCREAMING_SNAKE_CASE)
           __a							=    tokenizer.decode(greedy_ids[0])

           __a							=    TextIteratorStreamer(__SCREAMING_SNAKE_CASE)
           __a							=    {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
           __a							=    Thread(target=model.generate					,		kwargs=__SCREAMING_SNAKE_CASE)
           thread.start()
           __a							=    ''''''
           for new_text in streamer:
               streamer_text += new_text

           self.assertEqual(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           __a							=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(__SCREAMING_SNAKE_CASE)
           __a							=    -1

           __a							=    ids_tensor((1, 5)					,		vocab_size=model.config.vocab_size).to(__SCREAMING_SNAKE_CASE)
           __a							=    model.generate(__SCREAMING_SNAKE_CASE					,		max_new_tokens=10					,		do_sample=__SCREAMING_SNAKE_CASE)
           __a							=    greedy_ids[:, input_ids.shape[1] :]
           __a							=    tokenizer.decode(new_greedy_ids[0])

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

           self.assertEqual(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    AutoTokenizer.from_pretrained('''distilgpt2''')
           __a							=    AutoModelForCausalLM.from_pretrained('''distilgpt2''').to(__SCREAMING_SNAKE_CASE)
           __a							=    -1

           __a							=    torch.ones((1, 5)					,		device=__SCREAMING_SNAKE_CASE).long() * model.config.bos_token_id
           with CaptureStdout() as cs:
               __a							=    TextStreamer(__SCREAMING_SNAKE_CASE					,		skip_special_tokens=__SCREAMING_SNAKE_CASE)
               model.generate(__SCREAMING_SNAKE_CASE					,		max_new_tokens=1					,		do_sample=__SCREAMING_SNAKE_CASE					,		streamer=__SCREAMING_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
           __a							=    cs.out[:-1]  # Remove the final "\n"
           __a							=    tokenizer(__SCREAMING_SNAKE_CASE					,		return_tensors='''pt''')
           self.assertEqual(streamer_text_tokenized.input_ids.shape					,		(1, 1))





       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           __a							=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(__SCREAMING_SNAKE_CASE)
           __a							=    -1

           __a							=    ids_tensor((1, 5)					,		vocab_size=model.config.vocab_size).to(__SCREAMING_SNAKE_CASE)
           __a							=    TextIteratorStreamer(__SCREAMING_SNAKE_CASE					,		timeout=0.0_01)
           __a							=    {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
           __a							=    Thread(target=model.generate					,		kwargs=__SCREAMING_SNAKE_CASE)
           thread.start()

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










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						:Tuple						=					logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase   )
class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Optional[int]					,		**__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)

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

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



       def __call__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, "Image.Image", List[Dict[str, Any]]]					,		__SCREAMING_SNAKE_CASE							:					Union[str, List[str]] = None					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		):


           '''simple docstring'''

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

           if isinstance(__SCREAMING_SNAKE_CASE					,		(str, Image.Image)):
               __a							=    {'''image''': image, '''candidate_labels''': candidate_labels}
           else:
               __a							=    image
           __a							=    super().__call__(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           return results



       def 		_lowerCamelCase      (		self							:					str					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    {}
           if "threshold" in kwargs:
               __a							=    kwargs['''threshold''']
           if "top_k" in kwargs:
               __a							=    kwargs['''top_k''']
           return {}, {}, postprocess_params



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    load_image(inputs['''image'''])
           __a							=    inputs['''candidate_labels''']
           if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               __a							=    candidate_labels.split(''',''')

           __a							=    torch.tensor([[image.height, image.width]]					,		dtype=torch.intaa)
           for i, candidate_label in enumerate(__SCREAMING_SNAKE_CASE):
               __a							=    self.tokenizer(__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
               __a							=    self.image_processor(__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
               yield {
                   "is_last": i == len(__SCREAMING_SNAKE_CASE) - 1,
                   "target_size": target_size,
                   "candidate_label": candidate_label,
                   **text_inputs,
                   **image_features,
               }



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    model_inputs.pop('''target_size''')
           __a							=    model_inputs.pop('''candidate_label''')
           __a							=    model_inputs.pop('''is_last''')

           __a							=    self.model(**__SCREAMING_SNAKE_CASE)

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



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple=0.1					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=None):


           '''simple docstring'''

           __a							=    []
           for model_output in model_outputs:
               __a							=    model_output['''candidate_label''']
               __a							=    BaseModelOutput(__SCREAMING_SNAKE_CASE)
               __a							=    self.image_processor.post_process_object_detection(
                   outputs=__SCREAMING_SNAKE_CASE					,		threshold=__SCREAMING_SNAKE_CASE					,		target_sizes=model_output['''target_size'''])[0]

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

                   __a							=    {'''score''': score, '''label''': label, '''box''': box}
                   results.append(__SCREAMING_SNAKE_CASE)

           __a							=    sorted(__SCREAMING_SNAKE_CASE					,		key=lambda __SCREAMING_SNAKE_CASE: x["score"]					,		reverse=__SCREAMING_SNAKE_CASE)
           if top_k:
               __a							=    results[:top_k]

           return results





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					"torch.Tensor"):


           '''simple docstring'''

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





