





'''simple docstring'''


import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				_lowerCamelCase				:	Tuple				    =       False


if is_vision_available():
				from PIL import Image
else:



				class 							__UpperCAmelCase  :







						'''simple docstring'''







						@staticmethod
						def        A     (*_lowerCAmelCase						:      int      ,      **_lowerCAmelCase						:      List[Any]      ):
							pass



@is_pipeline_test
@require_vision
class 							__UpperCAmelCase  (       unittest.TestCase						):







		'''simple docstring'''







		__lowerCAmelCase 				=    MODEL_FOR_VISION_2_SEQ_MAPPING
		__lowerCAmelCase 				=    TF_MODEL_FOR_VISION_2_SEQ_MAPPING







		def        A     (self						:      Any      ,      _lowerCAmelCase						:      Tuple      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      int      ):
			A							  =      pipeline("""image-to-text"""      ,      model=_lowerCAmelCase      ,      tokenizer=_lowerCAmelCase      ,      image_processor=_lowerCAmelCase      )
			A							  =      [
			    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      ),
			    """./tests/fixtures/tests_samples/COCO/000000039769.png""",
			]
			return pipe, examples







		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      int      ):
			A							  =      pipe(_lowerCAmelCase      )
			self.assertEqual(
			    _lowerCAmelCase      ,      [
			        [{"""generated_text""": ANY(_lowerCAmelCase      )}],
			        [{"""generated_text""": ANY(_lowerCAmelCase      )}],
			    ]      ,      )







		@require_tf
		def        A     (self						:      Optional[Any]      ):
			A							  =      pipeline("""image-to-text"""      ,      model="""hf-internal-testing/tiny-random-vit-gpt2"""      ,      framework="""tf"""      )
			A							  =      """./tests/fixtures/tests_samples/COCO/000000039769.png"""

			A							  =      pipe(_lowerCAmelCase      )
			self.assertEqual(
			    _lowerCAmelCase      ,      [
			        {
			            """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
			        },
			    ]      ,      )

			A							  =      pipe([image, image]      )
			self.assertEqual(
			    _lowerCAmelCase      ,      [
			        [
			            {
			                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
			            }
			        ],
			        [
			            {
			                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
			            }
			        ],
			    ]      ,      )

			A							  =      pipe(_lowerCAmelCase      ,      max_new_tokens=1      )
			self.assertEqual(
			    _lowerCAmelCase      ,      [{"""generated_text""": """growth"""}]      ,      )







		@require_torch
		def        A     (self						:      Union[str, Any]      ):
			A							  =      pipeline("""image-to-text"""      ,      model="""hf-internal-testing/tiny-random-vit-gpt2"""      )
			A							  =      """./tests/fixtures/tests_samples/COCO/000000039769.png"""

			A							  =      pipe(_lowerCAmelCase      )
			self.assertEqual(
			    _lowerCAmelCase      ,      [
			        {
			            """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
			        },
			    ]      ,      )

			A							  =      pipe([image, image]      )
			self.assertEqual(
			    _lowerCAmelCase      ,      [
			        [
			            {
			                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
			            }
			        ],
			        [
			            {
			                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
			            }
			        ],
			    ]      ,      )







		@require_torch
		def        A     (self						:      int      ):
			A							  =      pipeline("""image-to-text"""      ,      model="""hf-internal-testing/tiny-random-BlipForConditionalGeneration"""      )
			A							  =      """./tests/fixtures/tests_samples/COCO/000000039769.png"""
			A							  =      """a photo of"""

			A							  =      pipe(_lowerCAmelCase      ,      prompt=_lowerCAmelCase      )
			self.assertTrue(outputs[0]["""generated_text"""].startswith(_lowerCAmelCase      )      )







		@slow
		@require_torch
		def        A     (self						:      List[Any]      ):
			A							  =      pipeline("""image-to-text"""      ,      model="""ydshieh/vit-gpt2-coco-en"""      )
			A							  =      """./tests/fixtures/tests_samples/COCO/000000039769.png"""

			A							  =      pipe(_lowerCAmelCase      )
			self.assertEqual(_lowerCAmelCase      ,      [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}]      )

			A							  =      pipe([image, image]      )
			self.assertEqual(
			    _lowerCAmelCase      ,      [
			        [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}],
			        [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}],
			    ]      ,      )







		@slow
		@require_torch
		def        A     (self						:      Optional[Any]      ):
			A							  =      pipeline("""image-to-text"""      ,      model="""Salesforce/blip-image-captioning-base"""      )
			A							  =      """https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"""
			A							  =      Image.open(requests.get(_lowerCAmelCase      ,      stream=_lowerCAmelCase      ).raw      )

			A							  =      pipe(_lowerCAmelCase      )
			self.assertEqual(_lowerCAmelCase      ,      [{"""generated_text""": """a pink pokemon pokemon with a blue shirt and a blue shirt"""}]      )







		@slow
		@require_torch
		def        A     (self						:      List[str]      ):
			A							  =      pipeline("""image-to-text"""      ,      model="""microsoft/git-base-coco"""      )
			A							  =      """https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"""
			A							  =      Image.open(requests.get(_lowerCAmelCase      ,      stream=_lowerCAmelCase      ).raw      )

			A							  =      pipe(_lowerCAmelCase      )
			self.assertEqual(_lowerCAmelCase      ,      [{"""generated_text""": """a cartoon of a purple character."""}]      )







		@slow
		@require_torch
		def        A     (self						:      str      ):
			A							  =      pipeline("""image-to-text"""      ,      model="""Salesforce/blip-image-captioning-base"""      )
			A							  =      """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
			A							  =      Image.open(requests.get(_lowerCAmelCase      ,      stream=_lowerCAmelCase      ).raw      )

			A							  =      """a photography of"""

			A							  =      pipe(_lowerCAmelCase      ,      prompt=_lowerCAmelCase      )
			self.assertEqual(_lowerCAmelCase      ,      [{"""generated_text""": """a photography of a volcano"""}]      )

			with self.assertRaises(_lowerCAmelCase      ):
				A							  =      pipe([image, image]      ,      prompt=[prompt, prompt]      )







		@slow
		@require_torch
		def        A     (self						:      Optional[int]      ):
			A							  =      pipeline("""image-to-text"""      ,      model="""microsoft/git-base-coco"""      )
			A							  =      """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
			A							  =      Image.open(requests.get(_lowerCAmelCase      ,      stream=_lowerCAmelCase      ).raw      )

			A							  =      """a photo of a"""

			A							  =      pipe(_lowerCAmelCase      ,      prompt=_lowerCAmelCase      )
			self.assertEqual(_lowerCAmelCase      ,      [{"""generated_text""": """a photo of a tent with a tent and a tent in the background."""}]      )

			with self.assertRaises(_lowerCAmelCase      ):
				A							  =      pipe([image, image]      ,      prompt=[prompt, prompt]      )







		@unittest.skipIf(
		    not is_torch_greater_or_equal_than_1_11      ,      reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""      )
		@slow
		@require_torch
		def        A     (self						:      Optional[Any]      ):
			A							  =      pipeline("""image-to-text"""      ,      model="""google/pix2struct-ai2d-base"""      )
			A							  =      """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
			A							  =      Image.open(requests.get(_lowerCAmelCase      ,      stream=_lowerCAmelCase      ).raw      )

			A							  =      """What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"""

			A							  =      pipe(_lowerCAmelCase      ,      prompt=_lowerCAmelCase      )
			self.assertEqual(_lowerCAmelCase      ,      [{"""generated_text""": """ash cloud"""}]      )

			with self.assertRaises(_lowerCAmelCase      ):
				A							  =      pipe([image, image]      ,      prompt=[prompt, prompt]      )







		@slow
		@require_tf
		def        A     (self						:      str      ):
			A							  =      pipeline("""image-to-text"""      ,      model="""ydshieh/vit-gpt2-coco-en"""      ,      framework="""tf"""      )
			A							  =      """./tests/fixtures/tests_samples/COCO/000000039769.png"""

			A							  =      pipe(_lowerCAmelCase      )
			self.assertEqual(_lowerCAmelCase      ,      [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}]      )

			A							  =      pipe([image, image]      )
			self.assertEqual(
			    _lowerCAmelCase      ,      [
			        [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}],
			        [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}],
			    ]      ,      )








'''simple docstring'''


import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_lowerCamelCase				:	Tuple				    =       logging.getLogger(__name__)



class 							__UpperCAmelCase  :







		'''simple docstring'''







		def __init__(self						:      Union[str, Any]      ):
			A							  =      False







		def        A     (self						:      Any      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Any      ):
			if not self.initialized:
				A							  =      RagRetriever(
				    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )
				A							  =      True







		def        A     (self						:      Optional[int]      ):
			self.retriever.index.init_index()







		def        A     (self						:      Optional[int]      ):
			# delete the old self.retriever object before assigning the new index
			del self.retriever
			A							  =      False







		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ):
			A  ,    A							  =      self.retriever._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
			A							  =      self.retriever.index.get_doc_dicts(_lowerCAmelCase      )
			return doc_ids, retrieved_doc_embeds, doc_dicts






class 							__UpperCAmelCase  (       A__						):







		'''simple docstring'''







		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      Tuple      ,      _lowerCAmelCase						:      str=None      ):
			if index is not None and index.is_initialized() and len(_lowerCAmelCase      ) > 0:
				raise ValueError(
				    """When using Ray for distributed fine-tuning, """
				    """you'll need to provide the paths instead, """
				    """as the dataset and the index are loaded """
				    """separately. More info in examples/rag/use_own_knowledge_dataset.py """      )

			super().__init__(
			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )

			A							  =      retrieval_workers
			A							  =      question_encoder_tokenizer
			A							  =      generator_tokenizer
			if len(self.retrieval_workers      ) > 0:
				ray.get(
				    [
				        worker.create_rag_retriever.remote(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )
				        for worker in self.retrieval_workers
				    ]      )







		def        A     (self						:      List[Any]      ):
			logger.info("""initializing retrieval"""      )

			if len(self.retrieval_workers      ) > 0:
				ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
			else:
				# Non-distributed training. Load index into this same process.
				self.index.init_index()







		def        A     (self						:      str      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Tuple      ):
			if len(self.retrieval_workers      ) > 0:
				# Select a random retrieval actor.
				A							  =      self.retrieval_workers[random.randint(0      ,      len(self.retrieval_workers      ) - 1      )]
				A  ,    A  ,    A							  =      ray.get(
				    random_worker.retrieve.remote(_lowerCAmelCase      ,      _lowerCAmelCase      )      )
			else:
				A  ,    A							  =      self._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
				A							  =      self.index.get_doc_dicts(_lowerCAmelCase      )
			return retrieved_doc_embeds, doc_ids, doc_dicts







		@classmethod
		def        A     (cls						:      Union[str, Any]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Optional[Any]=None      ,      **_lowerCAmelCase						:      Union[str, Any]      ):
			return super(_lowerCAmelCase      ,      cls      ).get_tokenizers(_lowerCAmelCase      ,      _lowerCAmelCase      ,      **_lowerCAmelCase      )







		@classmethod
		def        A     (cls						:      List[Any]      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str=None      ,      **_lowerCAmelCase						:      List[str]      ):
			A							  =      kwargs.pop("""config"""      ,      _lowerCAmelCase      ) or RagConfig.from_pretrained(_lowerCAmelCase      ,      **_lowerCAmelCase      )
			A							  =      RagTokenizer.from_pretrained(_lowerCAmelCase      ,      config=_lowerCAmelCase      )
			A							  =      rag_tokenizer.question_encoder
			A							  =      rag_tokenizer.generator

			if indexed_dataset is not None:
				A							  =      """custom"""
				A							  =      CustomHFIndex(config.retrieval_vector_size      ,      _lowerCAmelCase      )
			else:
				A							  =      cls._build_index(_lowerCAmelCase      )

			return cls(
			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      retrieval_workers=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      )







		def        A     (self						:      List[Any]      ):
			logger.info("""re-loading the new dataset with embeddings"""      )
			# access from the training loop

			ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]      )

			# build the index object again
			A							  =      self._build_index(self.config      )

			ray.get(
			    [
			        worker.create_rag_retriever.remote(
			            self.config      ,      self.question_encoder_tokenizer      ,      self.generator_tokenizer      ,      _lowerCAmelCase      )
			        for worker in self.retrieval_workers
			    ]      )

