


import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
	from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
	A_    :					int             =None

if is_ray_available():
	import ray  # noqa: E402 # isort:skip
	from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
	A_    :					List[Any]             =None
	A_    :					int             =None
	A_    :					Optional[int]             =None




def   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""simple docstring"""




						if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
												a_					=		unittest.skip("""test requires Datasets, Faiss, psutil"""      )(UpperCAmelCase__      )
						return test_case





@require_distributed_retrieval
class        lowercase_				( UpperCamelCase__):






							"""simple docstring"""







							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		tempfile.mkdtemp()
													a_					=		8

													# DPR tok
													a_					=		[
													    """[UNK]""",
													    """[CLS]""",
													    """[SEP]""",
													    """[PAD]""",
													    """[MASK]""",
													    """want""",
													    """##want""",
													    """##ed""",
													    """wa""",
													    """un""",
													    """runn""",
													    """##ing""",
													    """,""",
													    """low""",
													    """lowest""",
													]
													a_					=		os.path.join(self.tmpdirname			,    """dpr_tokenizer"""		)
													os.makedirs(_UpperCAmelCase			,    exist_ok=_UpperCAmelCase		)
													a_					=		os.path.join(_UpperCAmelCase			,    DPR_VOCAB_FILES_NAMES["""vocab_file"""]		)
													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as vocab_writer:
																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)

													# BART tok
													a_					=		[
													    """l""",
													    """o""",
													    """w""",
													    """e""",
													    """r""",
													    """s""",
													    """t""",
													    """i""",
													    """d""",
													    """n""",
													    """\u0120""",
													    """\u0120l""",
													    """\u0120n""",
													    """\u0120lo""",
													    """\u0120low""",
													    """er""",
													    """\u0120lowest""",
													    """\u0120newer""",
													    """\u0120wider""",
													    """<unk>""",
													]
													a_					=		dict(zip(_UpperCAmelCase			,    range(len(_UpperCAmelCase		)		)		)		)
													a_					=		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
													a_					=		{"""unk_token""": """<unk>"""}

													a_					=		os.path.join(self.tmpdirname			,    """bart_tokenizer"""		)
													os.makedirs(_UpperCAmelCase			,    exist_ok=_UpperCAmelCase		)
													a_					=		os.path.join(_UpperCAmelCase			,    BART_VOCAB_FILES_NAMES["""vocab_file"""]		)
													a_					=		os.path.join(_UpperCAmelCase			,    BART_VOCAB_FILES_NAMES["""merges_file"""]		)
													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
																			fp.write(json.dumps(_UpperCAmelCase		) + """\n"""		)
													with open(self.merges_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
																			fp.write("""\n""".join(_UpperCAmelCase		)		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,    """dpr_tokenizer"""		)		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,    """bart_tokenizer"""		)		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													shutil.rmtree(self.tmpdirname		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		Dataset.from_dict(
													    {
													        """id""": ["""0""", """1"""],
													        """text""": ["""foo""", """bar"""],
													        """title""": ["""Foo""", """Bar"""],
													        """embeddings""": [np.ones(self.retrieval_vector_size		), 2 * np.ones(self.retrieval_vector_size		)],
													    }		)
													dataset.add_faiss_index("""embeddings"""			,    string_factory="""Flat"""			,    metric_type=faiss.METRIC_INNER_PRODUCT		)
													return dataset


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase=12_345		):







													"""simple docstring"""


													a_					=		self.get_dummy_dataset()
													a_					=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size			,    question_encoder=DPRConfig().to_dict()			,    generator=BartConfig().to_dict()			,    )
													with patch("""transformers.models.rag.retrieval_rag.load_dataset"""		) as mock_load_dataset:
																			a_					=		dataset
																			a_					=		RagPyTorchDistributedRetriever(
																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    )
																			if init_retrieval:
																									retriever.init_retrieval(_UpperCAmelCase		)
													return retriever


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													ray.init(local_mode=_UpperCAmelCase		)
													a_					=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size			,    question_encoder=DPRConfig().to_dict()			,    generator=BartConfig().to_dict()			,    )
													a_					=		ray.remote(_UpperCAmelCase		)
													a_					=		[remote_cls.remote() for _ in range(1		)]
													with patch("""transformers.models.rag.retrieval_rag.load_dataset"""		) as mock_load_dataset:
																			a_					=		self.get_dummy_dataset()
																			a_					=		RagRayDistributedRetriever(
																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    retrieval_workers=_UpperCAmelCase			,    )
																			if init_retrieval:
																									retriever.init_retrieval()
													return retriever


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase=12_345		):







													"""simple docstring"""


													a_					=		self.get_dummy_dataset()
													a_					=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size			,    question_encoder=DPRConfig().to_dict()			,    generator=BartConfig().to_dict()			,    index_name="""custom"""			,    )
													if from_disk:
																			a_					=		os.path.join(self.tmpdirname			,    """dataset"""		)
																			a_					=		os.path.join(self.tmpdirname			,    """index.faiss"""		)
																			dataset.get_index("""embeddings"""		).save(os.path.join(self.tmpdirname			,    """index.faiss"""		)		)
																			dataset.drop_index("""embeddings"""		)
																			dataset.save_to_disk(os.path.join(self.tmpdirname			,    """dataset"""		)		)
																			del dataset
																			a_					=		RagPyTorchDistributedRetriever(
																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    )
													else:
																			a_					=		RagPyTorchDistributedRetriever(
																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    index=CustomHFIndex(config.retrieval_vector_size			,    _UpperCAmelCase		)			,    )
													if init_retrieval:
																			retriever.init_retrieval(_UpperCAmelCase		)
													return retriever


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase		):







													"""simple docstring"""


													ray.init(local_mode=_UpperCAmelCase		)
													a_					=		self.get_dummy_dataset()
													a_					=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size			,    question_encoder=DPRConfig().to_dict()			,    generator=BartConfig().to_dict()			,    index_name="""custom"""			,    )
													a_					=		ray.remote(_UpperCAmelCase		)
													a_					=		[remote_cls.remote() for _ in range(1		)]
													if from_disk:
																			a_					=		os.path.join(self.tmpdirname			,    """dataset"""		)
																			a_					=		os.path.join(self.tmpdirname			,    """index.faiss"""		)
																			dataset.get_index("""embeddings"""		).save(os.path.join(self.tmpdirname			,    """index.faiss"""		)		)
																			dataset.drop_index("""embeddings"""		)
																			dataset.save_to_disk(os.path.join(self.tmpdirname			,    """dataset"""		)		)
																			del dataset
																			a_					=		RagRayDistributedRetriever(
																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    retrieval_workers=_UpperCAmelCase			,    index=CustomHFIndex.load_from_disk(
																			        vector_size=config.retrieval_vector_size			,    dataset_path=config.passages_path			,    index_path=config.index_path			,    )			,    )
													else:
																			a_					=		RagRayDistributedRetriever(
																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    retrieval_workers=_UpperCAmelCase			,    index=CustomHFIndex(config.retrieval_vector_size			,    _UpperCAmelCase		)			,    )
													if init_retrieval:
																			retriever.init_retrieval()
													return retriever


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_			,   a_			,   a_					=		retriever.retrieve(_UpperCAmelCase			,    n_docs=_UpperCAmelCase		)
													self.assertEqual(retrieved_doc_embeds.shape			,    (2, n_docs, self.retrieval_vector_size)		)
													self.assertEqual(len(_UpperCAmelCase		)			,    2		)
													self.assertEqual(sorted(doc_dicts[0]		)			,    ["""embeddings""", """id""", """text""", """title"""]		)
													self.assertEqual(len(doc_dicts[0]["""id"""]		)			,    _UpperCAmelCase		)
													self.assertEqual(doc_dicts[0]["""id"""][0]			,    """1"""		)  # max inner product is reached with second doc
													self.assertEqual(doc_dicts[1]["""id"""][0]			,    """0"""		)  # max inner product is reached with first doc
													self.assertListEqual(doc_ids.tolist()			,    [[1], [0]]		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		1
													a_					=		np.array(
													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)

													self.distributed_retriever_check(
													    self.get_dummy_pytorch_distributed_retriever(init_retrieval=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		1
													a_					=		np.array(
													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)

													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_UpperCAmelCase			,    from_disk=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase			,    )


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		1
													a_					=		np.array(
													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)

													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_UpperCAmelCase			,    from_disk=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase			,    )


							@require_ray
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		1
													a_					=		np.array(
													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)

													self.distributed_retriever_check(
													    self.get_dummy_ray_distributed_retriever(init_retrieval=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase		)
													ray.shutdown()


							@require_ray
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		1
													a_					=		np.array(
													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)
													with self.assertRaises(_UpperCAmelCase		):
																			self.distributed_retriever_check(
																			    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_UpperCAmelCase			,    from_disk=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase			,    )
													ray.shutdown()






							@require_ray
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		1
													a_					=		np.array(
													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)

													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_UpperCAmelCase			,    from_disk=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase		)
													ray.shutdown()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    :					Optional[Any]             ={
    """configuration_wav2vec2_conformer""": [
        """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Wav2Vec2ConformerConfig""",
    ],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_    :					Union[str, Any]             =[
	    """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """Wav2Vec2ConformerForAudioFrameClassification""",
	    """Wav2Vec2ConformerForCTC""",
	    """Wav2Vec2ConformerForPreTraining""",
	    """Wav2Vec2ConformerForSequenceClassification""",
	    """Wav2Vec2ConformerForXVector""",
	    """Wav2Vec2ConformerModel""",
	    """Wav2Vec2ConformerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_wavaveca_conformer import (
	    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    WavaVecaConformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_wavaveca_conformer import (
		    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WavaVecaConformerForAudioFrameClassification,
		    WavaVecaConformerForCTC,
		    WavaVecaConformerForPreTraining,
		    WavaVecaConformerForSequenceClassification,
		    WavaVecaConformerForXVector,
		    WavaVecaConformerModel,
		    WavaVecaConformerPreTrainedModel,
		)

else:
	import sys

	A_    :					Union[str, Any]             =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)