



'''simple docstring'''




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

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
						import faiss







@require_faiss
class       _lowerCAmelCase				(     _a					):


	def        _a      (self				):
				A_      :		Tuple      =	tempfile.mkdtemp()
				A_      :		Any      =	8

				# DPR tok
				A_      :		Optional[Any]      =	[
				    """[UNK]""",
				    """[CLS]""",
				    """[SEP]""",
				    """[PAD]""",
				    """[MASK]""",
				    """want""",
				    """##want""",
				    """##ed""",
				    """wa""",
				    """un""",
				    """runn""",
				    """##ing""",
				    """,""",
				    """low""",
				    """lowest""",
				]
				A_      :		List[str]      =	os.path.join(self.tmpdirname			, """dpr_tokenizer"""				)
				os.makedirs(__lowerCamelCase			, exist_ok=__lowerCamelCase				)
				A_      :		Dict      =	os.path.join(__lowerCamelCase			, 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_      :		int      =	[
				    """l""",
				    """o""",
				    """w""",
				    """e""",
				    """r""",
				    """s""",
				    """t""",
				    """i""",
				    """d""",
				    """n""",
				    """\u0120""",
				    """\u0120l""",
				    """\u0120n""",
				    """\u0120lo""",
				    """\u0120low""",
				    """er""",
				    """\u0120lowest""",
				    """\u0120newer""",
				    """\u0120wider""",
				    """<unk>""",
				]
				A_      :		str      =	dict(zip(__lowerCamelCase			, range(len(__lowerCamelCase				)				)				)				)
				A_      :		Dict      =	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
				A_      :		str      =	{"""unk_token""": """<unk>"""}

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


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


	def        _a      (self				):
				return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			, """dpr_tokenizer"""				)				)


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


	def        _a      (self				):
				shutil.rmtree(self.tmpdirname				)


	def        _a      (self				):
				A_      :		List[str]      =	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        _a      (self				):
				A_      :		Tuple      =	self.get_dummy_dataset()
				A_      :		str      =	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_      :		Dict      =	dataset
							A_      :		Optional[int]      =	RagRetriever(
							    __lowerCamelCase			, question_encoder_tokenizer=self.get_dpr_tokenizer()			, generator_tokenizer=self.get_bart_tokenizer()			, )
				return retriever


	def        _a      (self			, lowercase				):
				A_      :		Optional[int]      =	self.get_dummy_dataset()
				A_      :		Optional[int]      =	RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size			, question_encoder=DPRConfig().to_dict()			, generator=BartConfig().to_dict()			, index_name="""custom"""			, )
				if from_disk:
							A_      :		int      =	os.path.join(self.tmpdirname			, """dataset"""				)
							A_      :		Tuple      =	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_      :		Dict      =	RagRetriever(
							    __lowerCamelCase			, question_encoder_tokenizer=self.get_dpr_tokenizer()			, generator_tokenizer=self.get_bart_tokenizer()			, )
				else:
							A_      :		Any      =	RagRetriever(
							    __lowerCamelCase			, question_encoder_tokenizer=self.get_dpr_tokenizer()			, generator_tokenizer=self.get_bart_tokenizer()			, index=CustomHFIndex(config.retrieval_vector_size			, __lowerCamelCase				)			, )
				return retriever


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

				A_      :		int      =	os.path.join(self.tmpdirname			, """hf_bert_base.hnswSQ8_correct_phi_128.c_index"""				)
				dataset.save_faiss_index("""embeddings"""			, index_file_name + """.index.dpr"""				)
				pickle.dump(dataset["""id"""]			, open(index_file_name + """.index_meta.dpr"""			, """wb"""				)				)

				A_      :		Optional[Any]      =	os.path.join(self.tmpdirname			, """psgs_w100.tsv.pkl"""				)
				A_      :		List[Any]      =	{sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
				pickle.dump(__lowerCamelCase			, open(__lowerCamelCase			, """wb"""				)				)

				A_      :		Any      =	RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size			, question_encoder=DPRConfig().to_dict()			, generator=BartConfig().to_dict()			, index_name="""legacy"""			, index_path=self.tmpdirname			, )
				A_      :		List[Any]      =	RagRetriever(
				    __lowerCamelCase			, question_encoder_tokenizer=self.get_dpr_tokenizer()			, generator_tokenizer=self.get_bart_tokenizer()				)
				return retriever


	def        _a      (self				):
				A_      :		Any      =	1
				A_      :		Optional[Any]      =	self.get_dummy_canonical_hf_index_retriever()
				A_      :		str      =	np.array(
				    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]			, dtype=np.floataa				)
				A_,       A_,       A_      :		Dict      =	retriever.retrieve(__lowerCamelCase			, n_docs=__lowerCamelCase				)
				self.assertEqual(retrieved_doc_embeds.shape			, (2, n_docs, self.retrieval_vector_size)				)
				self.assertEqual(len(__lowerCamelCase				)			, 2				)
				self.assertEqual(sorted(doc_dicts[0]				)			, ["""embeddings""", """id""", """text""", """title"""]				)
				self.assertEqual(len(doc_dicts[0]["""id"""]				)			, __lowerCamelCase				)
				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        _a      (self				):
				A_      :		List[Any]      =	self.get_dummy_canonical_hf_index_retriever()
				with tempfile.TemporaryDirectory() as tmp_dirname:
							with patch("""transformers.models.rag.retrieval_rag.load_dataset"""				) as mock_load_dataset:
										A_      :		Tuple      =	self.get_dummy_dataset()
										retriever.save_pretrained(__lowerCamelCase				)
										A_      :		Optional[Any]      =	RagRetriever.from_pretrained(__lowerCamelCase				)
										self.assertIsInstance(__lowerCamelCase			, __lowerCamelCase				)
										A_      :		List[Any]      =	np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]			, dtype=np.floataa				)
										A_      :		List[str]      =	retriever.retrieve(__lowerCamelCase			, n_docs=1				)
										self.assertTrue(out is not None				)


	def        _a      (self				):
				A_      :		Tuple      =	1
				A_      :		Tuple      =	self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase				)
				A_      :		Union[str, Any]      =	np.array(
				    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]			, dtype=np.floataa				)
				A_,       A_,       A_      :		Any      =	retriever.retrieve(__lowerCamelCase			, n_docs=__lowerCamelCase				)
				self.assertEqual(retrieved_doc_embeds.shape			, (2, n_docs, self.retrieval_vector_size)				)
				self.assertEqual(len(__lowerCamelCase				)			, 2				)
				self.assertEqual(sorted(doc_dicts[0]				)			, ["""embeddings""", """id""", """text""", """title"""]				)
				self.assertEqual(len(doc_dicts[0]["""id"""]				)			, __lowerCamelCase				)
				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        _a      (self				):
				A_      :		int      =	self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase				)
				with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(__lowerCamelCase				)
							A_      :		str      =	RagRetriever.from_pretrained(__lowerCamelCase				)
							self.assertIsInstance(__lowerCamelCase			, __lowerCamelCase				)
							A_      :		Union[str, Any]      =	np.array(
							    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]			, dtype=np.floataa				)
							A_      :		Optional[Any]      =	retriever.retrieve(__lowerCamelCase			, n_docs=1				)
							self.assertTrue(out is not None				)


	def        _a      (self				):
				A_      :		int      =	1
				A_      :		List[Any]      =	self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase				)
				A_      :		Union[str, Any]      =	np.array(
				    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]			, dtype=np.floataa				)
				A_,       A_,       A_      :		Union[str, Any]      =	retriever.retrieve(__lowerCamelCase			, n_docs=__lowerCamelCase				)
				self.assertEqual(retrieved_doc_embeds.shape			, (2, n_docs, self.retrieval_vector_size)				)
				self.assertEqual(len(__lowerCamelCase				)			, 2				)
				self.assertEqual(sorted(doc_dicts[0]				)			, ["""embeddings""", """id""", """text""", """title"""]				)
				self.assertEqual(len(doc_dicts[0]["""id"""]				)			, __lowerCamelCase				)
				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        _a      (self				):
				A_      :		Union[str, Any]      =	self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase				)
				with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(__lowerCamelCase				)
							A_      :		Optional[int]      =	RagRetriever.from_pretrained(__lowerCamelCase				)
							self.assertIsInstance(__lowerCamelCase			, __lowerCamelCase				)
							A_      :		int      =	np.array(
							    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]			, dtype=np.floataa				)
							A_      :		List[str]      =	retriever.retrieve(__lowerCamelCase			, n_docs=1				)
							self.assertTrue(out is not None				)


	def        _a      (self				):
				A_      :		int      =	1
				A_      :		Optional[int]      =	self.get_dummy_legacy_index_retriever()
				A_      :		Union[str, Any]      =	np.array(
				    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]			, dtype=np.floataa				)
				A_,       A_,       A_      :		Optional[int]      =	retriever.retrieve(__lowerCamelCase			, n_docs=__lowerCamelCase				)
				self.assertEqual(retrieved_doc_embeds.shape			, (2, n_docs, self.retrieval_vector_size)				)
				self.assertEqual(len(__lowerCamelCase				)			, 2				)
				self.assertEqual(sorted(doc_dicts[0]				)			, ["""text""", """title"""]				)
				self.assertEqual(len(doc_dicts[0]["""text"""]				)			, __lowerCamelCase				)
				self.assertEqual(doc_dicts[0]["""text"""][0]			, """bar"""				)  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]["""text"""][0]			, """foo"""				)  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()			, [[1], [0]]				)


	def        _a      (self				):
				A_      :		Optional[int]      =	self.get_dummy_legacy_index_retriever()
				with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(__lowerCamelCase				)
							A_      :		Dict      =	RagRetriever.from_pretrained(__lowerCamelCase				)
							self.assertIsInstance(__lowerCamelCase			, __lowerCamelCase				)
							A_      :		Any      =	np.array(
							    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]			, dtype=np.floataa				)
							A_      :		Union[str, Any]      =	retriever.retrieve(__lowerCamelCase			, n_docs=1				)
							self.assertTrue(out is not None				)


	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def        _a      (self				):
				import torch

				A_      :		List[Any]      =	1
				A_      :		Any      =	self.get_dummy_canonical_hf_index_retriever()
				A_      :		Any      =	[[5, 7], [10, 11]]
				A_      :		List[str]      =	np.array(
				    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]			, dtype=np.floataa				)
				A_      :		Optional[Any]      =	retriever(__lowerCamelCase			, __lowerCamelCase			, prefix=retriever.config.generator.prefix			, n_docs=__lowerCamelCase				)
				A_,       A_,       A_      :		str      =	(
				    out["""context_input_ids"""],
				    out["""context_attention_mask"""],
				    out["""retrieved_doc_embeds"""],
				)
				self.assertEqual(retrieved_doc_embeds.shape			, (2, n_docs, self.retrieval_vector_size)				)
				self.assertIsInstance(__lowerCamelCase			, __lowerCamelCase				)
				self.assertIsInstance(__lowerCamelCase			, __lowerCamelCase				)
				self.assertIsInstance(__lowerCamelCase			, np.ndarray				)

				A_      :		Optional[Any]      =	retriever(
				    __lowerCamelCase			, __lowerCamelCase			, prefix=retriever.config.generator.prefix			, n_docs=__lowerCamelCase			, return_tensors="""pt"""			, )
				A_,       A_,       A_,       A_      :		Optional[Any]      =	(  # noqa: F841
				    out["""context_input_ids"""],
				    out["""context_attention_mask"""],
				    out["""retrieved_doc_embeds"""],
				    out["""doc_ids"""],
				)
				self.assertEqual(retrieved_doc_embeds.shape			, (2, n_docs, self.retrieval_vector_size)				)
				self.assertIsInstance(__lowerCamelCase			, torch.Tensor				)
				self.assertIsInstance(__lowerCamelCase			, torch.Tensor				)
				self.assertIsInstance(__lowerCamelCase			, torch.Tensor				)


	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def        _a      (self				):
				A_      :		Optional[Any]      =	self.get_dpr_ctx_encoder_tokenizer()
				A_      :		Union[str, Any]      =	1
				A_      :		Any      =	self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase				)
				retriever.set_ctx_encoder_tokenizer(__lowerCamelCase				)

				A_      :		Optional[int]      =	[[5, 7], [10, 11]]
				A_      :		Union[str, Any]      =	np.array(
				    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]			, dtype=np.floataa				)
				A_      :		str      =	retriever(__lowerCamelCase			, __lowerCamelCase			, prefix=retriever.config.generator.prefix			, n_docs=__lowerCamelCase				)

				self.assertEqual(
				    len(__lowerCamelCase				)			, 6				)  # check whether the retriever output consist of 6 attributes including tokenized docs
				self.assertEqual(
				    all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""")				)			, __lowerCamelCase				)  # check for doc token related keys in dictionary.





import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__magic_name__  :  List[str]					=       logging.get_logger(__name__)  # pylint: disable=invalid-name





class     SCREAMING_SNAKE_CASE__		(_a			):
     def __init__(						self	:		List[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		List[Any]=7_68						):



       """simple docstring"""
       super().__init__(__lowerCamelCase						)
       lowerCAmelCase__										=      proj_size

       lowerCAmelCase__										=      CLIPVisionModel(__lowerCamelCase						)
       lowerCAmelCase__										=      PaintByExampleMapper(__lowerCamelCase						)
       lowerCAmelCase__										=      nn.LayerNorm(config.hidden_size						)
       lowerCAmelCase__										=      nn.Linear(config.hidden_size       ,						self.proj_size						)

       # uncondition for scaling
       lowerCAmelCase__										=      nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)



     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		str=False						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model(pixel_values=__lowerCamelCase						)
       lowerCAmelCase__										=      clip_output.pooler_output
       lowerCAmelCase__										=      self.mapper(latent_states[:, None]						)
       lowerCAmelCase__										=      self.final_layer_norm(__lowerCamelCase						)
       lowerCAmelCase__										=      self.proj_out(__lowerCamelCase						)
       if return_uncond_vector:
         return latent_states, self.uncond_vector

       return latent_states





class     SCREAMING_SNAKE_CASE__		(nn.Module			):
     def __init__(						self	:		Optional[Any]       ,						__lowerCamelCase	:		Tuple						):



       """simple docstring"""
       super().__init__()
       lowerCAmelCase__										=      (config.num_hidden_layers + 1) // 5
       lowerCAmelCase__										=      config.hidden_size
       lowerCAmelCase__										=      1
       lowerCAmelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						activation_fn='''gelu'''       ,						attention_bias=__lowerCamelCase						)
               for _ in range(__lowerCamelCase						)
           ]						)



     def        A__      (						self	:		str       ,						__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       for block in self.blocks:
         lowerCAmelCase__										=      block(__lowerCamelCase						)

       return hidden_states

