





'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
    if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import (
        VersatileDiffusionDualGuidedPipeline,
        VersatileDiffusionImageVariationPipeline,
        VersatileDiffusionPipeline,
        VersatileDiffusionTextToImagePipeline,
    )
else:
    from .modeling_text_unet import UNetFlatConditionModel
    from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
    from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
    from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
    from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline







'''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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  tempfile.mkdtemp()
        __UpperCamelCase       =  8

        # DPR tok
        __UpperCamelCase       =  [
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        __UpperCamelCase       =  os.path.join(self.tmpdirname							,	'dpr_tokenizer'	)
        os.makedirs(__A							,	exist_ok=__A	)
        __UpperCamelCase       =  os.path.join(__A							,	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
        __UpperCamelCase       =  [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        __UpperCamelCase       =  dict(zip(__A							,	range(len(__A	)	)	)	)
        __UpperCamelCase       =  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        __UpperCamelCase       =  {'unk_token': '<unk>'}

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






 def        _lowerCamelCase					(       self       :							Tuple	):
        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'dpr_tokenizer'	)	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'dpr_tokenizer'	)	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'bart_tokenizer'	)	)






 def        _lowerCamelCase					(       self       :							str	):
        shutil.rmtree(self.tmpdirname	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  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        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.get_dummy_dataset()
        __UpperCamelCase       =  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:
               __UpperCamelCase       =  dataset
               __UpperCamelCase       =  RagRetriever(
                   __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()							,	)
        return retriever






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							bool	):
        __UpperCamelCase       =  self.get_dummy_dataset()
        __UpperCamelCase       =  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size							,	question_encoder=DPRConfig().to_dict()							,	generator=BartConfig().to_dict()							,	index_name='custom'							,	)
        if from_disk:
               __UpperCamelCase       =  os.path.join(self.tmpdirname							,	'dataset'	)
               __UpperCamelCase       =  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
               __UpperCamelCase       =  RagRetriever(
                   __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()							,	)
        else:
               __UpperCamelCase       =  RagRetriever(
                   __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()							,	index=CustomHFIndex(config.retrieval_vector_size							,	__A	)							,	)
        return retriever






 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  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	)

        __UpperCamelCase       =  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'	)	)

        __UpperCamelCase       =  os.path.join(self.tmpdirname							,	'psgs_w100.tsv.pkl'	)
        __UpperCamelCase       =  {sample['id']: [sample['text'], sample['title']] for sample in dataset}
        pickle.dump(__A							,	open(__A							,	'wb'	)	)

        __UpperCamelCase       =  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size							,	question_encoder=DPRConfig().to_dict()							,	generator=BartConfig().to_dict()							,	index_name='legacy'							,	index_path=self.tmpdirname							,	)
        __UpperCamelCase       =  RagRetriever(
            __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()	)
        return retriever






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_canonical_hf_index_retriever()
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=__A	)
        self.assertEqual(retrieved_doc_embeds.shape							,	(2, n_docs, self.retrieval_vector_size)	)
        self.assertEqual(len(__A	)							,	2	)
        self.assertEqual(sorted(doc_dicts[0]	)							,	['embeddings', 'id', 'text', 'title']	)
        self.assertEqual(len(doc_dicts[0]['id']	)							,	__A	)
        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        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  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:
                      __UpperCamelCase       =  self.get_dummy_dataset()
                      retriever.save_pretrained(__A	)
                      __UpperCamelCase       =  RagRetriever.from_pretrained(__A	)
                      self.assertIsInstance(__A							,	__A	)
                      __UpperCamelCase       =  np.array(
                          [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
                      __UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=1	)
                      self.assertTrue(out is not None	)






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






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






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_legacy_index_retriever()
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=__A	)
        self.assertEqual(retrieved_doc_embeds.shape							,	(2, n_docs, self.retrieval_vector_size)	)
        self.assertEqual(len(__A	)							,	2	)
        self.assertEqual(sorted(doc_dicts[0]	)							,	['text', 'title']	)
        self.assertEqual(len(doc_dicts[0]['text']	)							,	__A	)
        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        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  self.get_dummy_legacy_index_retriever()
        with tempfile.TemporaryDirectory() as tmp_dirname:
               retriever.save_pretrained(__A	)
               __UpperCamelCase       =  RagRetriever.from_pretrained(__A	)
               self.assertIsInstance(__A							,	__A	)
               __UpperCamelCase       =  np.array(
                   [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
               __UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=1	)
               self.assertTrue(out is not None	)






 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        import torch

        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_canonical_hf_index_retriever()
        __UpperCamelCase       =  [[5, 7], [1_0, 1_1]]
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       =  retriever(__A							,	__A							,	prefix=retriever.config.generator.prefix							,	n_docs=__A	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  (
            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(__A							,	__A	)
        self.assertIsInstance(__A							,	__A	)
        self.assertIsInstance(__A							,	np.ndarray	)

        __UpperCamelCase       =  retriever(
            __A							,	__A							,	prefix=retriever.config.generator.prefix							,	n_docs=__A							,	return_tensors='pt'							,	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  (  # 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(__A							,	torch.Tensor	)
        self.assertIsInstance(__A							,	torch.Tensor	)
        self.assertIsInstance(__A							,	torch.Tensor	)






 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  self.get_dpr_ctx_encoder_tokenizer()
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
        retriever.set_ctx_encoder_tokenizer(__A	)

        __UpperCamelCase       =  [[5, 7], [1_0, 1_1]]
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       =  retriever(__A							,	__A							,	prefix=retriever.config.generator.prefix							,	n_docs=__A	)

        self.assertEqual(
            len(__A	)							,	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')	)							,	__A	)  # check for doc token related keys in dictionary.
