



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available



@dataclass
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  42


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .pipeline_text_to_video_synth import TextToVideoSDPipeline
  from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
  from .pipeline_text_to_video_zero import TextToVideoZeroPipeline











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     (	lowerCAmelCase__   ):




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    tempfile.mkdtemp()
    __lowerCamelCase													=    8

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

    __lowerCamelCase													=    os.path.join(self.tmpdirname	,				'''bart_tokenizer'''				)
    os.makedirs(__UpperCAmelCase	,				exist_ok=__UpperCAmelCase				)
    __lowerCamelCase													=    os.path.join(__UpperCAmelCase	,				BART_VOCAB_FILES_NAMES['''vocab_file''']				)
    __lowerCamelCase													=    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        lowerCamelCase      (						self				):



    '''simple docstring'''
    return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,				'''dpr_tokenizer'''				)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,				'''dpr_tokenizer'''				)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,				'''bart_tokenizer'''				)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    shutil.rmtree(self.tmpdirname				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    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				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_dataset()
    __lowerCamelCase													=    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:
       __lowerCamelCase													=    dataset
       __lowerCamelCase													=    RagRetriever(
           __UpperCAmelCase	,				question_encoder_tokenizer=self.get_dpr_tokenizer()	,				generator_tokenizer=self.get_bart_tokenizer()	,				)
    return retriever




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_dataset()
    __lowerCamelCase													=    RagConfig(
        retrieval_vector_size=self.retrieval_vector_size	,				question_encoder=DPRConfig().to_dict()	,				generator=BartConfig().to_dict()	,				index_name='''custom'''	,				)
    if from_disk:
       __lowerCamelCase													=    os.path.join(self.tmpdirname	,				'''dataset'''				)
       __lowerCamelCase													=    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
       __lowerCamelCase													=    RagRetriever(
           __UpperCAmelCase	,				question_encoder_tokenizer=self.get_dpr_tokenizer()	,				generator_tokenizer=self.get_bart_tokenizer()	,				)
    else:
       __lowerCamelCase													=    RagRetriever(
           __UpperCAmelCase	,				question_encoder_tokenizer=self.get_dpr_tokenizer()	,				generator_tokenizer=self.get_bart_tokenizer()	,				index=CustomHFIndex(config.retrieval_vector_size	,				__UpperCAmelCase				)	,				)
    return retriever




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    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				)

    __lowerCamelCase													=    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'''				)				)

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

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_canonical_hf_index_retriever()
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    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:
          __lowerCamelCase													=    self.get_dummy_dataset()
          retriever.save_pretrained(__UpperCAmelCase				)
          __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
          self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
          __lowerCamelCase													=    np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
          __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
          self.assertTrue(out is not None				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
       self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
       __lowerCamelCase													=    np.array(
           [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
       __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
       self.assertTrue(out is not None				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
       self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
       __lowerCamelCase													=    np.array(
           [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
       __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
       self.assertTrue(out is not None				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_legacy_index_retriever()
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    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]				)	,				['''text''', '''title''']				)
    self.assertEqual(len(doc_dicts[0]['''text''']				)	,				__UpperCAmelCase				)
    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				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_legacy_index_retriever()
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
       self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
       __lowerCamelCase													=    np.array(
           [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
       __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
       self.assertTrue(out is not None				)




 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_canonical_hf_index_retriever()
    __lowerCamelCase													=    [[5, 7], [10, 11]]
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase													=    retriever(__UpperCAmelCase	,				__UpperCAmelCase	,				prefix=retriever.config.generator.prefix	,				n_docs=__UpperCAmelCase				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    (
        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(__UpperCAmelCase	,				__UpperCAmelCase				)
    self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
    self.assertIsInstance(__UpperCAmelCase	,				np.ndarray				)

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




 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dpr_ctx_encoder_tokenizer()
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    retriever.set_ctx_encoder_tokenizer(__UpperCAmelCase				)

    __lowerCamelCase													=    [[5, 7], [10, 11]]
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase													=    retriever(__UpperCAmelCase	,				__UpperCAmelCase	,				prefix=retriever.config.generator.prefix	,				n_docs=__UpperCAmelCase				)

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






