


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import (
    DPRContextEncoder,
    DPRContextEncoderTokenizerFast,
    HfArgumentParser,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


_UpperCAmelCase     :					str       =logging.getLogger(__name__)
torch.set_grad_enabled(False)
_UpperCAmelCase     :					str       ="""cuda""" if torch.cuda.is_available() else """cpu"""



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_=100	,				lowerCAmelCase_=" "	)->	List[str]:
				lowerCAmelCase_	: List[Any]      			=			text.split(lowerCAmelCase_	)
				return [character.join(text[i : i + n]	).strip() for i in range(0	,				len(lowerCAmelCase_	)	,				lowerCAmelCase_	)]



def      lowerCAmelCase	(						lowerCAmelCase_	)->	dict:
				lowerCAmelCase_			,					lowerCAmelCase_	: List[Any]      			=			[], []
				for title, text in zip(documents['''title''']	,				documents['''text''']	):
								if text is not None:
												for passage in split_text(lowerCAmelCase_	):
																titles.append(title if title is not None else ''''''	)
																texts.append(lowerCAmelCase_	)
				return {"title": titles, "text": texts}



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	dict:
				lowerCAmelCase_	: int      			=			ctx_tokenizer(
				    documents['''title''']	,				documents['''text''']	,				truncation=lowerCAmelCase_	,				padding='''longest'''	,				return_tensors='''pt'''	)['''input_ids''']
				lowerCAmelCase_	: Optional[int]      			=			ctx_encoder(input_ids.to(device=lowerCAmelCase_	)	,				return_dict=lowerCAmelCase_	).pooler_output
				return {"embeddings": embeddings.detach().cpu().numpy()}



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				)->	str:
				######################################
				logger.info('''Step 1 - Create the dataset'''	)
				######################################

				# The dataset needed for RAG must have three columns:
				# - title (string): title of the document
				# - text (string): text of a passage of the document
				# - embeddings (array of dimension d): DPR representation of the passage

				# Let's say you have documents in tab-separated csv files with columns "title" and "text"
				assert os.path.isfile(rag_example_args.csv_path	), "Please provide a valid path to a csv file"

				# You can load a Dataset object this way
				lowerCAmelCase_	: Union[str, Any]      			=			load_dataset(
				    '''csv'''	,				data_files=[rag_example_args.csv_path]	,				split='''train'''	,				delimiter='''\t'''	,				column_names=['''title''', '''text''']	)

				# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

				# Then split the documents into passages of 100 words
				lowerCAmelCase_	: List[str]      			=			dataset.map(lowerCAmelCase_	,				batched=lowerCAmelCase_	,				num_proc=processing_args.num_proc	)

				# And compute the embeddings
				lowerCAmelCase_	: Optional[Any]      			=			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	).to(device=lowerCAmelCase_	)
				lowerCAmelCase_	: Optional[int]      			=			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	)
				lowerCAmelCase_	: Union[str, Any]      			=			Features(
				    {'''text''': Value('''string'''	), '''title''': Value('''string'''	), '''embeddings''': Sequence(Value('''float32'''	)	)}	)  # optional, save as float32 instead of float64 to save space
				lowerCAmelCase_	: Any      			=			dataset.map(
				    partial(lowerCAmelCase_	,				ctx_encoder=lowerCAmelCase_	,				ctx_tokenizer=lowerCAmelCase_	)	,				batched=lowerCAmelCase_	,				batch_size=processing_args.batch_size	,				features=lowerCAmelCase_	,				)

				# And finally save your dataset
				lowerCAmelCase_	: Optional[Any]      			=			os.path.join(rag_example_args.output_dir	,				'''my_knowledge_dataset'''	)
				dataset.save_to_disk(lowerCAmelCase_	)
				# from datasets import load_from_disk
				# dataset = load_from_disk(passages_path)  # to reload the dataset

				######################################
				logger.info('''Step 2 - Index the dataset'''	)
				######################################

				# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
				lowerCAmelCase_	: Tuple      			=			faiss.IndexHNSWFlat(index_hnsw_args.d	,				index_hnsw_args.m	,				faiss.METRIC_INNER_PRODUCT	)
				dataset.add_faiss_index('''embeddings'''	,				custom_index=lowerCAmelCase_	)

				# And save the index
				lowerCAmelCase_	: Any      			=			os.path.join(rag_example_args.output_dir	,				'''my_knowledge_dataset_hnsw_index.faiss'''	)
				dataset.get_index('''embeddings'''	).save(lowerCAmelCase_	)
				# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

				######################################
				logger.info('''Step 3 - Load RAG'''	)
				######################################

				# Easy way to load the model
				lowerCAmelCase_	: Union[str, Any]      			=			RagRetriever.from_pretrained(
				    rag_example_args.rag_model_name	,				index_name='''custom'''	,				indexed_dataset=lowerCAmelCase_	)
				lowerCAmelCase_	: Tuple      			=			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name	,				retriever=lowerCAmelCase_	)
				lowerCAmelCase_	: str      			=			RagTokenizer.from_pretrained(rag_example_args.rag_model_name	)

				# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
				# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

				######################################
				logger.info('''Step 4 - Have fun'''	)
				######################################

				lowerCAmelCase_	: Dict      			=			rag_example_args.question or '''What does Moses\' rod turn into ?'''
				lowerCAmelCase_	: Union[str, Any]      			=			tokenizer.question_encoder(lowerCAmelCase_	,				return_tensors='''pt'''	)['''input_ids''']
				lowerCAmelCase_	: List[Any]      			=			model.generate(lowerCAmelCase_	)
				lowerCAmelCase_	: Tuple      			=			tokenizer.batch_decode(lowerCAmelCase_	,				skip_special_tokens=lowerCAmelCase_	)[0]
				logger.info('''Q: ''' + question	)
				logger.info('''A: ''' + generated_string	)







@dataclass
class 			snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					str   =							field(
						    default=str(Path(UpperCAmelCase__							).parent / """test_data""" / """my_knowledge_dataset.csv"""							),  metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""},  )
						SCREAMING_SNAKE_CASE__ :					Optional[str]   =							field(
						    default=UpperCAmelCase__,  metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""},  )
						SCREAMING_SNAKE_CASE__ :					str   =							field(
						    default="""facebook/rag-sequence-nq""",  metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""},  )
						SCREAMING_SNAKE_CASE__ :					str   =							field(
						    default="""facebook/dpr-ctx_encoder-multiset-base""",  metadata={
						        """help""": (
						            """The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"""
						            """ 'facebook/dpr-ctx_encoder-multiset-base'"""
						        )
						    },  )
						SCREAMING_SNAKE_CASE__ :					Optional[str]   =							field(
						    default=UpperCAmelCase__,  metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""},  )







@dataclass
class 			snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					Optional[int]   =							field(
						    default=UpperCAmelCase__,  metadata={
						        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
						    },  )
						SCREAMING_SNAKE_CASE__ :					int   =							field(
						    default=16,  metadata={
						        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
						    },  )







@dataclass
class 			snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					int   =							field(
						    default=768,  metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""},  )
						SCREAMING_SNAKE_CASE__ :					int   =							field(
						    default=128,  metadata={
						        """help""": (
						            """The number of bi-directional links created for every new element during the HNSW index construction."""
						        )
						    },  )


if __name__ == "__main__":
						logging.basicConfig(level=logging.WARNING)
						logger.setLevel(logging.INFO)

						_UpperCAmelCase     :					Optional[Any]       =HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
						_UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase     :					Dict       =parser.parse_args_into_dataclasses()
						with TemporaryDirectory() as tmp_dir:
												_UpperCAmelCase     :					Dict       =rag_example_args.output_dir or tmp_dir
												main(rag_example_args, processing_args, index_hnsw_args)



import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_UpperCAmelCase     :					List[Any]       ="""src/diffusers"""
_UpperCAmelCase     :					Dict       ="""docs/source/en"""
_UpperCAmelCase     :					List[str]       ="""."""



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	List[str]:
				with open(lowerCAmelCase_	,				'''r'''	,				encoding='''utf-8'''	,				newline='''\n'''	) as f:
								lowerCAmelCase_	: Any      			=			f.readlines()
				# Find the start prompt.
				lowerCAmelCase_	: Optional[Any]      			=			0
				while not lines[start_index].startswith(lowerCAmelCase_	):
								start_index += 1
				start_index += 1

				lowerCAmelCase_	: List[Any]      			=			start_index
				while not lines[end_index].startswith(lowerCAmelCase_	):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]	) <= 1:
								start_index += 1
				while len(lines[end_index]	) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]	), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
_UpperCAmelCase     :					int       ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase     :					Any       =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase     :					Optional[int]       =re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_UpperCAmelCase     :					Union[str, Any]       =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
_UpperCAmelCase     :					Dict       =importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
_UpperCAmelCase     :					Tuple       =spec.loader.load_module()



def      lowerCAmelCase	(						lowerCAmelCase_	)->	Dict:
				lowerCAmelCase_	: Any      			=			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''	,				lowerCAmelCase_	)
				return [m.group(0	) for m in matches]



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	Any:
				lowerCAmelCase_	: Any      			=			2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_	)
				lowerCAmelCase_	: List[str]      			=			(width - text_length) // 2
				lowerCAmelCase_	: str      			=			width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def      lowerCAmelCase	(						)->	Dict:
				lowerCAmelCase_	: Optional[int]      			=			diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase_	: Tuple      			=			{
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				lowerCAmelCase_	: str      			=			{name: config.replace('''ConfigMixin'''	,				''''''	) for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				lowerCAmelCase_	: Union[str, Any]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Dict      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Tuple      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Optional[int]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Any      			=			collections.defaultdict(lowerCAmelCase_	)

				# Let's lookup through all diffusers object (once).
				for attr_name in dir(lowerCAmelCase_	):
								lowerCAmelCase_	: Tuple      			=			None
								if attr_name.endswith('''Tokenizer'''	):
												lowerCAmelCase_	: int      			=			slow_tokenizers
												lowerCAmelCase_	: Optional[Any]      			=			attr_name[:-9]
								elif attr_name.endswith('''TokenizerFast'''	):
												lowerCAmelCase_	: List[Any]      			=			fast_tokenizers
												lowerCAmelCase_	: Dict      			=			attr_name[:-13]
								elif _re_tf_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Union[str, Any]      			=			tf_models
												lowerCAmelCase_	: Optional[int]      			=			_re_tf_models.match(lowerCAmelCase_	).groups()[0]
								elif _re_flax_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Optional[Any]      			=			flax_models
												lowerCAmelCase_	: Optional[int]      			=			_re_flax_models.match(lowerCAmelCase_	).groups()[0]
								elif _re_pt_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Tuple      			=			pt_models
												lowerCAmelCase_	: Tuple      			=			_re_pt_models.match(lowerCAmelCase_	).groups()[0]

								if lookup_dict is not None:
												while len(lowerCAmelCase_	) > 0:
																if attr_name in model_name_to_prefix.values():
																				lowerCAmelCase_	: Dict      			=			True
																				break
																# Try again after removing the last word in the name
																lowerCAmelCase_	: Tuple      			=			''''''.join(camel_case_split(lowerCAmelCase_	)[:-1]	)

    # Let's build that table!
				lowerCAmelCase_	: Union[str, Any]      			=			list(model_name_to_config.keys()	)
				model_names.sort(key=str.lower	)
				lowerCAmelCase_	: Tuple      			=			['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				lowerCAmelCase_	: Optional[Any]      			=			[len(lowerCAmelCase_	) + 2 for c in columns]
				lowerCAmelCase_	: List[Any]      			=			max([len(lowerCAmelCase_	) for name in model_names]	) + 2

				# Build the table per se
				lowerCAmelCase_	: List[str]      			=			'''|''' + '''|'''.join([_center_text(lowerCAmelCase_	,				lowerCAmelCase_	) for c, w in zip(lowerCAmelCase_	,				lowerCAmelCase_	)]	) + '''|\n'''
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]	) + "|\n"

				lowerCAmelCase_	: Tuple      			=			{True: '''✅''', False: '''❌'''}
				for name in model_names:
								lowerCAmelCase_	: Optional[int]      			=			model_name_to_prefix[name]
								lowerCAmelCase_	: int      			=			[
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(lowerCAmelCase_	,				lowerCAmelCase_	) for l, w in zip(lowerCAmelCase_	,				lowerCAmelCase_	)]	) + "|\n"
				return table



def      lowerCAmelCase	(						lowerCAmelCase_=False	)->	str:
				lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	: Optional[int]      			=			_find_text_in_file(
				    filename=os.path.join(lowerCAmelCase_	,				'''index.mdx'''	)	,				start_prompt='''<!--This table is updated automatically from the auto modules'''	,				end_prompt='''<!-- End table-->'''	,				)
				lowerCAmelCase_	: int      			=			get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowerCAmelCase_	,				'''index.mdx'''	)	,				'''w'''	,				encoding='''utf-8'''	,				newline='''\n'''	) as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:]	)
								else:
												raise ValueError(
												    '''The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'''	)


if __name__ == "__main__":
						_UpperCAmelCase     :					Optional[Any]       =argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_UpperCAmelCase     :					int       =parser.parse_args()

						check_model_table(args.fix_and_overwrite)