


"""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  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				tempfile.mkdtemp()
						__SCREAMING_SNAKE_CASE						   =				8

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

						__SCREAMING_SNAKE_CASE						   =				os.path.join(self.tmpdirname			,			"bart_tokenizer"   )
						os.makedirs(UpperCAmelCase__			,			exist_ok=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				os.path.join(UpperCAmelCase__			,			BART_VOCAB_FILES_NAMES["vocab_file"]   )
						__SCREAMING_SNAKE_CASE						   =				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   UpperCAmelCase_      ( self			:				List[str]   )       ->	DPRQuestionEncoderTokenizer:
						return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,			"dpr_tokenizer"   )   )




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	DPRContextEncoderTokenizer:
						return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,			"dpr_tokenizer"   )   )




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	BartTokenizer:
						return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,			"bart_tokenizer"   )   )




	def   UpperCAmelCase_      ( self			:				int   )       ->	Union[str, Any]:
						shutil.rmtree(self.tmpdirname   )




	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				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   UpperCAmelCase_      ( self			:				Any   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_dataset()
						__SCREAMING_SNAKE_CASE						   =				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:
											__SCREAMING_SNAKE_CASE						   =				dataset
											__SCREAMING_SNAKE_CASE						   =				RagRetriever(
											    UpperCAmelCase__			,			question_encoder_tokenizer=self.get_dpr_tokenizer()			,			generator_tokenizer=self.get_bart_tokenizer()			,			)
						return retriever




	def   UpperCAmelCase_      ( self			:				int			,			UpperCAmelCase__			:				bool   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_dataset()
						__SCREAMING_SNAKE_CASE						   =				RagConfig(
						    retrieval_vector_size=self.retrieval_vector_size			,			question_encoder=DPRConfig().to_dict()			,			generator=BartConfig().to_dict()			,			index_name="custom"			,			)
						if from_disk:
											__SCREAMING_SNAKE_CASE						   =				os.path.join(self.tmpdirname			,			"dataset"   )
											__SCREAMING_SNAKE_CASE						   =				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
											__SCREAMING_SNAKE_CASE						   =				RagRetriever(
											    UpperCAmelCase__			,			question_encoder_tokenizer=self.get_dpr_tokenizer()			,			generator_tokenizer=self.get_bart_tokenizer()			,			)
						else:
											__SCREAMING_SNAKE_CASE						   =				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   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				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   )

						__SCREAMING_SNAKE_CASE						   =				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"   )   )

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

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




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				1
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_canonical_hf_index_retriever()
						__SCREAMING_SNAKE_CASE						   =				np.array(
						    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,			dtype=np.floataa   )
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				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   UpperCAmelCase_      ( self			:				Dict   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				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:
																__SCREAMING_SNAKE_CASE						   =				self.get_dummy_dataset()
																retriever.save_pretrained(UpperCAmelCase__   )
																__SCREAMING_SNAKE_CASE						   =				RagRetriever.from_pretrained(UpperCAmelCase__   )
																self.assertIsInstance(UpperCAmelCase__			,			UpperCAmelCase__   )
																__SCREAMING_SNAKE_CASE						   =				np.array(
																    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,			dtype=np.floataa   )
																__SCREAMING_SNAKE_CASE						   =				retriever.retrieve(UpperCAmelCase__			,			n_docs=1   )
																self.assertTrue(out is not None   )




	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				1
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				np.array(
						    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,			dtype=np.floataa   )
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				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   UpperCAmelCase_      ( self			:				Tuple   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__   )
						with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				RagRetriever.from_pretrained(UpperCAmelCase__   )
											self.assertIsInstance(UpperCAmelCase__			,			UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				np.array(
											    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,			dtype=np.floataa   )
											__SCREAMING_SNAKE_CASE						   =				retriever.retrieve(UpperCAmelCase__			,			n_docs=1   )
											self.assertTrue(out is not None   )




	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				1
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				np.array(
						    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,			dtype=np.floataa   )
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				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   UpperCAmelCase_      ( self			:				int   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__   )
						with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				RagRetriever.from_pretrained(UpperCAmelCase__   )
											self.assertIsInstance(UpperCAmelCase__			,			UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				np.array(
											    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,			dtype=np.floataa   )
											__SCREAMING_SNAKE_CASE						   =				retriever.retrieve(UpperCAmelCase__			,			n_docs=1   )
											self.assertTrue(out is not None   )




	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				1
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_legacy_index_retriever()
						__SCREAMING_SNAKE_CASE						   =				np.array(
						    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,			dtype=np.floataa   )
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				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   UpperCAmelCase_      ( self			:				str   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_legacy_index_retriever()
						with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				RagRetriever.from_pretrained(UpperCAmelCase__   )
											self.assertIsInstance(UpperCAmelCase__			,			UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				np.array(
											    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,			dtype=np.floataa   )
											__SCREAMING_SNAKE_CASE						   =				retriever.retrieve(UpperCAmelCase__			,			n_docs=1   )
											self.assertTrue(out is not None   )




	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def   UpperCAmelCase_      ( self			:				Dict   )       ->	int:
						import torch

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

						__SCREAMING_SNAKE_CASE						   =				retriever(
						    UpperCAmelCase__			,			UpperCAmelCase__			,			prefix=retriever.config.generator.prefix			,			n_docs=UpperCAmelCase__			,			return_tensors="pt"			,			)
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				(  # 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   UpperCAmelCase_      ( self			:				str   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				self.get_dpr_ctx_encoder_tokenizer()
						__SCREAMING_SNAKE_CASE						   =				1
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__   )
						retriever.set_ctx_encoder_tokenizer(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				[[5, 7], [1_0, 1_1]]
						__SCREAMING_SNAKE_CASE						   =				np.array(
						    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,			dtype=np.floataa   )
						__SCREAMING_SNAKE_CASE						   =				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.




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__				:							str						=     {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     ['''RoFormerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     [
		    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoFormerForCausalLM''',
		    '''RoFormerForMaskedLM''',
		    '''RoFormerForMultipleChoice''',
		    '''RoFormerForQuestionAnswering''',
		    '''RoFormerForSequenceClassification''',
		    '''RoFormerForTokenClassification''',
		    '''RoFormerLayer''',
		    '''RoFormerModel''',
		    '''RoFormerPreTrainedModel''',
		    '''load_tf_weights_in_roformer''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							List[Any]						=     [
		    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRoFormerForCausalLM''',
		    '''TFRoFormerForMaskedLM''',
		    '''TFRoFormerForMultipleChoice''',
		    '''TFRoFormerForQuestionAnswering''',
		    '''TFRoFormerForSequenceClassification''',
		    '''TFRoFormerForTokenClassification''',
		    '''TFRoFormerLayer''',
		    '''TFRoFormerModel''',
		    '''TFRoFormerPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							Tuple						=     [
		    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxRoFormerForMaskedLM''',
		    '''FlaxRoFormerForMultipleChoice''',
		    '''FlaxRoFormerForQuestionAnswering''',
		    '''FlaxRoFormerForSequenceClassification''',
		    '''FlaxRoFormerForTokenClassification''',
		    '''FlaxRoFormerModel''',
		    '''FlaxRoFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a__				:							int						=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
