


'''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   (       a__		):




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   int       				=       tempfile.mkdtemp()
								UpperCAmelCase__					:   Optional[int]       				=       8

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

								UpperCAmelCase__					:   List[Any]       				=       os.path.join(self.tmpdirname							,							"""bart_tokenizer""")
								os.makedirs(_lowerCamelCase							,							exist_ok=_lowerCamelCase)
								UpperCAmelCase__					:   List[str]       				=       os.path.join(_lowerCamelCase							,							BART_VOCAB_FILES_NAMES["""vocab_file"""])
								UpperCAmelCase__					:   Any       				=       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 			snake_case__  ( self):
								return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,							"""dpr_tokenizer"""))




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




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




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




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       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 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.get_dummy_dataset()
								UpperCAmelCase__					:   Any       				=       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__					:   List[Any]       				=       dataset
											UpperCAmelCase__					:   Optional[Any]       				=       RagRetriever(
											    _lowerCamelCase							,							question_encoder_tokenizer=self.get_dpr_tokenizer()							,							generator_tokenizer=self.get_bart_tokenizer()							,							)
								return retriever




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.get_dummy_dataset()
								UpperCAmelCase__					:   Any       				=       RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size							,							question_encoder=DPRConfig().to_dict()							,							generator=BartConfig().to_dict()							,							index_name="""custom"""							,							)
								if from_disk:
											UpperCAmelCase__					:   Any       				=       os.path.join(self.tmpdirname							,							"""dataset""")
											UpperCAmelCase__					:   Optional[Any]       				=       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__					:   Dict       				=       RagRetriever(
											    _lowerCamelCase							,							question_encoder_tokenizer=self.get_dpr_tokenizer()							,							generator_tokenizer=self.get_bart_tokenizer()							,							)
								else:
											UpperCAmelCase__					:   List[str]       				=       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 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       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__					:   str       				=       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__					:   int       				=       os.path.join(self.tmpdirname							,							"""psgs_w100.tsv.pkl""")
								UpperCAmelCase__					:   Optional[Any]       				=       {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
								pickle.dump(_lowerCamelCase							,							open(_lowerCamelCase							,							"""wb"""))

								UpperCAmelCase__					:   List[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							,							)
								UpperCAmelCase__					:   Union[str, Any]       				=       RagRetriever(
								    _lowerCamelCase							,							question_encoder_tokenizer=self.get_dpr_tokenizer()							,							generator_tokenizer=self.get_bart_tokenizer())
								return retriever




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




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




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




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




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

								UpperCAmelCase__					:   Dict       				=       1
								UpperCAmelCase__					:   str       				=       self.get_dummy_canonical_hf_index_retriever()
								UpperCAmelCase__					:   Optional[Any]       				=       [[5, 7], [10, 11]]
								UpperCAmelCase__					:   Optional[Any]       				=       np.array(
								    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
								UpperCAmelCase__					:   Optional[Any]       				=       retriever(_lowerCamelCase							,							_lowerCamelCase							,							prefix=retriever.config.generator.prefix							,							n_docs=_lowerCamelCase)
								UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Any       				=       (
								    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)

								UpperCAmelCase__					:   Optional[int]       				=       retriever(
								    _lowerCamelCase							,							_lowerCamelCase							,							prefix=retriever.config.generator.prefix							,							n_docs=_lowerCamelCase							,							return_tensors="""pt"""							,							)
								UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   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 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       self.get_dpr_ctx_encoder_tokenizer()
								UpperCAmelCase__					:   Union[str, Any]       				=       1
								UpperCAmelCase__					:   List[Any]       				=       self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCamelCase)
								retriever.set_ctx_encoder_tokenizer(_lowerCamelCase)

								UpperCAmelCase__					:   Tuple       				=       [[5, 7], [10, 11]]
								UpperCAmelCase__					:   Optional[int]       				=       np.array(
								    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
								UpperCAmelCase__					:   Optional[Any]       				=       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.



'''simple docstring'''



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)


class 						_snake_case   (       unittest.TestCase		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=13							,							_lowerCamelCase=7							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=99							,							_lowerCamelCase=32							,							_lowerCamelCase=5							,							_lowerCamelCase=4							,							_lowerCamelCase=37							,							_lowerCamelCase="gelu"							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=512							,							_lowerCamelCase=16							,							_lowerCamelCase=2							,							_lowerCamelCase=0.02							,							_lowerCamelCase=4							,							):
								UpperCAmelCase__					:   Union[str, Any]       				=       parent
								UpperCAmelCase__					:   str       				=       batch_size
								UpperCAmelCase__					:   Tuple       				=       seq_length
								UpperCAmelCase__					:   Any       				=       is_training
								UpperCAmelCase__					:   Tuple       				=       use_attention_mask
								UpperCAmelCase__					:   Optional[Any]       				=       use_token_type_ids
								UpperCAmelCase__					:   Optional[Any]       				=       use_labels
								UpperCAmelCase__					:   str       				=       vocab_size
								UpperCAmelCase__					:   Dict       				=       hidden_size
								UpperCAmelCase__					:   int       				=       num_hidden_layers
								UpperCAmelCase__					:   Optional[int]       				=       num_attention_heads
								UpperCAmelCase__					:   Optional[int]       				=       intermediate_size
								UpperCAmelCase__					:   Tuple       				=       hidden_act
								UpperCAmelCase__					:   Optional[Any]       				=       hidden_dropout_prob
								UpperCAmelCase__					:   Union[str, Any]       				=       attention_probs_dropout_prob
								UpperCAmelCase__					:   List[Any]       				=       max_position_embeddings
								UpperCAmelCase__					:   Optional[int]       				=       type_vocab_size
								UpperCAmelCase__					:   str       				=       type_sequence_label_size
								UpperCAmelCase__					:   Optional[int]       				=       initializer_range
								UpperCAmelCase__					:   str       				=       num_choices




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

								UpperCAmelCase__					:   Union[str, Any]       				=       None
								if self.use_attention_mask:
											UpperCAmelCase__					:   Tuple       				=       random_attention_mask([self.batch_size, self.seq_length])

								UpperCAmelCase__					:   Dict       				=       None
								if self.use_token_type_ids:
											UpperCAmelCase__					:   Any       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size)

								UpperCAmelCase__					:   Optional[Any]       				=       RoFormerConfig(
								    vocab_size=self.vocab_size							,							hidden_size=self.hidden_size							,							num_hidden_layers=self.num_hidden_layers							,							num_attention_heads=self.num_attention_heads							,							intermediate_size=self.intermediate_size							,							hidden_act=self.hidden_act							,							hidden_dropout_prob=self.hidden_dropout_prob							,							attention_probs_dropout_prob=self.attention_probs_dropout_prob							,							max_position_embeddings=self.max_position_embeddings							,							type_vocab_size=self.type_vocab_size							,							is_decoder=_lowerCamelCase							,							initializer_range=self.initializer_range							,							)

								return config, input_ids, token_type_ids, attention_mask




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       self.prepare_config_and_inputs()
								UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       config_and_inputs
								UpperCAmelCase__					:   Dict       				=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict


@require_flax
class 						_snake_case   (       a__    ,				unittest.TestCase		):
					lowerCAmelCase :Optional[int]						=   True

					lowerCAmelCase :Any						=   (
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       FlaxRoFormerModelTester(self)




					@slow
					def 			snake_case__  ( self):
								for model_class_name in self.all_model_classes:
											UpperCAmelCase__					:   List[str]       				=       model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,							from_pt=_lowerCamelCase)
											UpperCAmelCase__					:   Dict       				=       model(np.ones((1, 1)))
											self.assertIsNotNone(_lowerCamelCase)


@require_flax
class 						_snake_case   (       unittest.TestCase		):




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""")
								UpperCAmelCase__					:   int       				=       jnp.array([[0, 1, 2, 3, 4, 5]])
								UpperCAmelCase__					:   Optional[int]       				=       model(_lowerCamelCase)[0]

								UpperCAmelCase__					:   Union[str, Any]       				=       5_0000

								UpperCAmelCase__					:   Any       				=       (1, 6, vocab_size)
								self.assertEqual(output.shape							,							_lowerCamelCase)

								UpperCAmelCase__					:   Union[str, Any]       				=       jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]])

								self.assertTrue(jnp.allclose(output[:, :3, :3]							,							_lowerCamelCase							,							atol=1e-4))