





'''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 __a (   _snake_case			):





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __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(lowerCamelCase				,exist_ok=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       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
      __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(lowerCamelCase				,range(len(lowerCamelCase    )    )    )    )
      __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(lowerCamelCase				,exist_ok=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       os.path.join(lowerCamelCase				,BART_VOCAB_FILES_NAMES["""vocab_file"""]    )
      __SCREAMING_SNAKE_CASE      							=       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      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,"""dpr_tokenizer"""    )    )





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      shutil.rmtree(self.tmpdirname    )





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __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				:			List[Any]    ):

      '''simple docstring'''

      __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(
             lowerCamelCase				,question_encoder_tokenizer=self.get_dpr_tokenizer()				,generator_tokenizer=self.get_bart_tokenizer()				,)
      return retriever





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,lowerCamelCase				:			bool    ):

      '''simple docstring'''

      __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(
             lowerCamelCase				,question_encoder_tokenizer=self.get_dpr_tokenizer()				,generator_tokenizer=self.get_bart_tokenizer()				,)
      else:
         __SCREAMING_SNAKE_CASE      							=       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      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __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(lowerCamelCase				,open(lowerCamelCase				,"""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(
          lowerCamelCase				,question_encoder_tokenizer=self.get_dpr_tokenizer()				,generator_tokenizer=self.get_bart_tokenizer()    )
      return retriever





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __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(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      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __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(lowerCamelCase    )
            __SCREAMING_SNAKE_CASE      							=       RagRetriever.from_pretrained(lowerCamelCase    )
            self.assertIsInstance(lowerCamelCase				,lowerCamelCase    )
            __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(lowerCamelCase				,n_docs=1    )
            self.assertTrue(out is not None    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase    )
      __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(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      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase    )
      with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       RagRetriever.from_pretrained(lowerCamelCase    )
         self.assertIsInstance(lowerCamelCase				,lowerCamelCase    )
         __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(lowerCamelCase				,n_docs=1    )
         self.assertTrue(out is not None    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase    )
      __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(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      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase    )
      with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       RagRetriever.from_pretrained(lowerCamelCase    )
         self.assertIsInstance(lowerCamelCase				,lowerCamelCase    )
         __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(lowerCamelCase				,n_docs=1    )
         self.assertTrue(out is not None    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __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(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      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_legacy_index_retriever()
      with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       RagRetriever.from_pretrained(lowerCamelCase    )
         self.assertIsInstance(lowerCamelCase				,lowerCamelCase    )
         __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(lowerCamelCase				,n_docs=1    )
         self.assertTrue(out is not None    )





   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      import torch

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

      __SCREAMING_SNAKE_CASE      							=       retriever(
          lowerCamelCase				,lowerCamelCase				,prefix=retriever.config.generator.prefix				,n_docs=lowerCamelCase				,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(lowerCamelCase				,torch.Tensor    )
      self.assertIsInstance(lowerCamelCase				,torch.Tensor    )
      self.assertIsInstance(lowerCamelCase				,torch.Tensor    )





   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __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=lowerCamelCase    )
      retriever.set_ctx_encoder_tokenizer(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       [[5, 7], [10, 11]]
      __SCREAMING_SNAKE_CASE      							=       np.array(
          [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]				,dtype=np.floataa    )
      __SCREAMING_SNAKE_CASE      							=       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 flax.linen as nn
import jax
import jax.numpy as jnp







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[Any]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       hidden_states.shape
      __SCREAMING_SNAKE_CASE      							=       jax.image.resize(
          lowerCamelCase				,shape=(batch, height * 2, width * 2, channels)				,method="""nearest"""				,)
      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(2, 2)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : int	      =	None
   __UpperCamelCase   : float	      =	0.0
   __UpperCamelCase   : bool	      =	None
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.in_channels if self.out_channels is None else self.out_channels

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       nn.Dense(lowerCamelCase				,dtype=self.dtype    )

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Dropout(self.dropout_prob    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

      __SCREAMING_SNAKE_CASE      							=       None
      if use_nin_shortcut:
         __SCREAMING_SNAKE_CASE      							=       nn.Conv(
             lowerCamelCase				,kernel_size=(1, 1)				,strides=(1, 1)				,padding="""VALID"""				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			Union[str, Any]=True    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       hidden_states
      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       self.time_emb_proj(nn.swish(lowerCamelCase    )    )
      __SCREAMING_SNAKE_CASE      							=       jnp.expand_dims(jnp.expand_dims(lowerCamelCase				,1    )				,1    )
      __SCREAMING_SNAKE_CASE      							=       hidden_states + temb

      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.dropout(lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      if self.conv_shortcut is not None:
         __SCREAMING_SNAKE_CASE      							=       self.conv_shortcut(lowerCamelCase    )

      return hidden_states + residual





