
"""simple docstring"""
import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			RobertaTokenizer
     A__      =			RobertaTokenizerFast
     A__      =			True
     A__      =			{"""cls_token""": """<s>"""}
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       _SCREAMING_SNAKE_CASE   :				Any							    = dict(zip(snake_case__  ,     range(len(snake_case__				)				)				)				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       _SCREAMING_SNAKE_CASE   :				Dict							    = {"unk_token": "<unk>"}

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES["vocab_file"]				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES["merges_file"]				)
       with open(self.vocab_file  ,     "w"  ,     encoding="utf-8"				) as fp:
         fp.write(json.dumps(snake_case__				) + "\n"				)
       with open(self.merges_file  ,     "w"  ,     encoding="utf-8"				) as fp:
         fp.write("\n".join(snake_case__				)				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     **snake_case__				):




       """simple docstring"""


       kwargs.update(self.special_tokens_map				)
       return self.tokenizer_class.from_pretrained(self.tmpdirname  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     **snake_case__				):




       """simple docstring"""


       kwargs.update(self.special_tokens_map				)
       return RobertaTokenizerFast.from_pretrained(self.tmpdirname  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = "lower newer"
       _SCREAMING_SNAKE_CASE   :				int							    = "lower newer"
       return input_text, output_text
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.tokenizer_class(self.vocab_file  ,     self.merges_file  ,     **self.special_tokens_map				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = "lower newer"
       _SCREAMING_SNAKE_CASE   :				List[str]							    = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = tokenizer.tokenize(snake_case__				)  # , add_prefix_space=True)
       self.assertListEqual(snake_case__  ,     snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = tokens + [tokenizer.unk_token]
       _SCREAMING_SNAKE_CASE   :				str							    = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.get_tokenizer()

       self.assertListEqual(tokenizer.encode("Hello world!"  ,     add_special_tokens=snake_case__				)  ,     [0, 31414, 232, 328, 2]				)
       self.assertListEqual(
           tokenizer.encode("Hello world! cécé herlolip 418"  ,     add_special_tokens=snake_case__				)  ,     [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]  ,     )
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.tokenizer_class.from_pretrained("roberta-base"				)

       _SCREAMING_SNAKE_CASE   :				str							    = tokenizer.encode("sequence builders"  ,     add_special_tokens=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				int							    = tokenizer.encode("multi-sequence build"  ,     add_special_tokens=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = tokenizer.encode(
           "sequence builders"  ,     add_special_tokens=snake_case__  ,     add_prefix_space=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = tokenizer.encode(
           "sequence builders"  ,     "multi-sequence build"  ,     add_special_tokens=snake_case__  ,     add_prefix_space=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				str							    = tokenizer.build_inputs_with_special_tokens(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = tokenizer.build_inputs_with_special_tokens(snake_case__  ,     snake_case__				)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = self.get_tokenizer()

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = "Encode this sequence."
       _SCREAMING_SNAKE_CASE   :				List[str]							    = tokenizer.byte_encoder[" ".encode("utf-8"				)[0]]

       # Testing encoder arguments
       _SCREAMING_SNAKE_CASE   :				Any							    = tokenizer.encode(snake_case__  ,     add_special_tokens=snake_case__  ,     add_prefix_space=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
       self.assertNotEqual(snake_case__  ,     snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Any							    = tokenizer.encode(snake_case__  ,     add_special_tokens=snake_case__  ,     add_prefix_space=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
       self.assertEqual(snake_case__  ,     snake_case__				)

       tokenizer.add_special_tokens({"bos_token": "<s>"}				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tokenizer.encode(snake_case__  ,     add_special_tokens=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = tokenizer.convert_ids_to_tokens(encoded[1]				)[0]
       self.assertNotEqual(snake_case__  ,     snake_case__				)

       # Testing spaces after special tokens
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = "<mask>"
       tokenizer.add_special_tokens(
           {"mask_token": AddedToken(snake_case__  ,     lstrip=snake_case__  ,     rstrip=snake_case__				)}				)  # mask token has a left space
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tokenizer.convert_tokens_to_ids(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = "Encode <mask> sequence"
       _SCREAMING_SNAKE_CASE   :				str							    = "Encode <mask>sequence"

       _SCREAMING_SNAKE_CASE   :				str							    = tokenizer.encode(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = encoded.index(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
       self.assertEqual(snake_case__  ,     snake_case__				)

       _SCREAMING_SNAKE_CASE   :				str							    = tokenizer.encode(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				str							    = encoded.index(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
       self.assertNotEqual(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       pass
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
           _SCREAMING_SNAKE_CASE   :				List[str]							    = self.rust_tokenizer_class.from_pretrained(snake_case__  ,     **snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Tuple							    = self.tokenizer_class.from_pretrained(snake_case__  ,     **snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Any							    = "A, <mask> AllenNLP sentence."
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tokenizer_r.encode_plus(snake_case__  ,     add_special_tokens=snake_case__  ,     return_token_type_ids=snake_case__				)
           _SCREAMING_SNAKE_CASE   :				str							    = tokenizer_p.encode_plus(snake_case__  ,     add_special_tokens=snake_case__  ,     return_token_type_ids=snake_case__				)

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r["token_type_ids"]				)  ,     sum(tokens_p["token_type_ids"]				)				)

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r["attention_mask"]				) / len(tokens_r["attention_mask"]				)  ,     sum(tokens_p["attention_mask"]				) / len(tokens_p["attention_mask"]				)  ,     )

           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]				)
           _SCREAMING_SNAKE_CASE   :				int							    = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]				)

           # Rust correctly handles the space before the mask while python doesnt
           self.assertSequenceEqual(tokens_p["input_ids"]  ,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]				)
           self.assertSequenceEqual(tokens_r["input_ids"]  ,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]				)

           self.assertSequenceEqual(
               snake_case__  ,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]				)
           self.assertSequenceEqual(
               snake_case__  ,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for trim_offsets, add_prefix_space in itertools.product([True, False]  ,     repeat=2				):
         _SCREAMING_SNAKE_CASE   :				str							    = self.rust_tokenizer_class.from_pretrained(
             self.tmpdirname  ,     use_fast=snake_case__  ,     add_prefix_space=snake_case__  ,     trim_offsets=snake_case__				)

         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()				)
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()				)

         self.assertEqual(pre_tokenizer_state["add_prefix_space"]  ,     snake_case__				)

         self.assertEqual(post_processor_state["add_prefix_space"]  ,     snake_case__				)
         self.assertEqual(post_processor_state["trim_offsets"]  ,     snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
           _SCREAMING_SNAKE_CASE   :				Tuple							    = F'''{text_of_1_token} {text_of_1_token}'''

           _SCREAMING_SNAKE_CASE   :				str							    = self.rust_tokenizer_class.from_pretrained(
               snake_case__  ,     use_fast=snake_case__  ,     add_prefix_space=snake_case__  ,     trim_offsets=snake_case__				)
           _SCREAMING_SNAKE_CASE   :				List[str]							    = tokenizer_r(snake_case__  ,     return_offsets_mapping=snake_case__  ,     add_special_tokens=snake_case__				)
           self.assertEqual(encoding.offset_mapping[0]  ,     (0, len(snake_case__				))				)
           self.assertEqual(
               encoding.offset_mapping[1]  ,     (len(snake_case__				) + 1, len(snake_case__				) + 1 + len(snake_case__				))  ,     )

           _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.rust_tokenizer_class.from_pretrained(
               snake_case__  ,     use_fast=snake_case__  ,     add_prefix_space=snake_case__  ,     trim_offsets=snake_case__				)
           _SCREAMING_SNAKE_CASE   :				List[str]							    = tokenizer_r(snake_case__  ,     return_offsets_mapping=snake_case__  ,     add_special_tokens=snake_case__				)
           self.assertEqual(encoding.offset_mapping[0]  ,     (0, len(snake_case__				))				)
           self.assertEqual(
               encoding.offset_mapping[1]  ,     (len(snake_case__				) + 1, len(snake_case__				) + 1 + len(snake_case__				))  ,     )

           _SCREAMING_SNAKE_CASE   :				Any							    = self.rust_tokenizer_class.from_pretrained(
               snake_case__  ,     use_fast=snake_case__  ,     add_prefix_space=snake_case__  ,     trim_offsets=snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Dict							    = tokenizer_r(snake_case__  ,     return_offsets_mapping=snake_case__  ,     add_special_tokens=snake_case__				)
           self.assertEqual(encoding.offset_mapping[0]  ,     (0, len(snake_case__				))				)
           self.assertEqual(
               encoding.offset_mapping[1]  ,     (len(snake_case__				), len(snake_case__				) + 1 + len(snake_case__				))  ,     )

           _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.rust_tokenizer_class.from_pretrained(
               snake_case__  ,     use_fast=snake_case__  ,     add_prefix_space=snake_case__  ,     trim_offsets=snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tokenizer_r(snake_case__  ,     return_offsets_mapping=snake_case__  ,     add_special_tokens=snake_case__				)
           self.assertEqual(encoding.offset_mapping[0]  ,     (0, len(snake_case__				))				)
           self.assertEqual(
               encoding.offset_mapping[1]  ,     (len(snake_case__				), len(snake_case__				) + 1 + len(snake_case__				))  ,     )

           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = F''' {text}'''

           # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
           #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
           # )
           # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
           # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
           # self.assertEqual(
           #     encoding.offset_mapping[1],
           #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
           # )

           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.rust_tokenizer_class.from_pretrained(
               snake_case__  ,     use_fast=snake_case__  ,     add_prefix_space=snake_case__  ,     trim_offsets=snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Dict							    = tokenizer_r(snake_case__  ,     return_offsets_mapping=snake_case__  ,     add_special_tokens=snake_case__				)
           self.assertEqual(encoding.offset_mapping[0]  ,     (1, 1 + len(snake_case__				))				)
           self.assertEqual(
               encoding.offset_mapping[1]  ,     (1 + len(snake_case__				) + 1, 1 + len(snake_case__				) + 1 + len(snake_case__				))  ,     )

           _SCREAMING_SNAKE_CASE   :				List[str]							    = self.rust_tokenizer_class.from_pretrained(
               snake_case__  ,     use_fast=snake_case__  ,     add_prefix_space=snake_case__  ,     trim_offsets=snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tokenizer_r(snake_case__  ,     return_offsets_mapping=snake_case__  ,     add_special_tokens=snake_case__				)
           self.assertEqual(encoding.offset_mapping[0]  ,     (0, 1 + len(snake_case__				))				)
           self.assertEqual(
               encoding.offset_mapping[1]  ,     (1 + len(snake_case__				), 1 + len(snake_case__				) + 1 + len(snake_case__				))  ,     )

           _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.rust_tokenizer_class.from_pretrained(
               snake_case__  ,     use_fast=snake_case__  ,     add_prefix_space=snake_case__  ,     trim_offsets=snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Any							    = tokenizer_r(snake_case__  ,     return_offsets_mapping=snake_case__  ,     add_special_tokens=snake_case__				)
           self.assertEqual(encoding.offset_mapping[0]  ,     (0, 1 + len(snake_case__				))				)
           self.assertEqual(
               encoding.offset_mapping[1]  ,     (1 + len(snake_case__				), 1 + len(snake_case__				) + 1 + len(snake_case__				))  ,     )





"""simple docstring"""
from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)
@flax.struct.dataclass
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			42
@flax_register_to_config
class 			UpperCamelCase      (							nn.Module      ,       __SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE  ):
     A__      =			32
     A__      =			4
     A__      =			4
     A__      =			(
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "DownBlock2D",
     )
     A__      =			("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
     A__      =			False
     A__      =			(320, 640, 1280, 1280)
     A__      =			2
     A__      =			8
     A__      =			None
     A__      =			1280
     A__      =			0.0
     A__      =			False
     A__      =			jnp.floataa
     A__      =			True
     A__      =			0
     A__      =			False
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = (1, self.in_channels, self.sample_size, self.sample_size)
       _SCREAMING_SNAKE_CASE   :				int							    = jnp.zeros(snake_case__  ,     dtype=jnp.floataa				)
       _SCREAMING_SNAKE_CASE   :				str							    = jnp.ones((1,)  ,     dtype=jnp.intaa				)
       _SCREAMING_SNAKE_CASE   :				Any							    = jnp.zeros((1, 1, self.cross_attention_dim)  ,     dtype=jnp.floataa				)

       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Tuple							    = jax.random.split(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = {"params": params_rng, "dropout": dropout_rng}

       return self.init(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)["params"]
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.block_out_channels
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = block_out_channels[0] * 4

       if self.num_attention_heads is not None:
         raise ValueError(
             "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."				)

       # If `num_attention_heads` is not defined (which is the case for most models)
       # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
       # The reason for this behavior is to correct for incorrectly named variables that were introduced
       # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
       # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
       # which is why we correct for the naming here.
       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.num_attention_heads or self.attention_head_dim

       # input
       _SCREAMING_SNAKE_CASE   :				int							    = nn.Conv(
           block_out_channels[0]  ,     kernel_size=(3, 3)  ,     strides=(1, 1)  ,     padding=((1, 1), (1, 1))  ,     dtype=self.dtype  ,     )

       # time
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = FlaxTimesteps(
           block_out_channels[0]  ,     flip_sin_to_cos=self.flip_sin_to_cos  ,     freq_shift=self.config.freq_shift				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = FlaxTimestepEmbedding(snake_case__  ,     dtype=self.dtype				)

       _SCREAMING_SNAKE_CASE   :				str							    = self.only_cross_attention
       if isinstance(snake_case__  ,     snake_case__				):
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = (only_cross_attention,) * len(self.down_block_types				)

       if isinstance(snake_case__  ,     snake_case__				):
         _SCREAMING_SNAKE_CASE   :				int							    = (num_attention_heads,) * len(self.down_block_types				)

       # down
       _SCREAMING_SNAKE_CASE   :				Tuple							    = []
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = block_out_channels[0]
       for i, down_block_type in enumerate(self.down_block_types				):
         _SCREAMING_SNAKE_CASE   :				str							    = output_channel
         _SCREAMING_SNAKE_CASE   :				List[str]							    = block_out_channels[i]
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = i == len(snake_case__				) - 1

         if down_block_type == "CrossAttnDownBlock2D":
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = FlaxCrossAttnDownBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     dropout=self.dropout  ,     num_layers=self.layers_per_block  ,     num_attention_heads=num_attention_heads[i]  ,     add_downsample=not is_final_block  ,     use_linear_projection=self.use_linear_projection  ,     only_cross_attention=only_cross_attention[i]  ,     use_memory_efficient_attention=self.use_memory_efficient_attention  ,     dtype=self.dtype  ,     )
         else:
           _SCREAMING_SNAKE_CASE   :				str							    = FlaxDownBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     dropout=self.dropout  ,     num_layers=self.layers_per_block  ,     add_downsample=not is_final_block  ,     dtype=self.dtype  ,     )

         down_blocks.append(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = down_blocks

       # mid
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = FlaxUNetMidBlockaDCrossAttn(
           in_channels=block_out_channels[-1]  ,     dropout=self.dropout  ,     num_attention_heads=num_attention_heads[-1]  ,     use_linear_projection=self.use_linear_projection  ,     use_memory_efficient_attention=self.use_memory_efficient_attention  ,     dtype=self.dtype  ,     )

       # up
       _SCREAMING_SNAKE_CASE   :				int							    = []
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = list(reversed(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Any							    = list(reversed(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Any							    = list(reversed(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = reversed_block_out_channels[0]
       for i, up_block_type in enumerate(self.up_block_types				):
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = output_channel
         _SCREAMING_SNAKE_CASE   :				List[str]							    = reversed_block_out_channels[i]
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = reversed_block_out_channels[min(i + 1  ,     len(snake_case__				) - 1				)]

         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = i == len(snake_case__				) - 1

         if up_block_type == "CrossAttnUpBlock2D":
           _SCREAMING_SNAKE_CASE   :				int							    = FlaxCrossAttnUpBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     prev_output_channel=snake_case__  ,     num_layers=self.layers_per_block + 1  ,     num_attention_heads=reversed_num_attention_heads[i]  ,     add_upsample=not is_final_block  ,     dropout=self.dropout  ,     use_linear_projection=self.use_linear_projection  ,     only_cross_attention=only_cross_attention[i]  ,     use_memory_efficient_attention=self.use_memory_efficient_attention  ,     dtype=self.dtype  ,     )
         else:
           _SCREAMING_SNAKE_CASE   :				str							    = FlaxUpBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     prev_output_channel=snake_case__  ,     num_layers=self.layers_per_block + 1  ,     add_upsample=not is_final_block  ,     dropout=self.dropout  ,     dtype=self.dtype  ,     )

         up_blocks.append(snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Any							    = output_channel
       _SCREAMING_SNAKE_CASE   :				int							    = up_blocks

       # out
       _SCREAMING_SNAKE_CASE   :				str							    = nn.GroupNorm(num_groups=32  ,     epsilon=1E-5				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = nn.Conv(
           self.out_channels  ,     kernel_size=(3, 3)  ,     strides=(1, 1)  ,     padding=((1, 1), (1, 1))  ,     dtype=self.dtype  ,     )




     def __call__(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__ = True  ,     snake_case__ = False  ,     ):




       """simple docstring"""


       if not isinstance(snake_case__  ,     jnp.ndarray				):
         _SCREAMING_SNAKE_CASE   :				Any							    = jnp.array([timesteps]  ,     dtype=jnp.intaa				)
       elif isinstance(snake_case__  ,     jnp.ndarray				) and len(timesteps.shape				) == 0:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = timesteps.astype(dtype=jnp.floataa				)
         _SCREAMING_SNAKE_CASE   :				Tuple							    = jnp.expand_dims(snake_case__  ,     0				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.time_proj(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.time_embedding(snake_case__				)

       # 2. pre-process
       _SCREAMING_SNAKE_CASE   :				Dict							    = jnp.transpose(snake_case__  ,     (0, 2, 3, 1)				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.conv_in(snake_case__				)

       # 3. down
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = (sample,)
       for down_block in self.down_blocks:
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = down_block(snake_case__  ,     snake_case__  ,     snake_case__  ,     deterministic=not train				)
         else:
           _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = down_block(snake_case__  ,     snake_case__  ,     deterministic=not train				)
         down_block_res_samples += res_samples

       if down_block_additional_residuals is not None:
         _SCREAMING_SNAKE_CASE   :				str							    = ()

         for down_block_res_sample, down_block_additional_residual in zip(
             snake_case__  ,     snake_case__				):
           down_block_res_sample += down_block_additional_residual
           new_down_block_res_samples += (down_block_res_sample,)

         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_down_block_res_samples

       # 4. mid
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.mid_block(snake_case__  ,     snake_case__  ,     snake_case__  ,     deterministic=not train				)

       if mid_block_additional_residual is not None:
         sample += mid_block_additional_residual

       # 5. up
       for up_block in self.up_blocks:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = down_block_res_samples[-(self.layers_per_block + 1) :]
         _SCREAMING_SNAKE_CASE   :				int							    = down_block_res_samples[: -(self.layers_per_block + 1)]
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = up_block(
               snake_case__  ,     temb=snake_case__  ,     encoder_hidden_states=snake_case__  ,     res_hidden_states_tuple=snake_case__  ,     deterministic=not train  ,     )
         else:
           _SCREAMING_SNAKE_CASE   :				List[str]							    = up_block(snake_case__  ,     temb=snake_case__  ,     res_hidden_states_tuple=snake_case__  ,     deterministic=not train				)

        # 6. post-process
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.conv_norm_out(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = nn.silu(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.conv_out(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = jnp.transpose(snake_case__  ,     (0, 3, 1, 2)				)

       if not return_dict:
         return (sample,)

       return FlaxUNetaDConditionOutput(sample=snake_case__				)



