

"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
		_UpperCAmelCase          =		CanineTokenizer
		_UpperCAmelCase          =		False

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						super().setUp()
						_lowerCAmelCase       :   str  =    CanineTokenizer()
						tokenizer.save_pretrained(self.tmpdirname      )

		@cached_property
		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						return CanineTokenizer.from_pretrained('google/canine-s'      )

		def __lowerCamelCase		(		self    ,**_A      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Tuple  =    self.tokenizer_class.from_pretrained(self.tmpdirname    ,**_A      )
						_lowerCAmelCase       :   List[Any]  =    1024
						return tokenizer

		@require_torch
		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Optional[Any]  =    self.canine_tokenizer
						_lowerCAmelCase       :   Tuple  =    ['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
						# fmt: off
						_lowerCAmelCase       :   int  =    [5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
						# fmt: on
						_lowerCAmelCase       :   str  =    tokenizer(_A    ,padding=_A    ,return_tensors='pt'      )
						self.assertIsInstance(_A    ,_A      )

						_lowerCAmelCase       :   Tuple  =    list(batch.input_ids.numpy()[0]      )

						self.assertListEqual(_A    ,_A      )

						self.assertEqual((2, 39)    ,batch.input_ids.shape      )
						self.assertEqual((2, 39)    ,batch.attention_mask.shape      )

		@require_torch
		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   List[str]  =    self.canine_tokenizer
						_lowerCAmelCase       :   Optional[int]  =    ['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
						_lowerCAmelCase       :   Dict  =    tokenizer(_A    ,padding=_A    ,return_tensors='pt'      )
						# check if input_ids, attention_mask and token_type_ids are returned
						self.assertIn('input_ids'    ,_A      )
						self.assertIn('attention_mask'    ,_A      )
						self.assertIn('token_type_ids'    ,_A      )

		@require_torch
		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Optional[int]  =    self.canine_tokenizer
						_lowerCAmelCase       :   Dict  =    [
						    'What\'s the weater?',
						    'It\'s about 25 degrees.',
						]
						_lowerCAmelCase       :   Optional[int]  =    tokenizer(
						    text_target=_A    ,max_length=32    ,padding='max_length'    ,truncation=_A    ,return_tensors='pt'      )
						self.assertEqual(32    ,targets['input_ids'].shape[1]      )

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   List[str]  =    self.get_tokenizers()
						for tokenizer in tokenizers:
										with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
														self.assertNotEqual(tokenizer.model_max_length    ,42      )

        # Now let's start the test
						_lowerCAmelCase       :   Tuple  =    self.get_tokenizers()
						for tokenizer in tokenizers:
										with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
														# Isolate this from the other tests because we save additional tokens/etc
														_lowerCAmelCase       :   str  =    tempfile.mkdtemp()

														_lowerCAmelCase       :   int  =    ' He is very happy, UNwant\u00E9d,running'
														_lowerCAmelCase       :   Tuple  =    tokenizer.encode(_A    ,add_special_tokens=_A      )
														tokenizer.save_pretrained(_A      )

														_lowerCAmelCase       :   str  =    tokenizer.__class__.from_pretrained(_A      )
														_lowerCAmelCase       :   int  =    after_tokenizer.encode(_A    ,add_special_tokens=_A      )
														self.assertListEqual(_A    ,_A      )

														shutil.rmtree(_A      )

						_lowerCAmelCase       :   int  =    self.get_tokenizers(model_max_length=42      )
						for tokenizer in tokenizers:
										with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
														# Isolate this from the other tests because we save additional tokens/etc
														_lowerCAmelCase       :   Union[str, Any]  =    tempfile.mkdtemp()

														_lowerCAmelCase       :   str  =    ' He is very happy, UNwant\u00E9d,running'

														_lowerCAmelCase       :   List[str]  =    tokenizer.additional_special_tokens

														# We can add a new special token for Canine as follows:
														_lowerCAmelCase       :   Optional[int]  =    chr(0xE007      )
														additional_special_tokens.append(_A      )
														tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}      )
														_lowerCAmelCase       :   Any  =    tokenizer.encode(_A    ,add_special_tokens=_A      )
														tokenizer.save_pretrained(_A      )

														_lowerCAmelCase       :   int  =    tokenizer.__class__.from_pretrained(_A      )
														_lowerCAmelCase       :   str  =    after_tokenizer.encode(_A    ,add_special_tokens=_A      )
														self.assertListEqual(_A    ,_A      )
														self.assertIn(_A    ,after_tokenizer.additional_special_tokens      )
														self.assertEqual(after_tokenizer.model_max_length    ,42      )

														_lowerCAmelCase       :   str  =    tokenizer.__class__.from_pretrained(_A    ,model_max_length=43      )
														self.assertEqual(tokenizer.model_max_length    ,43      )

														shutil.rmtree(_A      )

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Union[str, Any]  =    self.get_tokenizers(do_lower_case=_A      )
						for tokenizer in tokenizers:
										with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
														_lowerCAmelCase       :   Dict  =    self.get_clean_sequence(_A      )

														# a special token for Canine can be defined as follows:
														_lowerCAmelCase       :   List[Any]  =    0xE005
														_lowerCAmelCase       :   Dict  =    chr(_A      )

														tokenizer.add_special_tokens({'cls_token': special_token}      )
														_lowerCAmelCase       :   Any  =    tokenizer.encode(_A    ,add_special_tokens=_A      )
														self.assertEqual(len(_A      )    ,1      )

														_lowerCAmelCase       :   Tuple  =    tokenizer.decode(ids + encoded_special_token    ,clean_up_tokenization_spaces=_A      )
														_lowerCAmelCase       :   Union[str, Any]  =    tokenizer.encode(_A    ,add_special_tokens=_A      )

														_lowerCAmelCase       :   List[Any]  =    tokenizer.encode(_A    ,add_special_tokens=_A      )
														_lowerCAmelCase       :   Optional[Any]  =    tokenizer.encode(_A    ,add_special_tokens=_A      )
														self.assertEqual(_A    ,input_encoded + special_token_id      )

														_lowerCAmelCase       :   List[Any]  =    tokenizer.decode(_A    ,skip_special_tokens=_A      )
														self.assertTrue(special_token not in decoded      )

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   str  =    self.get_tokenizers(do_lower_case=_A      )
						for tokenizer in tokenizers:
										with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
														_lowerCAmelCase       :   Union[str, Any]  =    chr(0xE005      )
														_lowerCAmelCase       :   Tuple  =    chr(0xE006      )

														# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
														tokenizer.add_tokens([SPECIAL_TOKEN_1]    ,special_tokens=_A      )
														# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
														# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
														tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]}      )

														_lowerCAmelCase       :   str  =    tokenizer.tokenize(_A      )
														_lowerCAmelCase       :   Tuple  =    tokenizer.tokenize(_A      )

														self.assertEqual(len(_A      )    ,1      )
														self.assertEqual(len(_A      )    ,1      )
														self.assertEqual(token_a[0]    ,_A      )
														self.assertEqual(token_a[0]    ,_A      )

		@require_tokenizers
		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Any  =    self.get_tokenizers(do_lower_case=_A      )
						for tokenizer in tokenizers:
										with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
														# a special token for Canine can be defined as follows:
														_lowerCAmelCase       :   Optional[int]  =    0xE006
														_lowerCAmelCase       :   List[Any]  =    chr(_A      )

														_lowerCAmelCase       :   Union[str, Any]  =    AddedToken(_A    ,lstrip=_A      )
														tokenizer.add_special_tokens({'additional_special_tokens': [new_token]}      )

														with tempfile.TemporaryDirectory() as tmp_dir_name:
																		tokenizer.save_pretrained(_A      )
																		tokenizer.from_pretrained(_A      )

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Tuple  =    []
						if self.test_slow_tokenizer:
										tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())      )

						if self.test_rust_tokenizer:
										tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())      )

						for tokenizer_class, tokenizer_utils in tokenizer_list:
										with tempfile.TemporaryDirectory() as tmp_dir:
														tokenizer_utils.save_pretrained(_A      )

														with open(os.path.join(_A    ,'special_tokens_map.json'      )    ,encoding='utf-8'      ) as json_file:
																		_lowerCAmelCase       :   List[Any]  =    json.load(_A      )

														with open(os.path.join(_A    ,'tokenizer_config.json'      )    ,encoding='utf-8'      ) as json_file:
																		_lowerCAmelCase       :   Optional[Any]  =    json.load(_A      )

														# a special token for Canine can be defined as follows:
														_lowerCAmelCase       :   str  =    0xE006
														_lowerCAmelCase       :   List[str]  =    chr(_A      )

														_lowerCAmelCase       :   Dict  =    [new_token_a]
														_lowerCAmelCase       :   Optional[Any]  =    [new_token_a]

														with open(os.path.join(_A    ,'special_tokens_map.json'      )    ,'w'    ,encoding='utf-8'      ) as outfile:
																		json.dump(_A    ,_A      )
														with open(os.path.join(_A    ,'tokenizer_config.json'      )    ,'w'    ,encoding='utf-8'      ) as outfile:
																		json.dump(_A    ,_A      )

														# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
														# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
														# "special_tokens_map.json" files
														_lowerCAmelCase       :   Union[str, Any]  =    tokenizer_class.from_pretrained(_A    ,extra_ids=0      )
														self.assertIn(_A    ,tokenizer_without_change_in_init.additional_special_tokens      )
														# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
														self.assertEqual(
														    [new_token_a]    ,tokenizer_without_change_in_init.convert_ids_to_tokens(
														        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]      )      )    ,)

														_lowerCAmelCase       :   List[Any]  =    0xE007
														_lowerCAmelCase       :   List[str]  =    chr(_A      )
														# Now we test that we can change the value of additional_special_tokens in the from_pretrained
														_lowerCAmelCase       :   Optional[Any]  =    [AddedToken(_A    ,lstrip=_A      )]
														_lowerCAmelCase       :   str  =    tokenizer_class.from_pretrained(
														    _A    ,additional_special_tokens=_A    ,extra_ids=0      )

														self.assertIn(_A    ,tokenizer.additional_special_tokens      )
														# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
														self.assertEqual(
														    [new_token_a]    ,tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]      )      )      )

		@require_tokenizers
		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Tuple  =    self.get_tokenizers(do_lower_case=_A      )
						for tokenizer in tokenizers:
										with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
														_lowerCAmelCase       :   str  =    'hello world'
														if self.space_between_special_tokens:
																		_lowerCAmelCase       :   Optional[Any]  =    '[CLS] hello world [SEP]'
														else:
																		_lowerCAmelCase       :   Optional[Any]  =    input
														_lowerCAmelCase       :   Union[str, Any]  =    tokenizer.encode(_A    ,add_special_tokens=_A      )
														_lowerCAmelCase       :   Tuple  =    tokenizer.decode(_A    ,spaces_between_special_tokens=self.space_between_special_tokens      )
														self.assertIn(_A    ,[output, output.lower()]      )

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Optional[Any]  =    self.get_tokenizers()
						for tokenizer in tokenizers:
										with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
														_lowerCAmelCase       :   Any  =    [
														    'bos_token',
														    'eos_token',
														    'unk_token',
														    'sep_token',
														    'pad_token',
														    'cls_token',
														    'mask_token',
														]

						_lowerCAmelCase       :   Union[str, Any]  =    'a'
						_lowerCAmelCase       :   List[str]  =    ord(_A      )

						for attr in attributes_list:
										setattr(_A    ,attr + '_id'    ,_A      )
										self.assertEqual(getattr(_A    ,_A      )    ,_A      )
										self.assertEqual(getattr(_A    ,attr + '_id'      )    ,_A      )

										setattr(_A    ,attr + '_id'    ,_A      )
										self.assertEqual(getattr(_A    ,_A      )    ,_A      )
										self.assertEqual(getattr(_A    ,attr + '_id'      )    ,_A      )

						setattr(_A    ,'additional_special_tokens_ids'    ,[]      )
						self.assertListEqual(getattr(_A    ,'additional_special_tokens'      )    ,[]      )
						self.assertListEqual(getattr(_A    ,'additional_special_tokens_ids'      )    ,[]      )

						_lowerCAmelCase       :   List[Any]  =    0xE006
						_lowerCAmelCase       :   Any  =    chr(_A      )
						setattr(_A    ,'additional_special_tokens_ids'    ,[additional_special_token_id]      )
						self.assertListEqual(getattr(_A    ,'additional_special_tokens'      )    ,[additional_special_token]      )
						self.assertListEqual(getattr(_A    ,'additional_special_tokens_ids'      )    ,[additional_special_token_id]      )

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						pass

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						pass

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						pass

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						pass

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						pass

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						pass

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						pass







		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						pass








"""simple docstring"""






from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin







@dataclass
class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		42







class 	__UpperCamelCase    (    a__							,							a__						):

  @register_to_config
  def __init__(		self    ,_A = 32    ,_A = 64    ,_A = 20    ,_A = 768    ,_A=77    ,_A=4    ,_A = 0.0    ,_A = "silu"    ,_A = None    ,_A = None    ,_A = "linear"    ,_A = "prd"    ,_A = None    ,_A = None    ,_A = None    ,):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Any  =    num_attention_heads
      _lowerCAmelCase       :   Optional[int]  =    attention_head_dim
      _lowerCAmelCase       :   Tuple  =    num_attention_heads * attention_head_dim
      _lowerCAmelCase       :   Optional[Any]  =    additional_embeddings

      _lowerCAmelCase       :   Union[str, Any]  =    time_embed_dim or inner_dim
      _lowerCAmelCase       :   Union[str, Any]  =    embedding_proj_dim or embedding_dim
      _lowerCAmelCase       :   Optional[int]  =    clip_embed_dim or embedding_dim

      _lowerCAmelCase       :   int  =    Timesteps(_A    ,_A    ,0      )
      _lowerCAmelCase       :   int  =    TimestepEmbedding(_A    ,_A    ,out_dim=_A    ,act_fn=_A      )

      _lowerCAmelCase       :   List[Any]  =    nn.Linear(_A    ,_A      )

      if embedding_proj_norm_type is None:
          _lowerCAmelCase       :   Optional[Any]  =    None
      elif embedding_proj_norm_type == "layer":
          _lowerCAmelCase       :   List[Any]  =    nn.LayerNorm(_A      )
      else:
          raise ValueError(F"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""      )

      _lowerCAmelCase       :   Tuple  =    nn.Linear(_A    ,_A      )

      if encoder_hid_proj_type is None:
          _lowerCAmelCase       :   int  =    None
      elif encoder_hid_proj_type == "linear":
          _lowerCAmelCase       :   List[Any]  =    nn.Linear(_A    ,_A      )
      else:
          raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""      )

      _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,num_embeddings + additional_embeddings    ,_A      )      )

      if added_emb_type == "prd":
          _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,1    ,_A      )      )
      elif added_emb_type is None:
          _lowerCAmelCase       :   List[Any]  =    None
      else:
          raise ValueError(
              F"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""      )

      _lowerCAmelCase       :   List[Any]  =    nn.ModuleList(
          [
              BasicTransformerBlock(
                  _A    ,_A    ,_A    ,dropout=_A    ,activation_fn='gelu'    ,attention_bias=_A    ,)
              for d in range(_A      )
          ]      )

      if norm_in_type == "layer":
          _lowerCAmelCase       :   Any  =    nn.LayerNorm(_A      )
      elif norm_in_type is None:
          _lowerCAmelCase       :   Any  =    None
      else:
          raise ValueError(F"""Unsupported norm_in_type: {norm_in_type}."""      )

      _lowerCAmelCase       :   Union[str, Any]  =    nn.LayerNorm(_A      )

      _lowerCAmelCase       :   int  =    nn.Linear(_A    ,_A      )

      _lowerCAmelCase       :   Any  =    torch.full(
          [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]    ,-1_0_0_0_0.0      )
      causal_attention_mask.triu_(1      )
      _lowerCAmelCase       :   Tuple  =    causal_attention_mask[None, ...]
      self.register_buffer('causal_attention_mask'    ,_A    ,persistent=_A      )

      _lowerCAmelCase       :   Tuple  =    nn.Parameter(torch.zeros(1    ,_A      )      )
      _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,_A      )      )

  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    {}

      def fn_recursive_add_processors(_A    ,_A    ,_A      ):
          if hasattr(_A    ,'set_processor'      ):
              _lowerCAmelCase       :   str  =    module.processor

          for sub_name, child in module.named_children():
              fn_recursive_add_processors(F"""{name}.{sub_name}"""    ,_A    ,_A      )

          return processors

      for name, module in self.named_children():
          fn_recursive_add_processors(_A    ,_A    ,_A      )

      return processors

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    len(self.attn_processors.keys()      )

      if isinstance(_A    ,_A      ) and len(_A      ) != count:
          raise ValueError(
              F"""A dict of processors was passed, but the number of processors {len(_A      )} does not match the"""
              F""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""      )

      def fn_recursive_attn_processor(_A    ,_A    ,_A      ):
          if hasattr(_A    ,'set_processor'      ):
              if not isinstance(_A    ,_A      ):
                  module.set_processor(_A      )
              else:
                  module.set_processor(processor.pop(F"""{name}.processor"""      )      )

          for sub_name, child in module.named_children():
              fn_recursive_attn_processor(F"""{name}.{sub_name}"""    ,_A    ,_A      )

      for name, module in self.named_children():
          fn_recursive_attn_processor(_A    ,_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.set_attn_processor(AttnProcessor()      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A = None    ,_A = None    ,_A = True    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    hidden_states.shape[0]

      _lowerCAmelCase       :   int  =    timestep
      if not torch.is_tensor(_A      ):
          _lowerCAmelCase       :   str  =    torch.tensor([timesteps]    ,dtype=torch.long    ,device=hidden_states.device      )
      elif torch.is_tensor(_A      ) and len(timesteps.shape      ) == 0:
          _lowerCAmelCase       :   Dict  =    timesteps[None].to(hidden_states.device      )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      _lowerCAmelCase       :   Optional[int]  =    timesteps * torch.ones(_A    ,dtype=timesteps.dtype    ,device=timesteps.device      )

      _lowerCAmelCase       :   Dict  =    self.time_proj(_A      )

      # timesteps does not contain any weights and will always return f32 tensors
      # but time_embedding might be fp16, so we need to cast here.
      _lowerCAmelCase       :   Any  =    timesteps_projected.to(dtype=self.dtype      )
      _lowerCAmelCase       :   Optional[Any]  =    self.time_embedding(_A      )

      if self.embedding_proj_norm is not None:
          _lowerCAmelCase       :   int  =    self.embedding_proj_norm(_A      )

      _lowerCAmelCase       :   str  =    self.embedding_proj(_A      )
      if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
          _lowerCAmelCase       :   str  =    self.encoder_hidden_states_proj(_A      )
      elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
          raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'      )

      _lowerCAmelCase       :   Any  =    self.proj_in(_A      )

      _lowerCAmelCase       :   Dict  =    self.positional_embedding.to(hidden_states.dtype      )

      _lowerCAmelCase       :   List[Any]  =    []
      _lowerCAmelCase       :   Optional[Any]  =    0

      if encoder_hidden_states is not None:
          additional_embeds.append(_A      )
          additional_embeddings_len += encoder_hidden_states.shape[1]

      if len(proj_embeddings.shape      ) == 2:
          _lowerCAmelCase       :   int  =    proj_embeddings[:, None, :]

      if len(hidden_states.shape      ) == 2:
          _lowerCAmelCase       :   Any  =    hidden_states[:, None, :]

      _lowerCAmelCase       :   int  =    additional_embeds + [
          proj_embeddings,
          time_embeddings[:, None, :],
          hidden_states,
      ]

      if self.prd_embedding is not None:
          _lowerCAmelCase       :   List[str]  =    self.prd_embedding.to(hidden_states.dtype      ).expand(_A    ,-1    ,-1      )
          additional_embeds.append(_A      )

      _lowerCAmelCase       :   List[str]  =    torch.cat(
          _A    ,dim=1    ,)

      # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
      _lowerCAmelCase       :   Tuple  =    additional_embeddings_len + proj_embeddings.shape[1] + 1
      if positional_embeddings.shape[1] < hidden_states.shape[1]:
          _lowerCAmelCase       :   Any  =    F.pad(
              _A    ,(
                  0,
                  0,
                  additional_embeddings_len,
                  self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
              )    ,value=0.0    ,)

      _lowerCAmelCase       :   int  =    hidden_states + positional_embeddings

      if attention_mask is not None:
          _lowerCAmelCase       :   Optional[Any]  =    (1 - attention_mask.to(hidden_states.dtype      )) * -1_0_0_0_0.0
          _lowerCAmelCase       :   Union[str, Any]  =    F.pad(_A    ,(0, self.additional_embeddings)    ,value=0.0      )
          _lowerCAmelCase       :   Tuple  =    (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype      )
          _lowerCAmelCase       :   Optional[Any]  =    attention_mask.repeat_interleave(self.config.num_attention_heads    ,dim=0      )

      if self.norm_in is not None:
          _lowerCAmelCase       :   Any  =    self.norm_in(_A      )

      for block in self.transformer_blocks:
          _lowerCAmelCase       :   int  =    block(_A    ,attention_mask=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.norm_out(_A      )

      if self.prd_embedding is not None:
          _lowerCAmelCase       :   Optional[int]  =    hidden_states[:, -1]
      else:
          _lowerCAmelCase       :   Any  =    hidden_states[:, additional_embeddings_len:]

      _lowerCAmelCase       :   Optional[int]  =    self.proj_to_clip_embeddings(_A      )

      if not return_dict:
          return (predicted_image_embedding,)

      return PriorTransformerOutput(predicted_image_embedding=_A      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    (prior_latents * self.clip_std) + self.clip_mean
      return prior_latents





