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     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     42




class     lowerCAmelCase_     (       __lowercase,  __lowercase ):







							@register_to_config
							def __init__(   self      :     int ,			_A      :     int = 32 ,			_A      :     int = 64 ,			_A      :     int = 20 ,			_A      :     int = 768 ,			_A      :     Union[str, Any]=77 ,			_A      :     Optional[Any]=4 ,			_A      :     float = 0.0 ,			_A      :     str = "silu" ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = "linear" ,			_A      :     Optional[str] = "prd" ,			_A      :     Optional[int] = None ,			_A      :     Optional[int] = None ,			_A      :     Optional[int] = None ,			):
												super().__init__()
												_UpperCamelCase			     =     num_attention_heads
												_UpperCamelCase			     =     attention_head_dim
												_UpperCamelCase			     =     num_attention_heads * attention_head_dim
												_UpperCamelCase			     =     additional_embeddings

												_UpperCamelCase			     =     time_embed_dim or inner_dim
												_UpperCamelCase			     =     embedding_proj_dim or embedding_dim
												_UpperCamelCase			     =     clip_embed_dim or embedding_dim

												_UpperCamelCase			     =     Timesteps(_A ,			_A ,			0		)
												_UpperCamelCase			     =     TimestepEmbedding(_A ,			_A ,			out_dim=_A ,			act_fn=_A		)

												_UpperCamelCase			     =     nn.Linear(_A ,			_A		)

												if embedding_proj_norm_type is None:
																	_UpperCamelCase			     =     None
												elif embedding_proj_norm_type == "layer":
																	_UpperCamelCase			     =     nn.LayerNorm(_A		)
												else:
																	raise ValueError(F"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""		)

												_UpperCamelCase			     =     nn.Linear(_A ,			_A		)

												if encoder_hid_proj_type is None:
																	_UpperCamelCase			     =     None
												elif encoder_hid_proj_type == "linear":
																	_UpperCamelCase			     =     nn.Linear(_A ,			_A		)
												else:
																	raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""		)

												_UpperCamelCase			     =     nn.Parameter(torch.zeros(1 ,			num_embeddings + additional_embeddings ,			_A		)		)

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

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

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

												_UpperCamelCase			     =     nn.LayerNorm(_A		)

												_UpperCamelCase			     =     nn.Linear(_A ,			_A		)

												_UpperCamelCase			     =     torch.full(
												    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] ,			-1_0000.0		)
												causal_attention_mask.triu_(1		)
												_UpperCamelCase			     =     causal_attention_mask[None, ...]
												self.register_buffer('''causal_attention_mask''' ,			_A ,			persistent=_A		)

												_UpperCamelCase			     =     nn.Parameter(torch.zeros(1 ,			_A		)		)
												_UpperCamelCase			     =     nn.Parameter(torch.zeros(1 ,			_A		)		)







							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase			     =     {}

												def fn_recursive_add_processors(_A      :     str ,			_A      :     torch.nn.Module ,			_A      :     Dict[str, AttentionProcessor]		):
																	if hasattr(_A ,			'''set_processor'''		):
																						_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     Union[AttentionProcessor, Dict[str, AttentionProcessor]]		):
												_UpperCamelCase			     =     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      :     str ,			_A      :     torch.nn.Module ,			_A      :     List[str]		):
																	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  UpperCamelCase_						(   self      :     Union[str, Any]		):
												self.set_attn_processor(AttnProcessor()		)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Optional[Any] ,			_A      :     Union[torch.Tensor, float, int] ,			_A      :     torch.FloatTensor ,			_A      :     Optional[torch.FloatTensor] = None ,			_A      :     Optional[torch.BoolTensor] = None ,			_A      :     bool = True ,			):
												_UpperCamelCase			     =     hidden_states.shape[0]

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

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

												_UpperCamelCase			     =     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.
												_UpperCamelCase			     =     timesteps_projected.to(dtype=self.dtype		)
												_UpperCamelCase			     =     self.time_embedding(_A		)

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

												_UpperCamelCase			     =     self.embedding_proj(_A		)
												if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
																	_UpperCamelCase			     =     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'''		)

												_UpperCamelCase			     =     self.proj_in(_A		)

												_UpperCamelCase			     =     self.positional_embedding.to(hidden_states.dtype		)

												_UpperCamelCase			     =     []
												_UpperCamelCase			     =     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:
																	_UpperCamelCase			     =     proj_embeddings[:, None, :]

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

												_UpperCamelCase			     =     additional_embeds + [
												    proj_embeddings,
												    time_embeddings[:, None, :],
												    hidden_states,
												]

												if self.prd_embedding is not None:
																	_UpperCamelCase			     =     self.prd_embedding.to(hidden_states.dtype		).expand(_A ,			-1 ,			-1		)
																	additional_embeds.append(_A		)

												_UpperCamelCase			     =     torch.cat(
												    _A ,			dim=1 ,			)

												# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
												_UpperCamelCase			     =     additional_embeddings_len + proj_embeddings.shape[1] + 1
												if positional_embeddings.shape[1] < hidden_states.shape[1]:
																	_UpperCamelCase			     =     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 ,			)

												_UpperCamelCase			     =     hidden_states + positional_embeddings

												if attention_mask is not None:
																	_UpperCamelCase			     =     (1 - attention_mask.to(hidden_states.dtype		)) * -1_0000.0
																	_UpperCamelCase			     =     F.pad(_A ,			(0, self.additional_embeddings) ,			value=0.0		)
																	_UpperCamelCase			     =     (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype		)
																	_UpperCamelCase			     =     attention_mask.repeat_interleave(self.config.num_attention_heads ,			dim=0		)

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

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

												_UpperCamelCase			     =     self.norm_out(_A		)

												if self.prd_embedding is not None:
																	_UpperCamelCase			     =     hidden_states[:, -1]
												else:
																	_UpperCamelCase			     =     hidden_states[:, additional_embeddings_len:]

												_UpperCamelCase			     =     self.proj_to_clip_embeddings(_A		)

												if not return_dict:
																	return (predicted_image_embedding,)

												return PriorTransformerOutput(predicted_image_embedding=_A		)







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     Dict		):
												_UpperCamelCase			     =     (prior_latents * self.clip_std) + self.clip_mean
												return prior_latents





'''simple docstring'''





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__          =  get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:List[Any]     =       XGLMTokenizer
       lowerCAmelCase__	:Tuple     =       XGLMTokenizerFast
       lowerCAmelCase__	:Any     =       True
       lowerCAmelCase__	:List[Any]     =       True



       def 				_a   (					self   )	->				Tuple:
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowercase__									=    XGLMTokenizer(UpperCAmelCase_  ,keep_accents=UpperCAmelCase_   )
           tokenizer.save_pretrained(self.tmpdirname   )



       def 				_a   (					self   )	->				Tuple:
           lowercase__									=    "<pad>"
           lowercase__									=    1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_   )  ,UpperCAmelCase_   )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_   )  ,UpperCAmelCase_   )



       def 				_a   (					self   )	->				Dict:
           lowercase__									=    list(self.get_tokenizer().get_vocab().keys()   )

           self.assertEqual(vocab_keys[0]  ,"<s>"   )
           self.assertEqual(vocab_keys[1]  ,"<pad>"   )
           self.assertEqual(len(UpperCAmelCase_   )  ,1_008   )



       def 				_a   (					self   )	->				Union[str, Any]:
           self.assertEqual(self.get_tokenizer().vocab_size  ,1_008   )



       def 				_a   (					self   )	->				Dict:
           lowercase__									=    XGLMTokenizer(UpperCAmelCase_  ,keep_accents=UpperCAmelCase_   )

           lowercase__									=    tokenizer.tokenize("This is a test"   )
           self.assertListEqual(UpperCAmelCase_  ,["▁This", "▁is", "▁a", "▁t", "est"]   )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )  ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,)

           lowercase__									=    tokenizer.tokenize("I was born in 92000, and this is falsé."   )
           self.assertListEqual(
               UpperCAmelCase_  ,[
                   SPIECE_UNDERLINE + "I",
                   SPIECE_UNDERLINE + "was",
                   SPIECE_UNDERLINE + "b",
                   "or",
                   "n",
                   SPIECE_UNDERLINE + "in",
                   SPIECE_UNDERLINE + "",
                   "9",
                   "2",
                   "0",
                   "0",
                   "0",
                   ",",
                   SPIECE_UNDERLINE + "and",
                   SPIECE_UNDERLINE + "this",
                   SPIECE_UNDERLINE + "is",
                   SPIECE_UNDERLINE + "f",
                   "al",
                   "s",
                   "é",
                   ".",
               ]  ,)
           lowercase__									=    tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )
           self.assertListEqual(
               UpperCAmelCase_  ,[
                   value + tokenizer.fairseq_offset
                   for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               ]  ,)

           lowercase__									=    tokenizer.convert_ids_to_tokens(UpperCAmelCase_   )
           self.assertListEqual(
               UpperCAmelCase_  ,[
                   SPIECE_UNDERLINE + "I",
                   SPIECE_UNDERLINE + "was",
                   SPIECE_UNDERLINE + "b",
                   "or",
                   "n",
                   SPIECE_UNDERLINE + "in",
                   SPIECE_UNDERLINE + "",
                   "<unk>",
                   "2",
                   "0",
                   "0",
                   "0",
                   ",",
                   SPIECE_UNDERLINE + "and",
                   SPIECE_UNDERLINE + "this",
                   SPIECE_UNDERLINE + "is",
                   SPIECE_UNDERLINE + "f",
                   "al",
                   "s",
                   "<unk>",
                   ".",
               ]  ,)



       @cached_property
       def 				_a   (					self   )	->				Optional[int]:
           return XGLMTokenizer.from_pretrained("facebook/xglm-564M"   )



       def 				_a   (					self   )	->				int:
           with tempfile.NamedTemporaryFile() as f:
               shutil.copyfile(UpperCAmelCase_  ,f.name   )
               lowercase__									=    XGLMTokenizer(f.name  ,keep_accents=UpperCAmelCase_   )
               lowercase__									=    pickle.dumps(UpperCAmelCase_   )
           pickle.loads(UpperCAmelCase_   )



       def 				_a   (					self   )	->				Optional[int]:
           if not self.test_rust_tokenizer:
               return

           lowercase__									=    self.get_tokenizer()
           lowercase__									=    self.get_rust_tokenizer()

           lowercase__									=    "I was born in 92000, and this is falsé."

           lowercase__									=    tokenizer.tokenize(UpperCAmelCase_   )
           lowercase__									=    rust_tokenizer.tokenize(UpperCAmelCase_   )
           self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )

           lowercase__									=    tokenizer.encode(UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_   )
           lowercase__									=    rust_tokenizer.encode(UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_   )
           self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )

           lowercase__									=    self.get_rust_tokenizer()
           lowercase__									=    tokenizer.encode(UpperCAmelCase_   )
           lowercase__									=    rust_tokenizer.encode(UpperCAmelCase_   )
           self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )



       @slow
       def 				_a   (					self   )	->				List[str]:
           lowercase__									=    "Hello World!"
           lowercase__									=    [2, 31_227, 4_447, 35]

           self.assertListEqual(UpperCAmelCase_  ,self.big_tokenizer.encode(UpperCAmelCase_   )   )



       @slow
       def 				_a   (					self   )	->				str:
           lowercase__									=    (
               "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
               " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
           )
           # fmt: off
           lowercase__									=    [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
           # fmt: on

           self.assertListEqual(UpperCAmelCase_  ,self.big_tokenizer.encode(UpperCAmelCase_   )   )







       @slow
       def 				_a   (					self   )	->				str:
           # fmt: off
           lowercase__									=    {
               "input_ids": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
               "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
           }  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCAmelCase_  ,model_name="facebook/xglm-564M"  ,padding=UpperCAmelCase_  ,)




