



"""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    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       42




class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@register_to_config
		def __init__(					self							,    _lowercase = 3_2							,    _lowercase = 6_4							,    _lowercase = 2_0							,    _lowercase = 7_6_8							,    _lowercase=7_7							,    _lowercase=4							,    _lowercase = 0.0							,    _lowercase = "silu"							,    _lowercase = None							,    _lowercase = None							,    _lowercase = "linear"							,    _lowercase = "prd"							,    _lowercase = None							,    _lowercase = None							,    _lowercase = None							,    ) ->						Dict:







								'''simple docstring'''

								super().__init__()
								snake_case_   : Tuple						=			num_attention_heads
								snake_case_   : str						=			attention_head_dim
								snake_case_   : Optional[Any]						=			num_attention_heads * attention_head_dim
								snake_case_   : List[str]						=			additional_embeddings

								snake_case_   : Any						=			time_embed_dim or inner_dim
								snake_case_   : Any						=			embedding_proj_dim or embedding_dim
								snake_case_   : List[str]						=			clip_embed_dim or embedding_dim

								snake_case_   : int						=			Timesteps(_lowercase							,    _lowercase							,    0   )
								snake_case_   : Dict						=			TimestepEmbedding(_lowercase							,    _lowercase							,    out_dim=_lowercase							,    act_fn=_lowercase   )

								snake_case_   : Union[str, Any]						=			nn.Linear(_lowercase							,    _lowercase   )

								if embedding_proj_norm_type is None:
														snake_case_   : List[str]						=			None
								elif embedding_proj_norm_type == "layer":
														snake_case_   : Dict						=			nn.LayerNorm(_lowercase   )
								else:
														raise ValueError(f'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'   )

								snake_case_   : Any						=			nn.Linear(_lowercase							,    _lowercase   )

								if encoder_hid_proj_type is None:
														snake_case_   : str						=			None
								elif encoder_hid_proj_type == "linear":
														snake_case_   : List[str]						=			nn.Linear(_lowercase							,    _lowercase   )
								else:
														raise ValueError(f'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'   )

								snake_case_   : Any						=			nn.Parameter(torch.zeros(1							,    num_embeddings + additional_embeddings							,    _lowercase   )   )

								if added_emb_type == "prd":
														snake_case_   : Dict						=			nn.Parameter(torch.zeros(1							,    1							,    _lowercase   )   )
								elif added_emb_type is None:
														snake_case_   : int						=			None
								else:
														raise ValueError(
														    f'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'   )

								snake_case_   : Tuple						=			nn.ModuleList(
								    [
								        BasicTransformerBlock(
								            _lowercase							,    _lowercase							,    _lowercase							,    dropout=_lowercase							,    activation_fn="""gelu"""							,    attention_bias=_lowercase							,    )
								        for d in range(_lowercase   )
								    ]   )

								if norm_in_type == "layer":
														snake_case_   : Optional[Any]						=			nn.LayerNorm(_lowercase   )
								elif norm_in_type is None:
														snake_case_   : List[str]						=			None
								else:
														raise ValueError(f'Unsupported norm_in_type: {norm_in_type}.'   )

								snake_case_   : int						=			nn.LayerNorm(_lowercase   )

								snake_case_   : str						=			nn.Linear(_lowercase							,    _lowercase   )

								snake_case_   : Optional[Any]						=			torch.full(
								    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]							,    -1_0000.0   )
								causal_attention_mask.triu_(1   )
								snake_case_   : Optional[int]						=			causal_attention_mask[None, ...]
								self.register_buffer("""causal_attention_mask"""							,    _lowercase							,    persistent=_lowercase   )

								snake_case_   : Any						=			nn.Parameter(torch.zeros(1							,    _lowercase   )   )
								snake_case_   : Dict						=			nn.Parameter(torch.zeros(1							,    _lowercase   )   )





		@property
		# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
		def    UpperCAmelCase__							(					self   ) ->						Dict[str, AttentionProcessor]:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			{}

								def fn_recursive_add_processors(_lowercase							,    _lowercase							,    _lowercase   ):
														if hasattr(_lowercase							,    """set_processor"""   ):
																				snake_case_   : str						=			module.processor

														for sub_name, child in module.named_children():
																				fn_recursive_add_processors(f'{name}.{sub_name}'							,    _lowercase							,    _lowercase   )

														return processors

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

								return processors





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







								'''simple docstring'''

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

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

								def fn_recursive_attn_processor(_lowercase							,    _lowercase							,    _lowercase   ):
														if hasattr(_lowercase							,    """set_processor"""   ):
																				if not isinstance(_lowercase							,    _lowercase   ):
																										module.set_processor(_lowercase   )
																				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}'							,    _lowercase							,    _lowercase   )

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





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								self.set_attn_processor(AttnProcessor()   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase = None							,    _lowercase = None							,    _lowercase = True							,    ) ->						Any:







								'''simple docstring'''

								snake_case_   : Tuple						=			hidden_states.shape[0]

								snake_case_   : List[Any]						=			timestep
								if not torch.is_tensor(_lowercase   ):
														snake_case_   : Optional[Any]						=			torch.tensor([timesteps]							,    dtype=torch.long							,    device=hidden_states.device   )
								elif torch.is_tensor(_lowercase   ) and len(timesteps.shape   ) == 0:
														snake_case_   : str						=			timesteps[None].to(hidden_states.device   )

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

								snake_case_   : Tuple						=			self.time_proj(_lowercase   )

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

								if self.embedding_proj_norm is not None:
														snake_case_   : Optional[Any]						=			self.embedding_proj_norm(_lowercase   )

								snake_case_   : List[Any]						=			self.embedding_proj(_lowercase   )
								if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
														snake_case_   : List[Any]						=			self.encoder_hidden_states_proj(_lowercase   )
								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"""   )

								snake_case_   : Dict						=			self.proj_in(_lowercase   )

								snake_case_   : str						=			self.positional_embedding.to(hidden_states.dtype   )

								snake_case_   : Any						=			[]
								snake_case_   : Optional[Any]						=			0

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

								if len(proj_embeddings.shape   ) == 2:
														snake_case_   : str						=			proj_embeddings[:, None, :]

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

								snake_case_   : List[str]						=			additional_embeds + [
								    proj_embeddings,
								    time_embeddings[:, None, :],
								    hidden_states,
								]

								if self.prd_embedding is not None:
														snake_case_   : Optional[int]						=			self.prd_embedding.to(hidden_states.dtype   ).expand(_lowercase							,    -1							,    -1   )
														additional_embeds.append(_lowercase   )

								snake_case_   : List[Any]						=			torch.cat(
								    _lowercase							,    dim=1							,    )

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

								snake_case_   : int						=			hidden_states + positional_embeddings

								if attention_mask is not None:
														snake_case_   : Union[str, Any]						=			(1 - attention_mask.to(hidden_states.dtype   )) * -1_0000.0
														snake_case_   : Dict						=			F.pad(_lowercase							,    (0, self.additional_embeddings)							,    value=0.0   )
														snake_case_   : List[Any]						=			(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype   )
														snake_case_   : int						=			attention_mask.repeat_interleave(self.config.num_attention_heads							,    dim=0   )

								if self.norm_in is not None:
														snake_case_   : List[str]						=			self.norm_in(_lowercase   )

								for block in self.transformer_blocks:
														snake_case_   : Optional[Any]						=			block(_lowercase							,    attention_mask=_lowercase   )

								snake_case_   : int						=			self.norm_out(_lowercase   )

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

								snake_case_   : List[Any]						=			self.proj_to_clip_embeddings(_lowercase   )

								if not return_dict:
														return (predicted_image_embedding,)

								return PriorTransformerOutput(predicted_image_embedding=_lowercase   )





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







								'''simple docstring'''

								snake_case_   : List[Any]						=			(prior_latents * self.clip_std) + self.clip_mean
								return prior_latents






"""simple docstring"""





import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
					import optax
					from flax.training.common_utils import onehot

					from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
					from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : int						=			FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""   )
								snake_case_   : List[Any]						=			AutoTokenizer.from_pretrained("""google/mt5-small"""   )

								snake_case_   : Dict						=			tokenizer("""Hello there"""							,    return_tensors="""np"""   ).input_ids
								snake_case_   : List[str]						=			tokenizer("""Hi I am"""							,    return_tensors="""np"""   ).input_ids

								snake_case_   : Optional[Any]						=			shift_tokens_right(_lowercase							,    model.config.pad_token_id							,    model.config.decoder_start_token_id   )

								snake_case_   : Tuple						=			model(_lowercase							,    decoder_input_ids=_lowercase   ).logits
								snake_case_   : Tuple						=			optax.softmax_cross_entropy(_lowercase							,    onehot(_lowercase							,    logits.shape[-1]   )   ).mean()

								snake_case_   : List[str]						=			-(labels.shape[-1] * loss.item())

								snake_case_   : Optional[int]						=			-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )

