




import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation






def  snake_case (							A						,       A						,       A = False						,       A = 1						,       A = 1						,       A = 10000						,       )							->							Dict:




	'''simple docstring'''


	assert len(timesteps.shape      ) == 1, "Timesteps should be a 1d-array"

	lowerCamelCase_							:		Union[str, Any]							       =							embedding_dim // 2
	lowerCamelCase_							:		List[Any]							       =							-math.log(A      ) * torch.arange(
	    start=0						,       end=A						,       dtype=torch.floataa						,       device=timesteps.device      )
	lowerCamelCase_							:		Any							       =							exponent / (half_dim - downscale_freq_shift)

	lowerCamelCase_							:		Optional[int]							       =							torch.exp(A      )
	lowerCamelCase_							:		Dict							       =							timesteps[:, None].float() * emb[None, :]

	# scale embeddings
	lowerCamelCase_							:		Optional[Any]							       =							scale * emb

	# concat sine and cosine embeddings
	lowerCamelCase_							:		Dict							       =							torch.cat([torch.sin(A      ), torch.cos(A      )]						,       dim=-1      )

	# flip sine and cosine embeddings
	if flip_sin_to_cos:
		lowerCamelCase_							:		Tuple							       =							torch.cat([emb[:, half_dim:], emb[:, :half_dim]]						,       dim=-1      )

	# zero pad
	if embedding_dim % 2 == 1:
		lowerCamelCase_							:		Optional[Any]							       =							torch.nn.functional.pad(A						,       (0, 1, 0, 0)      )
	return emb






def  snake_case (							A						,       A						,       A=False						,       A=0      )							->							Tuple:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[int]							       =							np.arange(A						,       dtype=np.floataa      )
	lowerCamelCase_							:		List[str]							       =							np.arange(A						,       dtype=np.floataa      )
	lowerCamelCase_							:		Optional[Any]							       =							np.meshgrid(A						,       A      )  # here w goes first
	lowerCamelCase_							:		Optional[Any]							       =							np.stack(A						,       axis=0      )

	lowerCamelCase_							:		Dict							       =							grid.reshape([2, 1, grid_size, grid_size]      )
	lowerCamelCase_							:		str							       =							get_ad_sincos_pos_embed_from_grid(A						,       A      )
	if cls_token and extra_tokens > 0:
		lowerCamelCase_							:		Dict							       =							np.concatenate([np.zeros([extra_tokens, embed_dim]      ), pos_embed]						,       axis=0      )
	return pos_embed






def  snake_case (							A						,       A      )							->							Tuple:




	'''simple docstring'''


	if embed_dim % 2 != 0:
		raise ValueError('embed_dim must be divisible by 2'      )

	# use half of dimensions to encode grid_h
	lowerCamelCase_							:		int							       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2						,       grid[0]      )  # (H*W, D/2)
	lowerCamelCase_							:		List[Any]							       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2						,       grid[1]      )  # (H*W, D/2)

	lowerCamelCase_							:		Any							       =							np.concatenate([emb_h, emb_w]						,       axis=1      )  # (H*W, D)
	return emb






def  snake_case (							A						,       A      )							->							Any:




	'''simple docstring'''


	if embed_dim % 2 != 0:
		raise ValueError('embed_dim must be divisible by 2'      )

	lowerCamelCase_							:		Optional[Any]							       =							np.arange(embed_dim // 2						,       dtype=np.floataa      )
	omega /= embed_dim / 2.0
	lowerCamelCase_							:		int							       =							1.0 / 10000**omega  # (D/2,)

	lowerCamelCase_							:		Dict							       =							pos.reshape(-1      )  # (M,)
	lowerCamelCase_							:		Union[str, Any]							       =							np.einsum('m,d->md'						,       A						,       A      )  # (M, D/2), outer product

	lowerCamelCase_							:		Any							       =							np.sin(A      )  # (M, D/2)
	lowerCamelCase_							:		str							       =							np.cos(A      )  # (M, D/2)

	lowerCamelCase_							:		Optional[Any]							       =							np.concatenate([emb_sin, emb_cos]						,       axis=1      )  # (M, D)
	return emb


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase=224			,	__lowercase=224			,	__lowercase=16			,	__lowercase=3			,	__lowercase=768			,	__lowercase=False			,	__lowercase=True			,	__lowercase=True			,	):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		Any							       =							(height // patch_size) * (width // patch_size)
						lowerCamelCase_							:		List[str]							       =							flatten
						lowerCamelCase_							:		Any							       =							layer_norm

						lowerCamelCase_							:		Optional[Any]							       =							nn.Convad(
						    __lowercase			,	__lowercase			,	kernel_size=(patch_size, patch_size)			,	stride=__lowercase			,	bias=__lowercase )
						if layer_norm:
							lowerCamelCase_							:		Tuple							       =							nn.LayerNorm(__lowercase			,	elementwise_affine=__lowercase			,	eps=1E-6 )
						else:
							lowerCamelCase_							:		Optional[int]							       =							None

						lowerCamelCase_							:		Optional[int]							       =							get_ad_sincos_pos_embed(__lowercase			,	int(num_patches**0.5 ) )
						self.register_buffer('pos_embed'			,	torch.from_numpy(__lowercase ).float().unsqueeze(0 )			,	persistent=__lowercase )





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							self.proj(__lowercase )
						if self.flatten:
							lowerCamelCase_							:		Optional[int]							       =							latent.flatten(2 ).transpose(1			,	2 )  # BCHW -> BNC
						if self.layer_norm:
							lowerCamelCase_							:		Optional[Any]							       =							self.norm(__lowercase )
						return latent + self.pos_embed


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase = "silu"			,	__lowercase = None			,	__lowercase = None			,	__lowercase=None			,	):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		Dict							       =							nn.Linear(__lowercase			,	__lowercase )

						if cond_proj_dim is not None:
							lowerCamelCase_							:		Any							       =							nn.Linear(__lowercase			,	__lowercase			,	bias=__lowercase )
						else:
							lowerCamelCase_							:		List[str]							       =							None

						lowerCamelCase_							:		str							       =							get_activation(__lowercase )

						if out_dim is not None:
							lowerCamelCase_							:		Optional[int]							       =							out_dim
						else:
							lowerCamelCase_							:		str							       =							time_embed_dim
						lowerCamelCase_							:		List[str]							       =							nn.Linear(__lowercase			,	__lowercase )

						if post_act_fn is None:
							lowerCamelCase_							:		str							       =							None
						else:
							lowerCamelCase_							:		List[str]							       =							get_activation(__lowercase )





					def      lowercase_							(    self			,	__lowercase			,	__lowercase=None ):







						'''simple docstring'''




						if condition is not None:
							lowerCamelCase_							:		Dict							       =							sample + self.cond_proj(__lowercase )
						lowerCamelCase_							:		List[Any]							       =							self.linear_a(__lowercase )

						if self.act is not None:
							lowerCamelCase_							:		Optional[int]							       =							self.act(__lowercase )

						lowerCamelCase_							:		Optional[Any]							       =							self.linear_a(__lowercase )

						if self.post_act is not None:
							lowerCamelCase_							:		Dict							       =							self.post_act(__lowercase )
						return sample


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		str							       =							num_channels
						lowerCamelCase_							:		Tuple							       =							flip_sin_to_cos
						lowerCamelCase_							:		List[str]							       =							downscale_freq_shift





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							get_timestep_embedding(
						    __lowercase			,	self.num_channels			,	flip_sin_to_cos=self.flip_sin_to_cos			,	downscale_freq_shift=self.downscale_freq_shift			,	)
						return t_emb


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 256			,	__lowercase = 1.0			,	__lowercase=True			,	__lowercase=True			,	__lowercase=False ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		Tuple							       =							nn.Parameter(torch.randn(__lowercase ) * scale			,	requires_grad=__lowercase )
						lowerCamelCase_							:		Any							       =							log
						lowerCamelCase_							:		List[str]							       =							flip_sin_to_cos

						if set_W_to_weight:
							# to delete later
							lowerCamelCase_							:		Optional[Any]							       =							nn.Parameter(torch.randn(__lowercase ) * scale			,	requires_grad=__lowercase )

							lowerCamelCase_							:		Union[str, Any]							       =							self.W





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						if self.log:
							lowerCamelCase_							:		List[Any]							       =							torch.log(__lowercase )

						lowerCamelCase_							:		Optional[Any]							       =							x[:, None] * self.weight[None, :] * 2 * np.pi

						if self.flip_sin_to_cos:
							lowerCamelCase_							:		int							       =							torch.cat([torch.cos(__lowercase ), torch.sin(__lowercase )]			,	dim=-1 )
						else:
							lowerCamelCase_							:		Optional[int]							       =							torch.cat([torch.sin(__lowercase ), torch.cos(__lowercase )]			,	dim=-1 )
						return out


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		str							       =							height
						lowerCamelCase_							:		str							       =							width
						lowerCamelCase_							:		List[Any]							       =							num_embed
						lowerCamelCase_							:		List[Any]							       =							embed_dim

						lowerCamelCase_							:		List[Any]							       =							nn.Embedding(self.num_embed			,	__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							nn.Embedding(self.height			,	__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							nn.Embedding(self.width			,	__lowercase )





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							self.emb(__lowercase )

						lowerCamelCase_							:		str							       =							self.height_emb(torch.arange(self.height			,	device=index.device ).view(1			,	self.height ) )

						# 1 x H x D -> 1 x H x 1 x D
						lowerCamelCase_							:		Dict							       =							height_emb.unsqueeze(2 )

						lowerCamelCase_							:		List[str]							       =							self.width_emb(torch.arange(self.width			,	device=index.device ).view(1			,	self.width ) )

						# 1 x W x D -> 1 x 1 x W x D
						lowerCamelCase_							:		Optional[Any]							       =							width_emb.unsqueeze(1 )

						lowerCamelCase_							:		Union[str, Any]							       =							height_emb + width_emb

						# 1 x H x W x D -> 1 x L xD
						lowerCamelCase_							:		Union[str, Any]							       =							pos_emb.view(1			,	self.height * self.width			,	-1 )

						lowerCamelCase_							:		Any							       =							emb + pos_emb[:, : emb.shape[1], :]

						return emb


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		List[Any]							       =							dropout_prob > 0
						lowerCamelCase_							:		Any							       =							nn.Embedding(num_classes + use_cfg_embedding			,	__lowercase )
						lowerCamelCase_							:		str							       =							num_classes
						lowerCamelCase_							:		Optional[int]							       =							dropout_prob

					def      lowercase_							(    self			,	__lowercase			,	__lowercase=None ):







						'''simple docstring'''




						if force_drop_ids is None:
							lowerCamelCase_							:		Optional[int]							       =							torch.rand(labels.shape[0]			,	device=labels.device ) < self.dropout_prob
						else:
							lowerCamelCase_							:		Optional[int]							       =							torch.tensor(force_drop_ids == 1 )
						lowerCamelCase_							:		int							       =							torch.where(__lowercase			,	self.num_classes			,	__lowercase )
						return labels





					def      lowercase_							(    self			,	__lowercase			,	__lowercase=None ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.dropout_prob > 0
						if (self.training and use_dropout) or (force_drop_ids is not None):
							lowerCamelCase_							:		Union[str, Any]							       =							self.token_drop(__lowercase			,	__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							self.embedding_table(__lowercase )
						return embeddings


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 1_024			,	__lowercase = 768			,	__lowercase = 768			,	__lowercase = 10			,	):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		Union[str, Any]							       =							num_image_text_embeds
						lowerCamelCase_							:		Tuple							       =							nn.Linear(__lowercase			,	self.num_image_text_embeds * cross_attention_dim )
						lowerCamelCase_							:		Optional[int]							       =							nn.Linear(__lowercase			,	__lowercase )





					def      lowercase_							(    self			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							text_embeds.shape[0]

						# image
						lowerCamelCase_							:		Optional[Any]							       =							self.image_embeds(__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							image_text_embeds.reshape(__lowercase			,	self.num_image_text_embeds			,	-1 )

						# text
						lowerCamelCase_							:		str							       =							self.text_proj(__lowercase )

						return torch.cat([image_text_embeds, text_embeds]			,	dim=1 )


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 768			,	__lowercase = 768			,	__lowercase = 32			,	):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		int							       =							num_image_text_embeds
						lowerCamelCase_							:		Optional[int]							       =							nn.Linear(__lowercase			,	self.num_image_text_embeds * cross_attention_dim )
						lowerCamelCase_							:		Optional[int]							       =							nn.LayerNorm(__lowercase )





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							image_embeds.shape[0]

						# image
						lowerCamelCase_							:		str							       =							self.image_embeds(__lowercase )
						lowerCamelCase_							:		Dict							       =							image_embeds.reshape(__lowercase			,	self.num_image_text_embeds			,	-1 )
						lowerCamelCase_							:		Optional[Any]							       =							self.norm(__lowercase )
						return image_embeds


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase=0.1 ):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		Any							       =							Timesteps(num_channels=256			,	flip_sin_to_cos=__lowercase			,	downscale_freq_shift=1 )
						lowerCamelCase_							:		Tuple							       =							TimestepEmbedding(in_channels=256			,	time_embed_dim=__lowercase )
						lowerCamelCase_							:		Dict							       =							LabelEmbedding(__lowercase			,	__lowercase			,	__lowercase )





					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase=None ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							self.time_proj(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							self.timestep_embedder(timesteps_proj.to(dtype=__lowercase ) )  # (N, D)

						lowerCamelCase_							:		int							       =							self.class_embedder(__lowercase )  # (N, D)

						lowerCamelCase_							:		int							       =							timesteps_emb + class_labels  # (N, D)

						return conditioning


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase = 64 ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		Optional[Any]							       =							nn.LayerNorm(__lowercase )
						lowerCamelCase_							:		str							       =							AttentionPooling(__lowercase			,	__lowercase )
						lowerCamelCase_							:		List[Any]							       =							nn.Linear(__lowercase			,	__lowercase )
						lowerCamelCase_							:		Dict							       =							nn.LayerNorm(__lowercase )





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							self.norma(__lowercase )
						lowerCamelCase_							:		str							       =							self.pool(__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							self.proj(__lowercase )
						lowerCamelCase_							:		Dict							       =							self.norma(__lowercase )
						return hidden_states


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 768			,	__lowercase = 768			,	__lowercase = 1_536 ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		List[Any]							       =							nn.Linear(__lowercase			,	__lowercase )
						lowerCamelCase_							:		Dict							       =							nn.LayerNorm(__lowercase )
						lowerCamelCase_							:		Any							       =							nn.Linear(__lowercase			,	__lowercase )





					def      lowercase_							(    self			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.text_proj(__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							self.text_norm(__lowercase )

						# image
						lowerCamelCase_							:		Optional[int]							       =							self.image_proj(__lowercase )

						return time_image_embeds + time_text_embeds


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 768			,	__lowercase = 1_536 ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		List[Any]							       =							nn.Linear(__lowercase			,	__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							nn.LayerNorm(__lowercase )





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.image_proj(__lowercase )
						lowerCamelCase_							:		List[Any]							       =							self.image_norm(__lowercase )
						return time_image_embeds


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 768			,	__lowercase = 1_536 ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		Any							       =							nn.Linear(__lowercase			,	__lowercase )
						lowerCamelCase_							:		str							       =							nn.LayerNorm(__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							nn.Sequential(
						    nn.Convad(3			,	16			,	3			,	padding=1 )			,	nn.SiLU()			,	nn.Convad(16			,	16			,	3			,	padding=1 )			,	nn.SiLU()			,	nn.Convad(16			,	32			,	3			,	padding=1			,	stride=2 )			,	nn.SiLU()			,	nn.Convad(32			,	32			,	3			,	padding=1 )			,	nn.SiLU()			,	nn.Convad(32			,	96			,	3			,	padding=1			,	stride=2 )			,	nn.SiLU()			,	nn.Convad(96			,	96			,	3			,	padding=1 )			,	nn.SiLU()			,	nn.Convad(96			,	256			,	3			,	padding=1			,	stride=2 )			,	nn.SiLU()			,	nn.Convad(256			,	4			,	3			,	padding=1 )			,	)





					def      lowercase_							(    self			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.image_proj(__lowercase )
						lowerCamelCase_							:		int							       =							self.image_norm(__lowercase )
						lowerCamelCase_							:		List[Any]							       =							self.input_hint_block(__lowercase )
						return time_image_embeds, hint


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase=None ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		Tuple							       =							dtype
						lowerCamelCase_							:		Optional[Any]							       =							nn.Parameter(torch.randn(1			,	__lowercase ) / embed_dim**0.5 )
						lowerCamelCase_							:		Tuple							       =							nn.Linear(__lowercase			,	__lowercase			,	dtype=self.dtype )
						lowerCamelCase_							:		List[str]							       =							nn.Linear(__lowercase			,	__lowercase			,	dtype=self.dtype )
						lowerCamelCase_							:		Union[str, Any]							       =							nn.Linear(__lowercase			,	__lowercase			,	dtype=self.dtype )
						lowerCamelCase_							:		str							       =							num_heads
						lowerCamelCase_							:		Dict							       =							embed_dim // self.num_heads





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_       ,      lowerCamelCase_       ,      lowerCamelCase_							:		Union[str, Any]							       =							x.size()

						def shape(__lowercase ):
							# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
							lowerCamelCase_							:		Union[str, Any]							       =							x.view(__lowercase			,	-1			,	self.num_heads			,	self.dim_per_head )
							# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
							lowerCamelCase_							:		Any							       =							x.transpose(1			,	2 )
							# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
							lowerCamelCase_							:		str							       =							x.reshape(bs * self.num_heads			,	-1			,	self.dim_per_head )
							# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
							lowerCamelCase_							:		Tuple							       =							x.transpose(1			,	2 )
							return x

						lowerCamelCase_							:		Optional[int]							       =							x.mean(dim=1			,	keepdim=__lowercase ) + self.positional_embedding.to(x.dtype )
						lowerCamelCase_							:		Tuple							       =							torch.cat([class_token, x]			,	dim=1 )  # (bs, length+1, width)

						# (bs*n_heads, class_token_length, dim_per_head)
						lowerCamelCase_							:		str							       =							shape(self.q_proj(__lowercase ) )
						# (bs*n_heads, length+class_token_length, dim_per_head)
						lowerCamelCase_							:		str							       =							shape(self.k_proj(__lowercase ) )
						lowerCamelCase_							:		Optional[Any]							       =							shape(self.v_proj(__lowercase ) )

						# (bs*n_heads, class_token_length, length+class_token_length):
						lowerCamelCase_							:		Dict							       =							1 / math.sqrt(math.sqrt(self.dim_per_head ) )
						lowerCamelCase_							:		Dict							       =							torch.einsum('bct,bcs->bts'			,	q * scale			,	k * scale )  # More stable with f16 than dividing afterwards
						lowerCamelCase_							:		int							       =							torch.softmax(weight.float()			,	dim=-1 ).type(weight.dtype )

						# (bs*n_heads, dim_per_head, class_token_length)
						lowerCamelCase_							:		Optional[int]							       =							torch.einsum('bts,bcs->bct'			,	__lowercase			,	__lowercase )

						# (bs, length+1, width)
						lowerCamelCase_							:		Dict							       =							a.reshape(__lowercase			,	-1			,	1 ).transpose(1			,	2 )

						return a[:, 0, :]  # cls_token






from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers.models.mpnet.modeling_tf_mpnet import (
							    TFMPNetForMaskedLM,
							    TFMPNetForMultipleChoice,
							    TFMPNetForQuestionAnswering,
							    TFMPNetForSequenceClassification,
							    TFMPNetForTokenClassification,
							    TFMPNetModel,
							)


class     __SCREAMING_SNAKE_CASE							:

					def __init__(    self			,	__lowercase			,	__lowercase=13			,	__lowercase=7			,	__lowercase=True			,	__lowercase=True			,	__lowercase=False			,	__lowercase=True			,	__lowercase=99			,	__lowercase=64			,	__lowercase=2			,	__lowercase=4			,	__lowercase=64			,	__lowercase="gelu"			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=512			,	__lowercase=16			,	__lowercase=2			,	__lowercase=0.02			,	__lowercase=3			,	__lowercase=4			,	__lowercase=None			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							parent
						lowerCamelCase_							:		Optional[Any]							       =							batch_size
						lowerCamelCase_							:		Union[str, Any]							       =							seq_length
						lowerCamelCase_							:		Optional[int]							       =							is_training
						lowerCamelCase_							:		Optional[int]							       =							use_input_mask
						lowerCamelCase_							:		str							       =							use_token_type_ids
						lowerCamelCase_							:		Optional[int]							       =							use_labels
						lowerCamelCase_							:		Dict							       =							vocab_size
						lowerCamelCase_							:		List[str]							       =							hidden_size
						lowerCamelCase_							:		Any							       =							num_hidden_layers
						lowerCamelCase_							:		int							       =							num_attention_heads
						lowerCamelCase_							:		Any							       =							intermediate_size
						lowerCamelCase_							:		Union[str, Any]							       =							hidden_act
						lowerCamelCase_							:		Any							       =							hidden_dropout_prob
						lowerCamelCase_							:		str							       =							attention_probs_dropout_prob
						lowerCamelCase_							:		Optional[Any]							       =							max_position_embeddings
						lowerCamelCase_							:		Dict							       =							type_vocab_size
						lowerCamelCase_							:		Any							       =							type_sequence_label_size
						lowerCamelCase_							:		Any							       =							initializer_range
						lowerCamelCase_							:		Tuple							       =							num_labels
						lowerCamelCase_							:		List[str]							       =							num_choices
						lowerCamelCase_							:		Optional[Any]							       =							scope

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							ids_tensor([self.batch_size, self.seq_length]			,	self.vocab_size )

						lowerCamelCase_							:		Optional[int]							       =							None
						if self.use_input_mask:
							lowerCamelCase_							:		List[Any]							       =							random_attention_mask([self.batch_size, self.seq_length] )

						lowerCamelCase_							:		Optional[Any]							       =							None
						lowerCamelCase_							:		Any							       =							None
						lowerCamelCase_							:		int							       =							None
						if self.use_labels:
							lowerCamelCase_							:		int							       =							ids_tensor([self.batch_size]			,	self.type_sequence_label_size )
							lowerCamelCase_							:		Optional[Any]							       =							ids_tensor([self.batch_size, self.seq_length]			,	self.num_labels )
							lowerCamelCase_							:		str							       =							ids_tensor([self.batch_size]			,	self.num_choices )

						lowerCamelCase_							:		Any							       =							MPNetConfig(
						    vocab_size=self.vocab_size			,	hidden_size=self.hidden_size			,	num_hidden_layers=self.num_hidden_layers			,	num_attention_heads=self.num_attention_heads			,	intermediate_size=self.intermediate_size			,	hidden_act=self.hidden_act			,	hidden_dropout_prob=self.hidden_dropout_prob			,	attention_probs_dropout_prob=self.attention_probs_dropout_prob			,	max_position_embeddings=self.max_position_embeddings			,	initializer_range=self.initializer_range			,	)
						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							TFMPNetModel(config=__lowercase )
						lowerCamelCase_							:		str							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		str							       =							model(__lowercase )
						lowerCamelCase_							:		Dict							       =							[input_ids, input_mask]
						lowerCamelCase_							:		Dict							       =							model(__lowercase )
						self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							TFMPNetForMaskedLM(config=__lowercase )
						lowerCamelCase_							:		List[Any]							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		int							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.vocab_size) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							TFMPNetForQuestionAnswering(config=__lowercase )
						lowerCamelCase_							:		Tuple							       =							{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						}
						lowerCamelCase_							:		Optional[Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.start_logits.shape			,	(self.batch_size, self.seq_length) )
						self.parent.assertEqual(result.end_logits.shape			,	(self.batch_size, self.seq_length) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							self.num_labels
						lowerCamelCase_							:		Union[str, Any]							       =							TFMPNetForSequenceClassification(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		List[Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.num_choices
						lowerCamelCase_							:		Tuple							       =							TFMPNetForMultipleChoice(__lowercase )
						lowerCamelCase_							:		Dict							       =							tf.tile(tf.expand_dims(__lowercase			,	1 )			,	(1, self.num_choices, 1) )
						lowerCamelCase_							:		Any							       =							tf.tile(tf.expand_dims(__lowercase			,	1 )			,	(1, self.num_choices, 1) )
						lowerCamelCase_							:		Optional[Any]							       =							{
						    'input_ids': multiple_choice_inputs_ids,
						    'attention_mask': multiple_choice_input_mask,
						}
						lowerCamelCase_							:		Union[str, Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_choices) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							self.num_labels
						lowerCamelCase_							:		str							       =							TFMPNetForTokenClassification(__lowercase )
						lowerCamelCase_							:		Any							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		str							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.num_labels) )





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.prepare_config_and_inputs()
						((lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_))							:		Tuple							       =							config_and_inputs
						lowerCamelCase_							:		str							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict


@require_tf
class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					lowerCamelCase       ,					unittest.TestCase  ):
					_lowerCamelCase		  =			(
					    (
					        TFMPNetForMaskedLM,
					        TFMPNetForMultipleChoice,
					        TFMPNetForQuestionAnswering,
					        TFMPNetForSequenceClassification,
					        TFMPNetForTokenClassification,
					        TFMPNetModel,
					    )
					    if is_tf_available()
					    else ()
					)
					_lowerCamelCase		  =			(
					    {
					        """feature-extraction""": TFMPNetModel,
					        """fill-mask""": TFMPNetForMaskedLM,
					        """question-answering""": TFMPNetForQuestionAnswering,
					        """text-classification""": TFMPNetForSequenceClassification,
					        """token-classification""": TFMPNetForTokenClassification,
					        """zero-shot""": TFMPNetForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					_lowerCamelCase		  =			False
					_lowerCamelCase		  =			False

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							TFMPNetModelTester(self )
						lowerCamelCase_							:		str							       =							ConfigTester(self			,	config_class=__lowercase			,	hidden_size=37 )

					def      lowercase_							(    self ):







						'''simple docstring'''




						self.config_tester.run_common_tests()

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_model(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_masked_lm(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_question_answering(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_token_classification(*__lowercase )





					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						for model_name in ["microsoft/mpnet-base"]:
							lowerCamelCase_							:		str							       =							TFMPNetModel.from_pretrained(__lowercase )
							self.assertIsNotNone(__lowercase )


@require_tf
class     __SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							TFMPNetModel.from_pretrained('microsoft/mpnet-base' )
						lowerCamelCase_							:		Optional[int]							       =							tf.constant([[0, 1, 2, 3, 4, 5]] )
						lowerCamelCase_							:		str							       =							model(__lowercase )[0]

						lowerCamelCase_							:		List[Any]							       =							[1, 6, 768]
						self.assertEqual(output.shape			,	__lowercase )

						lowerCamelCase_							:		Any							       =							tf.constant(
						    [
						        [
						            [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
						            [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
						            [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
						        ]
						    ] )
						tf.debugging.assert_near(output[:, :3, :3]			,	__lowercase			,	atol=1E-4 )
