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

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin




@dataclass
class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  : torch.FloatTensor




class        lowercase_  (  UpperCamelCase_				,					UpperCamelCase_						):



	"""simple docstring"""





	@register_to_config
	def __init__(							self		,      __SCREAMING_SNAKE_CASE = 16		,      __SCREAMING_SNAKE_CASE = 88		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = 1		,      __SCREAMING_SNAKE_CASE = 0.0		,      __SCREAMING_SNAKE_CASE = 32		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = False		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = "geglu"		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = False		,      __SCREAMING_SNAKE_CASE = False		,      __SCREAMING_SNAKE_CASE = False		,      __SCREAMING_SNAKE_CASE = "layer_norm"		,      __SCREAMING_SNAKE_CASE = True		,      )						->Optional[int]:
								super().__init__()
								lowerCAmelCase     		=		use_linear_projection
								lowerCAmelCase     		=		num_attention_heads
								lowerCAmelCase     		=		attention_head_dim
								lowerCAmelCase     		=		num_attention_heads * attention_head_dim

								# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
								# Define whether input is continuous or discrete depending on configuration
								lowerCAmelCase     		=		(in_channels is not None) and (patch_size is None)
								lowerCAmelCase     		=		num_vector_embeds is not None
								lowerCAmelCase     		=		in_channels is not None and patch_size is not None

								if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
															lowerCAmelCase     		=		(
															    F"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
															    ''' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'''
															    ''' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'''
															    ''' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'''
															    ''' would be very nice if you could open a Pull request for the `transformer/config.json` file'''
															)
															deprecate('''norm_type!=num_embeds_ada_norm'''		,      '''1.0.0'''		,      __SCREAMING_SNAKE_CASE		,      standard_warn=__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		'''ada_norm'''

								if self.is_input_continuous and self.is_input_vectorized:
															raise ValueError(
															    F"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
															    ''' sure that either `in_channels` or `num_vector_embeds` is None.'''    )
								elif self.is_input_vectorized and self.is_input_patches:
															raise ValueError(
															    F"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
															    ''' sure that either `num_vector_embeds` or `num_patches` is None.'''    )
								elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
															raise ValueError(
															    F"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
															    F" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."    )

								# 2. Define input layers
								if self.is_input_continuous:
															lowerCAmelCase     		=		in_channels

															lowerCAmelCase     		=		torch.nn.GroupNorm(num_groups=__SCREAMING_SNAKE_CASE		,      num_channels=__SCREAMING_SNAKE_CASE		,      eps=1e-6		,      affine=__SCREAMING_SNAKE_CASE    )
															if use_linear_projection:
																						lowerCAmelCase     		=		nn.Linear(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )
															else:
																						lowerCAmelCase     		=		nn.Convad(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      kernel_size=1		,      stride=1		,      padding=0    )
								elif self.is_input_vectorized:
															assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
															assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

															lowerCAmelCase     		=		sample_size
															lowerCAmelCase     		=		sample_size
															lowerCAmelCase     		=		num_vector_embeds
															lowerCAmelCase     		=		self.height * self.width

															lowerCAmelCase     		=		ImagePositionalEmbeddings(
															    num_embed=__SCREAMING_SNAKE_CASE		,      embed_dim=__SCREAMING_SNAKE_CASE		,      height=self.height		,      width=self.width    )
								elif self.is_input_patches:
															assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

															lowerCAmelCase     		=		sample_size
															lowerCAmelCase     		=		sample_size

															lowerCAmelCase     		=		patch_size
															lowerCAmelCase     		=		PatchEmbed(
															    height=__SCREAMING_SNAKE_CASE		,      width=__SCREAMING_SNAKE_CASE		,      patch_size=__SCREAMING_SNAKE_CASE		,      in_channels=__SCREAMING_SNAKE_CASE		,      embed_dim=__SCREAMING_SNAKE_CASE		,      )

								# 3. Define transformers blocks
								lowerCAmelCase     		=		nn.ModuleList(
								    [
								        BasicTransformerBlock(
								            __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      dropout=__SCREAMING_SNAKE_CASE		,      cross_attention_dim=__SCREAMING_SNAKE_CASE		,      activation_fn=__SCREAMING_SNAKE_CASE		,      num_embeds_ada_norm=__SCREAMING_SNAKE_CASE		,      attention_bias=__SCREAMING_SNAKE_CASE		,      only_cross_attention=__SCREAMING_SNAKE_CASE		,      upcast_attention=__SCREAMING_SNAKE_CASE		,      norm_type=__SCREAMING_SNAKE_CASE		,      norm_elementwise_affine=__SCREAMING_SNAKE_CASE		,      )
								        for d in range(__SCREAMING_SNAKE_CASE    )
								    ]    )

								# 4. Define output layers
								lowerCAmelCase     		=		in_channels if out_channels is None else out_channels
								if self.is_input_continuous:
															# TODO: should use out_channels for continuous projections
															if use_linear_projection:
																						lowerCAmelCase     		=		nn.Linear(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )
															else:
																						lowerCAmelCase     		=		nn.Convad(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      kernel_size=1		,      stride=1		,      padding=0    )
								elif self.is_input_vectorized:
															lowerCAmelCase     		=		nn.LayerNorm(__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		nn.Linear(__SCREAMING_SNAKE_CASE		,      self.num_vector_embeds - 1    )
								elif self.is_input_patches:
															lowerCAmelCase     		=		nn.LayerNorm(__SCREAMING_SNAKE_CASE		,      elementwise_affine=__SCREAMING_SNAKE_CASE		,      eps=1e-6    )
															lowerCAmelCase     		=		nn.Linear(__SCREAMING_SNAKE_CASE		,      2 * inner_dim    )
															lowerCAmelCase     		=		nn.Linear(__SCREAMING_SNAKE_CASE		,      patch_size * patch_size * self.out_channels    )




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = True		,      )						->List[Any]:
								# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
								#   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
								#   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
								# expects mask of shape:
								#   [batch, key_tokens]
								# adds singleton query_tokens dimension:
								#   [batch,                    1, key_tokens]
								# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
								#   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
								#   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
								if attention_mask is not None and attention_mask.ndim == 2:
															# assume that mask is expressed as:
															#   (1 = keep,      0 = discard)
															# convert mask into a bias that can be added to attention scores:
															#       (keep = +0,     discard = -10000.0)
															lowerCAmelCase     		=		(1 - attention_mask.to(hidden_states.dtype    )) * -1_0_0_0_0.0
															lowerCAmelCase     		=		attention_mask.unsqueeze(1    )

								# convert encoder_attention_mask to a bias the same way we do for attention_mask
								if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
															lowerCAmelCase     		=		(1 - encoder_attention_mask.to(hidden_states.dtype    )) * -1_0_0_0_0.0
															lowerCAmelCase     		=		encoder_attention_mask.unsqueeze(1    )

								# 1. Input
								if self.is_input_continuous:
															lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		hidden_states.shape
															lowerCAmelCase     		=		hidden_states

															lowerCAmelCase     		=		self.norm(__SCREAMING_SNAKE_CASE    )
															if not self.use_linear_projection:
																						lowerCAmelCase     		=		self.proj_in(__SCREAMING_SNAKE_CASE    )
																						lowerCAmelCase     		=		hidden_states.shape[1]
																						lowerCAmelCase     		=		hidden_states.permute(0		,      2		,      3		,      1    ).reshape(__SCREAMING_SNAKE_CASE		,      height * width		,      __SCREAMING_SNAKE_CASE    )
															else:
																						lowerCAmelCase     		=		hidden_states.shape[1]
																						lowerCAmelCase     		=		hidden_states.permute(0		,      2		,      3		,      1    ).reshape(__SCREAMING_SNAKE_CASE		,      height * width		,      __SCREAMING_SNAKE_CASE    )
																						lowerCAmelCase     		=		self.proj_in(__SCREAMING_SNAKE_CASE    )
								elif self.is_input_vectorized:
															lowerCAmelCase     		=		self.latent_image_embedding(__SCREAMING_SNAKE_CASE    )
								elif self.is_input_patches:
															lowerCAmelCase     		=		self.pos_embed(__SCREAMING_SNAKE_CASE    )

								# 2. Blocks
								for block in self.transformer_blocks:
															lowerCAmelCase     		=		block(
															    __SCREAMING_SNAKE_CASE		,      attention_mask=__SCREAMING_SNAKE_CASE		,      encoder_hidden_states=__SCREAMING_SNAKE_CASE		,      encoder_attention_mask=__SCREAMING_SNAKE_CASE		,      timestep=__SCREAMING_SNAKE_CASE		,      cross_attention_kwargs=__SCREAMING_SNAKE_CASE		,      class_labels=__SCREAMING_SNAKE_CASE		,      )

								# 3. Output
								if self.is_input_continuous:
															if not self.use_linear_projection:
																						lowerCAmelCase     		=		hidden_states.reshape(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    ).permute(0		,      3		,      1		,      2    ).contiguous()
																						lowerCAmelCase     		=		self.proj_out(__SCREAMING_SNAKE_CASE    )
															else:
																						lowerCAmelCase     		=		self.proj_out(__SCREAMING_SNAKE_CASE    )
																						lowerCAmelCase     		=		hidden_states.reshape(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    ).permute(0		,      3		,      1		,      2    ).contiguous()

															lowerCAmelCase     		=		hidden_states + residual
								elif self.is_input_vectorized:
															lowerCAmelCase     		=		self.norm_out(__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		self.out(__SCREAMING_SNAKE_CASE    )
															# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
															lowerCAmelCase     		=		logits.permute(0		,      2		,      1    )

															# log(p(x_0))
															lowerCAmelCase     		=		F.log_softmax(logits.double()		,      dim=1    ).float()
								elif self.is_input_patches:
															# TODO: cleanup!
															lowerCAmelCase     		=		self.transformer_blocks[0].norma.emb(
															    __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      hidden_dtype=hidden_states.dtype    )
															lowerCAmelCase		,				lowerCAmelCase     		=		self.proj_out_a(F.silu(__SCREAMING_SNAKE_CASE    )    ).chunk(2		,      dim=1    )
															lowerCAmelCase     		=		self.norm_out(__SCREAMING_SNAKE_CASE    ) * (1 + scale[:, None]) + shift[:, None]
															lowerCAmelCase     		=		self.proj_out_a(__SCREAMING_SNAKE_CASE    )

															# unpatchify
															lowerCAmelCase     		=		lowerCAmelCase     		=		int(hidden_states.shape[1] ** 0.5    )
															lowerCAmelCase     		=		hidden_states.reshape(
															    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)    )
															lowerCAmelCase     		=		torch.einsum('''nhwpqc->nchpwq'''		,      __SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		hidden_states.reshape(
															    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)    )

								if not return_dict:
															return (output,)

								return TransformeraDModelOutput(sample=__SCREAMING_SNAKE_CASE    )




# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Union[str, Any]:
								return cls




	@slow
	@require_torch_gpu
	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase     		=		self.get_auto_remove_tmp_dir()
								lowerCAmelCase     		=		F"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

								# we want this test to run even if there is only one GPU, but if there are more we use them all
								lowerCAmelCase     		=		get_gpu_count()
								lowerCAmelCase     		=		F"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
								lowerCAmelCase     		=		[sys.executable] + distributed_args + args
								execute_subprocess_async(__SCREAMING_SNAKE_CASE		,      env=self.get_env()    )

								lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      '''test_bleu.json'''    )
								lowerCAmelCase     		=		load_json(__SCREAMING_SNAKE_CASE    )
								# print(metrics)
								self.assertGreaterEqual(metrics['''bleu''']		,      25    )



