



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  __lowerCAmelCase			(					lowerCamelCase__	):
				__lowerCamelCase			=    42





class  __lowerCAmelCase			(					lowerCamelCase__	,						lowerCamelCase__	):




				@register_to_config
				def __init__(							self						,    _snake_case = 16						,    _snake_case = 88						,    _snake_case = None						,    _snake_case = None						,    _snake_case = 1						,    _snake_case = 0.0						,    _snake_case = 32						,    _snake_case = None						,    _snake_case = False						,    _snake_case = None						,    _snake_case = None						,    _snake_case = None						,    _snake_case = "geglu"						,    _snake_case = None						,    _snake_case = False						,    _snake_case = False						,    _snake_case = False						,    _snake_case = "layer_norm"						,    _snake_case = True						,    ):



									"""simple docstring"""





									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														    F'The configuration file of this model: {self.__class__} is outdated.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"""						,    _snake_case						,    standard_warn=_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=_snake_case						,    num_channels=_snake_case						,    eps=1e-6						,    affine=_snake_case		)
														if use_linear_projection:
																			_lowerCAmelCase      =   nn.Linear(_snake_case						,    _snake_case		)
														else:
																			_lowerCAmelCase      =   nn.Convad(_snake_case						,    _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=_snake_case						,    embed_dim=_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=_snake_case						,    width=_snake_case						,    patch_size=_snake_case						,    in_channels=_snake_case						,    embed_dim=_snake_case						,    )

									# 3. Define transformers blocks
									_lowerCAmelCase      =   nn.ModuleList(
									    [
									        BasicTransformerBlock(
									            _snake_case						,    _snake_case						,    _snake_case						,    dropout=_snake_case						,    cross_attention_dim=_snake_case						,    activation_fn=_snake_case						,    num_embeds_ada_norm=_snake_case						,    attention_bias=_snake_case						,    only_cross_attention=_snake_case						,    upcast_attention=_snake_case						,    norm_type=_snake_case						,    norm_elementwise_affine=_snake_case						,    )
									        for d in range(_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(_snake_case						,    _snake_case		)
														else:
																			_lowerCAmelCase      =   nn.Convad(_snake_case						,    _snake_case						,    kernel_size=1						,    stride=1						,    padding=0		)
									elif self.is_input_vectorized:
														_lowerCAmelCase      =   nn.LayerNorm(_snake_case		)
														_lowerCAmelCase      =   nn.Linear(_snake_case						,    self.num_vector_embeds - 1		)
									elif self.is_input_patches:
														_lowerCAmelCase      =   nn.LayerNorm(_snake_case						,    elementwise_affine=_snake_case						,    eps=1e-6		)
														_lowerCAmelCase      =   nn.Linear(_snake_case						,    2 * inner_dim		)
														_lowerCAmelCase      =   nn.Linear(_snake_case						,    patch_size * patch_size * self.out_channels		)




				def 							snake_case       (							self						,    _snake_case						,    _snake_case = None						,    _snake_case = None						,    _snake_case = None						,    _snake_case = None						,    _snake_case = None						,    _snake_case = None						,    _snake_case = True						,    ):



									"""simple docstring"""





									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_0000.0
														_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_0000.0
														_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(_snake_case		)
														if not self.use_linear_projection:
																			_lowerCAmelCase      =   self.proj_in(_snake_case		)
																			_lowerCAmelCase      =   hidden_states.shape[1]
																			_lowerCAmelCase      =   hidden_states.permute(0						,    2						,    3						,    1		).reshape(_snake_case						,    height * width						,    _snake_case		)
														else:
																			_lowerCAmelCase      =   hidden_states.shape[1]
																			_lowerCAmelCase      =   hidden_states.permute(0						,    2						,    3						,    1		).reshape(_snake_case						,    height * width						,    _snake_case		)
																			_lowerCAmelCase      =   self.proj_in(_snake_case		)
									elif self.is_input_vectorized:
														_lowerCAmelCase      =   self.latent_image_embedding(_snake_case		)
									elif self.is_input_patches:
														_lowerCAmelCase      =   self.pos_embed(_snake_case		)

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

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

														_lowerCAmelCase      =   hidden_states + residual
									elif self.is_input_vectorized:
														_lowerCAmelCase      =   self.norm_out(_snake_case		)
														_lowerCAmelCase      =   self.out(_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(
														    _snake_case						,    _snake_case						,    hidden_dtype=hidden_states.dtype		)
														_lowerCAmelCase     ,     _lowerCAmelCase      =   self.proj_out_a(F.silu(_snake_case		)		).chunk(2						,    dim=1		)
														_lowerCAmelCase      =   self.norm_out(_snake_case		) * (1 + scale[:, None]) + shift[:, None]
														_lowerCAmelCase      =   self.proj_out_a(_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"""						,    _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=_snake_case		)








import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
	from PIL import Image





class  __lowerCAmelCase			(					unittest.TestCase	,						lowerCamelCase__	):




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   load_tool("""image-question-answering"""		)
									self.tool.setup()
									_lowerCAmelCase      =   load_tool("""image-question-answering"""						,    remote=_snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.tool(_snake_case						,    """How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.remote_tool(_snake_case						,    """How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.tool(image=_snake_case						,    question="""How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.remote_tool(image=_snake_case						,    question="""How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)



