

import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           EfficientFormerForImageClassification,
           EfficientFormerForImageClassificationWithTeacher,
           EfficientFormerModel,
       )
       from transformers.models.efficientformer.modeling_efficientformer import (
           EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
       )


if is_vision_available():
       from PIL import Image

       from transformers import EfficientFormerImageProcessor



class 					__snake_case     :

  def __init__(    self  ,						_A  ,						_A = 13  ,						_A = 64  ,						_A = 2  ,						_A = 3  ,						_A = 3  ,						_A = True  ,						_A = True  ,						_A = 128  ,						_A=[16, 32, 64, 128]  ,						_A = 7  ,						_A = 4  ,						_A = 37  ,						_A = "gelu"  ,						_A = 0.1  ,						_A = 0.1  ,						_A = 10  ,						_A = 0.0_2  ,						_A = 2  ,						_A = 1  ,						_A = 128  ,						_A = [2, 2, 2, 2]  ,						_A = 2  ,						_A = 2  ,						):
      SCREAMING_SNAKE_CASE_													=  parent
      SCREAMING_SNAKE_CASE_													=  batch_size
      SCREAMING_SNAKE_CASE_													=  image_size
      SCREAMING_SNAKE_CASE_													=  patch_size
      SCREAMING_SNAKE_CASE_													=  num_channels
      SCREAMING_SNAKE_CASE_													=  is_training
      SCREAMING_SNAKE_CASE_													=  use_labels
      SCREAMING_SNAKE_CASE_													=  hidden_size
      SCREAMING_SNAKE_CASE_													=  num_hidden_layers
      SCREAMING_SNAKE_CASE_													=  num_attention_heads
      SCREAMING_SNAKE_CASE_													=  intermediate_size
      SCREAMING_SNAKE_CASE_													=  hidden_act
      SCREAMING_SNAKE_CASE_													=  hidden_dropout_prob
      SCREAMING_SNAKE_CASE_													=  attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_													=  type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  initializer_range
      SCREAMING_SNAKE_CASE_													=  encoder_stride
      SCREAMING_SNAKE_CASE_													=  num_attention_outputs
      SCREAMING_SNAKE_CASE_													=  embed_dim
      SCREAMING_SNAKE_CASE_													=  embed_dim + 1
      SCREAMING_SNAKE_CASE_													=  resolution
      SCREAMING_SNAKE_CASE_													=  depths
      SCREAMING_SNAKE_CASE_													=  hidden_sizes
      SCREAMING_SNAKE_CASE_													=  dim
      SCREAMING_SNAKE_CASE_													=  mlp_expansion_ratio

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      SCREAMING_SNAKE_CASE_													=  None
      if self.use_labels:
          SCREAMING_SNAKE_CASE_													=  ids_tensor([self.batch_size]  ,						self.type_sequence_label_size)

      SCREAMING_SNAKE_CASE_													=  self.get_config()
      return config, pixel_values, labels

  def   lowerCAmelCase__   (    self):
      return EfficientFormerConfig(
          image_size=self.image_size  ,						patch_size=self.patch_size  ,						num_channels=self.num_channels  ,						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  ,						is_decoder=_A  ,						initializer_range=self.initializer_range  ,						encoder_stride=self.encoder_stride  ,						resolution=self.resolution  ,						depths=self.depths  ,						hidden_sizes=self.hidden_sizes  ,						dim=self.dim  ,						mlp_expansion_ratio=self.mlp_expansion_ratio  ,						)

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  EfficientFormerModel(config=_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE_													=  model(_A)
      self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size))

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  EfficientFormerForImageClassification(_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE_													=  model(_A  ,						labels=_A)
      self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size))

      # test greyscale images
      SCREAMING_SNAKE_CASE_													=  1
      SCREAMING_SNAKE_CASE_													=  EfficientFormerForImageClassification(_A)
      model.to(_A)
      model.eval()

      SCREAMING_SNAKE_CASE_													=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      SCREAMING_SNAKE_CASE_													=  model(_A)
      self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size))


  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_config_and_inputs()
      (
          (
          SCREAMING_SNAKE_CASE_
      )							,	(
          SCREAMING_SNAKE_CASE_
      )							,	(
          SCREAMING_SNAKE_CASE_
      )							,	
      )													=  config_and_inputs
      SCREAMING_SNAKE_CASE_													=  {'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 					__snake_case     (			lowerCAmelCase__   ,       lowerCAmelCase__   ,       unittest.TestCase				):
  __lowerCAmelCase   :		str         =     (
      (
          EfficientFormerModel,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerForImageClassification,
      )
      if is_torch_available()
      else ()
  )
  __lowerCAmelCase   :		Dict         =     (
      {
          'feature-extraction': EfficientFormerModel,
          'image-classification': (
              EfficientFormerForImageClassification,
              EfficientFormerForImageClassificationWithTeacher,
          ),
      }
      if is_torch_available()
      else {}
  )
  __lowerCAmelCase   :		List[str]         =     False

  __lowerCAmelCase   :		int         =     False
  __lowerCAmelCase   :		Dict         =     False
  __lowerCAmelCase   :		Dict         =     False

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  EfficientFormerModelTester(self)
      SCREAMING_SNAKE_CASE_													=  ConfigTester(
          self  ,						config_class=_A  ,						has_text_modality=_A  ,						hidden_size=37)

  def   lowerCAmelCase__   (    self):
      self.config_tester.run_common_tests()

  @unittest.skip(reason='EfficientFormer does not use inputs_embeds')
  def   lowerCAmelCase__   (    self):
      pass

  @unittest.skip(reason='EfficientFormer does not support input and output embeddings')
  def   lowerCAmelCase__   (    self):
      pass

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_													=  model_class(_A)
          SCREAMING_SNAKE_CASE_													=  inspect.signature(model.forward)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          SCREAMING_SNAKE_CASE_													=  [*signature.parameters.keys()]

          SCREAMING_SNAKE_CASE_													=  ['pixel_values']
          self.assertListEqual(arg_names[:1]  ,						_A)

  def   lowerCAmelCase__   (    self):
      def check_hidden_states_output(_A  ,						_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  model_class(_A)
          model.to(_A)
          model.eval()

          with torch.no_grad():
              SCREAMING_SNAKE_CASE_													=  model(**self._prepare_for_class(_A  ,						_A))

          SCREAMING_SNAKE_CASE_													=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          SCREAMING_SNAKE_CASE_													=  getattr(
              self.model_tester  ,						'expected_num_hidden_layers'  ,						self.model_tester.num_hidden_layers + 1)
          self.assertEqual(len(_A)  ,						_A)

          if hasattr(self.model_tester  ,						'encoder_seq_length'):
              SCREAMING_SNAKE_CASE_													=  self.model_tester.encoder_seq_length
              if hasattr(self.model_tester  ,						'chunk_length') and self.model_tester.chunk_length > 1:
                  SCREAMING_SNAKE_CASE_													=  seq_length * self.model_tester.chunk_length
          else:
              SCREAMING_SNAKE_CASE_													=  self.model_tester.seq_length

          self.assertListEqual(
              list(hidden_states[-1].shape[-2:])  ,						[seq_length, self.model_tester.hidden_size]  ,						)

          if config.is_encoder_decoder:
              SCREAMING_SNAKE_CASE_													=  outputs.decoder_hidden_states

              self.assertIsInstance(_A  ,						(list, tuple))
              self.assertEqual(len(_A)  ,						_A)
              SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'seq_length'  ,						_A)
              SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'decoder_seq_length'  ,						_A)

              self.assertListEqual(
                  list(hidden_states[-1].shape[-2:])  ,						[decoder_seq_length, self.model_tester.hidden_size]  ,						)

      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_													=  True
          check_hidden_states_output(_A  ,						_A  ,						_A)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          SCREAMING_SNAKE_CASE_													=  True

          check_hidden_states_output(_A  ,						_A  ,						_A)

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A=False):
      SCREAMING_SNAKE_CASE_													=  super()._prepare_for_class(_A  ,						_A  ,						return_labels=_A)

      if return_labels:
          if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
              del inputs_dict["labels"]

      return inputs_dict

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A)

  @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet')
  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*_A)

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_A)

  def   lowerCAmelCase__   (    self):
      if not self.model_tester.is_training:
          return

      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE_													=  True

      for model_class in self.all_model_classes:
          # EfficientFormerForImageClassificationWithTeacher supports inference-only
          if (
              model_class in get_values(_A)
              or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
          ):
              continue
          SCREAMING_SNAKE_CASE_													=  model_class(_A)
          model.to(_A)
          model.train()
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          SCREAMING_SNAKE_CASE_													=  model(**_A).loss
          loss.backward()

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_													=  [
          {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
          {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
          {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
      ]

      for model_class in self.all_model_classes:
          if (
              model_class
              not in [
                  *get_values(_A),
              ]
              or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
          ):
              continue

          for problem_type in problem_types:
              with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}"""):
                  SCREAMING_SNAKE_CASE_													=  problem_type['title']
                  SCREAMING_SNAKE_CASE_													=  problem_type['num_labels']

                  SCREAMING_SNAKE_CASE_													=  model_class(_A)
                  model.to(_A)
                  model.train()

                  SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)

                  if problem_type["num_labels"] > 1:
                      SCREAMING_SNAKE_CASE_													=  inputs['labels'].unsqueeze(1).repeat(1  ,						problem_type['num_labels'])

                  SCREAMING_SNAKE_CASE_													=  inputs['labels'].to(problem_type['dtype'])

                  # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                  # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                  # they have the same size." which is a symptom something in wrong for the regression problem.
                  # See https://github.com/huggingface/transformers/issues/11780
                  with warnings.catch_warnings(record=_A) as warning_list:
                      SCREAMING_SNAKE_CASE_													=  model(**_A).loss
                  for w in warning_list:
                      if "Using a target size that is different to the input size" in str(w.message):
                          raise ValueError(
                              f"""Something is going wrong in the regression problem: intercepted {w.message}""")

                  loss.backward()

  @slow
  def   lowerCAmelCase__   (    self):
      for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          SCREAMING_SNAKE_CASE_													=  EfficientFormerModel.from_pretrained(_A)
          self.assertIsNotNone(_A)


  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE_													=  True

      SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'seq_length'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'encoder_seq_length'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'key_length'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'chunk_length'  ,						_A)

      if chunk_length is not None and hasattr(self.model_tester  ,						'num_hashes'):
          SCREAMING_SNAKE_CASE_													=  encoder_seq_length * self.model_tester.num_hashes

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_													=  True
          SCREAMING_SNAKE_CASE_													=  False
          SCREAMING_SNAKE_CASE_													=  True
          SCREAMING_SNAKE_CASE_													=  model_class(_A)
          model.to(_A)
          model.eval()
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_													=  model(**self._prepare_for_class(_A  ,						_A))
          SCREAMING_SNAKE_CASE_													=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(_A)  ,						self.model_tester.num_attention_outputs)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          SCREAMING_SNAKE_CASE_													=  True
          SCREAMING_SNAKE_CASE_													=  model_class(_A)
          model.to(_A)
          model.eval()
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_													=  model(**self._prepare_for_class(_A  ,						_A))
          SCREAMING_SNAKE_CASE_													=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(_A)  ,						self.model_tester.num_attention_outputs)

          if chunk_length is not None:
              self.assertListEqual(
                  list(attentions[0].shape[-4:])  ,						[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]  ,						)
          else:
              self.assertListEqual(
                  list(attentions[0].shape[-3:])  ,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]  ,						)






def     _UpperCAmelCase				(     ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image





@require_torch
@require_vision
class 					__snake_case     (			unittest.TestCase				):

  @cached_property
  def   lowerCAmelCase__   (    self):
      return (
          EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300')
          if is_vision_available()
          else None
      )

  @slow
  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300').to(
          _A)

      SCREAMING_SNAKE_CASE_													=  self.default_image_processor
      SCREAMING_SNAKE_CASE_													=  prepare_img()
      SCREAMING_SNAKE_CASE_													=  image_processor(images=_A  ,						return_tensors='pt').to(_A)

      # forward pass
      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**_A)

      # verify the logits
      SCREAMING_SNAKE_CASE_													=  (1, 1000)
      self.assertEqual(outputs.logits.shape  ,						_A)

      SCREAMING_SNAKE_CASE_													=  torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]).to(_A)
      self.assertTrue(torch.allclose(outputs.logits[0][:3]  ,						_A  ,						atol=1E-4))


  @slow
  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  EfficientFormerForImageClassificationWithTeacher.from_pretrained(
          'snap-research/efficientformer-l1-300').to(_A)

      SCREAMING_SNAKE_CASE_													=  self.default_image_processor
      SCREAMING_SNAKE_CASE_													=  prepare_img()
      SCREAMING_SNAKE_CASE_													=  image_processor(images=_A  ,						return_tensors='pt').to(_A)

      # forward pass
      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**_A)

      # verify the logits
      SCREAMING_SNAKE_CASE_													=  (1, 1000)
      self.assertEqual(outputs.logits.shape  ,						_A)

      SCREAMING_SNAKE_CASE_													=  torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]).to(_A)
      self.assertTrue(torch.allclose(outputs.logits[0][:3]  ,						_A  ,						atol=1E-4))





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 					__snake_case     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		torch.FloatTensor






class 					__snake_case     (			lowerCAmelCase__   ,       lowerCAmelCase__				):

  @register_to_config
  def __init__(    self  ,						_A = 16  ,						_A = 88  ,						_A = None  ,						_A = None  ,						_A = 1  ,						_A = 0.0  ,						_A = 32  ,						_A = None  ,						_A = False  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = "geglu"  ,						_A = None  ,						_A = False  ,						_A = False  ,						_A = False  ,						_A = "layer_norm"  ,						_A = True  ,						):
      super().__init__()
      SCREAMING_SNAKE_CASE_													=  use_linear_projection
      SCREAMING_SNAKE_CASE_													=  num_attention_heads
      SCREAMING_SNAKE_CASE_													=  attention_head_dim
      SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  (in_channels is not None) and (patch_size is None)
      SCREAMING_SNAKE_CASE_													=  num_vector_embeds is not None
      SCREAMING_SNAKE_CASE_													=  in_channels is not None and patch_size is not None

      if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
          SCREAMING_SNAKE_CASE_													=  (
              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'  ,						_A  ,						standard_warn=_A)
          SCREAMING_SNAKE_CASE_													=  '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:
          SCREAMING_SNAKE_CASE_													=  in_channels

          SCREAMING_SNAKE_CASE_													=  torch.nn.GroupNorm(num_groups=_A  ,						num_channels=_A  ,						eps=1E-6  ,						affine=_A)
          if use_linear_projection:
              SCREAMING_SNAKE_CASE_													=  nn.Linear(_A  ,						_A)
          else:
              SCREAMING_SNAKE_CASE_													=  nn.Convad(_A  ,						_A  ,						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"

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

          SCREAMING_SNAKE_CASE_													=  ImagePositionalEmbeddings(
              num_embed=_A  ,						embed_dim=_A  ,						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"

          SCREAMING_SNAKE_CASE_													=  sample_size
          SCREAMING_SNAKE_CASE_													=  sample_size

          SCREAMING_SNAKE_CASE_													=  patch_size
          SCREAMING_SNAKE_CASE_													=  PatchEmbed(
              height=_A  ,						width=_A  ,						patch_size=_A  ,						in_channels=_A  ,						embed_dim=_A  ,						)

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

      # 4. Define output layers
      SCREAMING_SNAKE_CASE_													=  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:
              SCREAMING_SNAKE_CASE_													=  nn.Linear(_A  ,						_A)
          else:
              SCREAMING_SNAKE_CASE_													=  nn.Convad(_A  ,						_A  ,						kernel_size=1  ,						stride=1  ,						padding=0)
      elif self.is_input_vectorized:
          SCREAMING_SNAKE_CASE_													=  nn.LayerNorm(_A)
          SCREAMING_SNAKE_CASE_													=  nn.Linear(_A  ,						self.num_vector_embeds - 1)
      elif self.is_input_patches:
          SCREAMING_SNAKE_CASE_													=  nn.LayerNorm(_A  ,						elementwise_affine=_A  ,						eps=1E-6)
          SCREAMING_SNAKE_CASE_													=  nn.Linear(_A  ,						2 * inner_dim)
          SCREAMING_SNAKE_CASE_													=  nn.Linear(_A  ,						patch_size * patch_size * self.out_channels)


  def   lowerCAmelCase__   (    self  ,						_A  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = True  ,						):
      # 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)
          SCREAMING_SNAKE_CASE_													=  (1 - attention_mask.to(hidden_states.dtype)) * -1_0_0_0_0.0
          SCREAMING_SNAKE_CASE_													=  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:
          SCREAMING_SNAKE_CASE_													=  (1 - encoder_attention_mask.to(hidden_states.dtype)) * -1_0_0_0_0.0
          SCREAMING_SNAKE_CASE_													=  encoder_attention_mask.unsqueeze(1)

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

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

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

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

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

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

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


