
"""simple docstring"""

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        _UpperCAmelCase							:




      def __init__(							self				,    lowercase_				,    lowercase_ = 1_3				,    lowercase_ = 6_4				,    lowercase_ = 2				,    lowercase_ = 3				,    lowercase_ = 3				,    lowercase_ = True				,    lowercase_ = True				,    lowercase_ = 1_2_8				,    lowercase_=[1_6, 3_2, 6_4, 1_2_8]				,    lowercase_ = 7				,    lowercase_ = 4				,    lowercase_ = 3_7				,    lowercase_ = "gelu"				,    lowercase_ = 0.1				,    lowercase_ = 0.1				,    lowercase_ = 1_0				,    lowercase_ = 0.0_2				,    lowercase_ = 2				,    lowercase_ = 1				,    lowercase_ = 1_2_8				,    lowercase_ = [2, 2, 2, 2]				,    lowercase_ = 2				,    lowercase_ = 2				,    )  ->   Union[str, Any]:
          UpperCAmelCase       =   parent
          UpperCAmelCase       =   batch_size
          UpperCAmelCase       =   image_size
          UpperCAmelCase       =   patch_size
          UpperCAmelCase       =   num_channels
          UpperCAmelCase       =   is_training
          UpperCAmelCase       =   use_labels
          UpperCAmelCase       =   hidden_size
          UpperCAmelCase       =   num_hidden_layers
          UpperCAmelCase       =   num_attention_heads
          UpperCAmelCase       =   intermediate_size
          UpperCAmelCase       =   hidden_act
          UpperCAmelCase       =   hidden_dropout_prob
          UpperCAmelCase       =   attention_probs_dropout_prob
          UpperCAmelCase       =   type_sequence_label_size
          UpperCAmelCase       =   initializer_range
          UpperCAmelCase       =   encoder_stride
          UpperCAmelCase       =   num_attention_outputs
          UpperCAmelCase       =   embed_dim
          UpperCAmelCase       =   embed_dim + 1
          UpperCAmelCase       =   resolution
          UpperCAmelCase       =   depths
          UpperCAmelCase       =   hidden_sizes
          UpperCAmelCase       =   dim
          UpperCAmelCase       =   mlp_expansion_ratio




      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

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




      def       a_ (							self							)  ->   Union[str, Any]:
          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=__lowerCAmelCase				,    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       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   EfficientFormerModel(config=__lowerCAmelCase							)
          model.to(__lowerCAmelCase							)
          model.eval()
          UpperCAmelCase       =   model(__lowerCAmelCase							)
          self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Any:
          UpperCAmelCase       =   self.type_sequence_label_size
          UpperCAmelCase       =   EfficientFormerForImageClassification(__lowerCAmelCase							)
          model.to(__lowerCAmelCase							)
          model.eval()
          UpperCAmelCase       =   model(__lowerCAmelCase				,    labels=__lowerCAmelCase							)
          self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.type_sequence_label_size)							)

          # test greyscale images
          UpperCAmelCase       =   1
          UpperCAmelCase       =   EfficientFormerForImageClassification(__lowerCAmelCase							)
          model.to(__lowerCAmelCase							)
          model.eval()

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




      def       a_ (							self							)  ->   List[str]:
          UpperCAmelCase       =   self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase					
          )     ,							(
              UpperCAmelCase					
          )     ,							(
              UpperCAmelCase					
          )     ,												
          )       =   config_and_inputs
          UpperCAmelCase       =   {'pixel_values': pixel_values}
          return config, inputs_dict

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

      __SCREAMING_SNAKE_CASE				:							List[str]							 =		False
      __SCREAMING_SNAKE_CASE				:							Optional[Any]							 =		False
      __SCREAMING_SNAKE_CASE				:							int							 =		False




      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase       =   EfficientFormerModelTester(self							)
          UpperCAmelCase       =   ConfigTester(
              self				,    config_class=__lowerCAmelCase				,    has_text_modality=__lowerCAmelCase				,    hidden_size=3_7							)




      def       a_ (							self							)  ->   Tuple:
          self.config_tester.run_common_tests()




      @unittest.skip(reason='EfficientFormer does not use inputs_embeds'							)
      def       a_ (							self							)  ->   Dict:
          pass




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




      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase       =   ['pixel_values']
              self.assertListEqual(arg_names[:1]				,    __lowerCAmelCase							)




      def       a_ (							self							)  ->   str:


          def check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_							):
              UpperCAmelCase       =   model_class(__lowerCAmelCase							)
              model.to(__lowerCAmelCase							)
              model.eval()

              with torch.no_grad():
                  UpperCAmelCase       =   model(**self._prepare_for_class(__lowerCAmelCase				,    __lowerCAmelCase							)							)

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

              UpperCAmelCase       =   getattr(
                  self.model_tester				,    'expected_num_hidden_layers'				,    self.model_tester.num_hidden_layers + 1							)
              self.assertEqual(len(__lowerCAmelCase							)				,    __lowerCAmelCase							)

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

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

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

          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase       =   True
              check_hidden_states_output(__lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase							)

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

              check_hidden_states_output(__lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_=False							)  ->   int:
          UpperCAmelCase       =   super()._prepare_for_class(__lowerCAmelCase				,    __lowerCAmelCase				,    return_labels=__lowerCAmelCase							)

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

          return inputs_dict




      def       a_ (							self							)  ->   Optional[Any]:
          UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__lowerCAmelCase							)




      @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'							)
      def       a_ (							self							)  ->   List[str]:
          UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCAmelCase							)




      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase							)




      def       a_ (							self							)  ->   List[Any]:


          if not self.model_tester.is_training:
              return

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

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




      def       a_ (							self							)  ->   Optional[int]:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs_for_common()

          UpperCAmelCase       =   [
              {'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(__lowerCAmelCase							),
                  ]
                  or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
              ):
                  continue

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

                      UpperCAmelCase       =   model_class(__lowerCAmelCase							)
                      model.to(__lowerCAmelCase							)
                      model.train()

                      UpperCAmelCase       =   self._prepare_for_class(__lowerCAmelCase				,    __lowerCAmelCase				,    return_labels=__lowerCAmelCase							)

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

                      UpperCAmelCase       =   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=__lowerCAmelCase							) as warning_list:
                          UpperCAmelCase       =   model(**__lowerCAmelCase							).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       a_ (							self							)  ->   List[str]:


          for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase       =   EfficientFormerModel.from_pretrained(__lowerCAmelCase							)
              self.assertIsNotNone(__lowerCAmelCase							)




      def       a_ (							self							)  ->   Optional[int]:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase       =   True

          UpperCAmelCase       =   getattr(self.model_tester				,    'seq_length'				,    __lowerCAmelCase							)
          UpperCAmelCase       =   getattr(self.model_tester				,    'encoder_seq_length'				,    __lowerCAmelCase							)
          UpperCAmelCase       =   getattr(self.model_tester				,    'key_length'				,    __lowerCAmelCase							)
          UpperCAmelCase       =   getattr(self.model_tester				,    'chunk_length'				,    __lowerCAmelCase							)

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

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

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              UpperCAmelCase       =   True
              UpperCAmelCase       =   model_class(__lowerCAmelCase							)
              model.to(__lowerCAmelCase							)
              model.eval()
              with torch.no_grad():
                  UpperCAmelCase       =   model(**self._prepare_for_class(__lowerCAmelCase				,    __lowerCAmelCase							)							)
              UpperCAmelCase       =   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
              self.assertEqual(len(__lowerCAmelCase							)				,    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       lowercase__  (   )	-> Union[str, Any]:






    """simple docstring"""




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







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




      @cached_property
      def       a_ (							self							)  ->   Union[str, Any]:
          return (
              EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'							)
              if is_vision_available()
              else None
          )




      @slow
      def       a_ (							self							)  ->   Optional[Any]:
          UpperCAmelCase       =   EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'							).to(
              __lowerCAmelCase							)

          UpperCAmelCase       =   self.default_image_processor
          UpperCAmelCase       =   prepare_img()
          UpperCAmelCase       =   image_processor(images=__lowerCAmelCase				,    return_tensors='pt'							).to(__lowerCAmelCase							)

          # forward pass
          with torch.no_grad():
              UpperCAmelCase       =   model(**__lowerCAmelCase							)

          # verify the logits
          UpperCAmelCase       =   (1, 1_0_0_0)
          self.assertEqual(outputs.logits.shape				,    __lowerCAmelCase							)

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




      @slow
      def       a_ (							self							)  ->   Any:
          UpperCAmelCase       =   EfficientFormerForImageClassificationWithTeacher.from_pretrained(
              'snap-research/efficientformer-l1-300'							).to(__lowerCAmelCase							)

          UpperCAmelCase       =   self.default_image_processor
          UpperCAmelCase       =   prepare_img()
          UpperCAmelCase       =   image_processor(images=__lowerCAmelCase				,    return_tensors='pt'							).to(__lowerCAmelCase							)

          # forward pass
          with torch.no_grad():
              UpperCAmelCase       =   model(**__lowerCAmelCase							)

          # verify the logits
          UpperCAmelCase       =   (1, 1_0_0_0)
          self.assertEqual(outputs.logits.shape				,    __lowerCAmelCase							)

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








"""simple docstring"""

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_							):




      @register_to_config
      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = False				,    )  ->   List[Any]:
          super().__init__()

          UpperCAmelCase       =   nn.Linear(lowercase_				,    lowercase_				,    bias=lowercase_							)

          UpperCAmelCase       =   nn.Embedding(lowercase_				,    lowercase_							)
          UpperCAmelCase       =   False

          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)

          UpperCAmelCase       =   TaConfig(
              d_model=lowercase_				,    num_heads=lowercase_				,    d_kv=lowercase_				,    d_ff=lowercase_				,    feed_forward_proj=lowercase_				,    dropout_rate=lowercase_				,    is_decoder=lowercase_				,    is_encoder_decoder=lowercase_				,    )
          UpperCAmelCase       =   nn.ModuleList()
          for lyr_num in range(lowercase_							):
              UpperCAmelCase       =   TaBlock(lowercase_							)
              self.encoders.append(lowercase_							)

          UpperCAmelCase       =   TaLayerNorm(lowercase_							)
          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   self.input_proj(lowercase_							)

          # terminal relative positional encodings
          UpperCAmelCase       =   encoder_inputs.shape[1]
          UpperCAmelCase       =   torch.arange(lowercase_				,    device=encoder_inputs.device							)

          UpperCAmelCase       =   encoder_inputs_mask.sum(-1							)
          UpperCAmelCase       =   torch.roll(input_positions.unsqueeze(0							)				,    tuple(seq_lens.tolist()							)				,    dims=0							)
          x += self.position_encoding(lowercase_							)

          UpperCAmelCase       =   self.dropout_pre(lowercase_							)

          # inverted the attention mask
          UpperCAmelCase       =   encoder_inputs.size()
          UpperCAmelCase       =   self.get_extended_attention_mask(lowercase_				,    lowercase_							)

          for lyr in self.encoders:
              UpperCAmelCase       =   lyr(lowercase_				,    lowercase_							)[0]
          UpperCAmelCase       =   self.layer_norm(lowercase_							)

          return self.dropout_post(lowercase_							), encoder_inputs_mask



