

from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TFSharedEmbeddings,
      )

      if _tf_gpu_memory_limit is not None:
            _SCREAMING_SNAKE_CASE							 =			tf.config.list_physical_devices('''GPU''')
            for gpu in gpus:
                  # Restrict TensorFlow to only allocate x GB of memory on the GPUs
                  try:
                        tf.config.set_logical_device_configuration(
                            gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                        )
                        _SCREAMING_SNAKE_CASE							 =			tf.config.list_logical_devices('''GPU''')
                        print('''Logical GPUs''', logical_gpus)
                  except RuntimeError as e:
                        # Virtual devices must be set before GPUs have been initialized
                        print(e)


@require_tf
class      lowerCamelCase_ :
 _lowerCAmelCase									=	None
 _lowerCAmelCase									=	()
 _lowerCAmelCase									=	()
 _lowerCAmelCase									=	True
 _lowerCAmelCase									=	True
 _lowerCAmelCase									=	True
 _lowerCAmelCase									=	False




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase=False							):
   __UpperCAmelCase	:  str		=     copy.deepcopy(_UpperCAmelCase							)

   if model_class in get_values(_UpperCAmelCase							):
     __UpperCAmelCase	:  List[str]		=     {
         k: tf.tile(tf.expand_dims(_UpperCAmelCase  ,  1							)  ,  (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
         if isinstance(_UpperCAmelCase  ,  tf.Tensor							) and v.ndim > 0
         else v
         for k, v in inputs_dict.items()
     }

   if return_labels:
     if model_class in get_values(_UpperCAmelCase							):
       __UpperCAmelCase	:  List[Any]		=     tf.ones(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in get_values(_UpperCAmelCase							):
       __UpperCAmelCase	:  str		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
       __UpperCAmelCase	:  Optional[Any]		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in [
         *get_values(_UpperCAmelCase							),
         *get_values(_UpperCAmelCase							),
     ]:
       __UpperCAmelCase	:  Union[str, Any]		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in get_values(_UpperCAmelCase							):
       __UpperCAmelCase	:  int		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in [
         *get_values(_UpperCAmelCase							),
         *get_values(_UpperCAmelCase							),
         *get_values(_UpperCAmelCase							),
         *get_values(_UpperCAmelCase							),
         *get_values(_UpperCAmelCase							),
     ]:
       __UpperCAmelCase	:  List[Any]		=     tf.zeros(
           (self.model_tester.batch_size, self.model_tester.seq_length)  ,  dtype=tf.intaa							)
   return inputs_dict




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  int		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  int		=     model_class(_UpperCAmelCase							)

     @tf.function
     def run_in_graph_mode():
       return model(_UpperCAmelCase							)

     __UpperCAmelCase	:  Optional[Any]		=     run_in_graph_mode()
     self.assertIsNotNone(_UpperCAmelCase							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  List[str]		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  Optional[Any]		=     model_class(_UpperCAmelCase							)

     @tf.function(experimental_compile=_UpperCAmelCase							)
     def run_in_graph_mode():
       return model(_UpperCAmelCase							)

     __UpperCAmelCase	:  List[Any]		=     run_in_graph_mode()
     self.assertIsNotNone(_UpperCAmelCase							)




 @slow
 def     a       (			self							):
   # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
   __UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  List[Any]		=     model_class(_UpperCAmelCase							)
     if getattr(_UpperCAmelCase  ,  'hf_compute_loss'  ,  _UpperCAmelCase							):
       # Test that model correctly compute the loss with kwargs
       __UpperCAmelCase	:  int		=     self._prepare_for_class(inputs_dict.copy()  ,  _UpperCAmelCase  ,  return_labels=_UpperCAmelCase							)
       # Is there a better way to remove these decoder inputs?
       __UpperCAmelCase	:  Union[str, Any]		=     {
           key: val
           for key, val in prepared_for_class.items()
           if key not in ('head_mask', 'decoder_head_mask', 'cross_attn_head_mask', 'decoder_input_ids')
       }

       __UpperCAmelCase	:  Optional[Any]		=     {
           'labels',
           'label',
           'label_ids',
           'start_positions',
           'start_position',
           'end_positions',
           'end_position',
           'next_sentence_label',
       }
       __UpperCAmelCase	:  Dict		=     possible_label_cols.intersection(set(_UpperCAmelCase							)							)
       self.assertGreater(len(_UpperCAmelCase							)  ,  0  ,  msg='No matching label names found!'							)
       __UpperCAmelCase	:  List[Any]		=     {key: val for key, val in prepared_for_class.items() if key in label_names}
       __UpperCAmelCase	:  Dict		=     {key: val for key, val in prepared_for_class.items() if key not in label_names}
       self.assertGreater(len(_UpperCAmelCase							)  ,  0							)

       # Make sure it works with XLA!
       model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)  ,  jit_compile=_UpperCAmelCase							)
       # Make sure the model fits without crashing regardless of where we pass the labels
       __UpperCAmelCase	:  Optional[int]		=     model.fit(
           _UpperCAmelCase  ,  validation_data=_UpperCAmelCase  ,  steps_per_epoch=1  ,  validation_steps=1  ,  shuffle=_UpperCAmelCase  ,  verbose=0  ,  )
       __UpperCAmelCase	:  Dict		=     history.history['loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase							)							)
       __UpperCAmelCase	:  List[str]		=     history.history['val_loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase							)							)

       # Now test it with separate labels, to make sure that path works in XLA too.
       __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
       model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)  ,  jit_compile=_UpperCAmelCase							)
       __UpperCAmelCase	:  str		=     model.fit(
           _UpperCAmelCase  ,  _UpperCAmelCase  ,  validation_data=(inputs_minus_labels, labels)  ,  steps_per_epoch=1  ,  validation_steps=1  ,  shuffle=_UpperCAmelCase  ,  verbose=0  ,  )

       __UpperCAmelCase	:  List[str]		=     history.history['loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase							)							)
       __UpperCAmelCase	:  Union[str, Any]		=     history.history['val_loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase							)							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()
   __UpperCAmelCase	:  Dict		=     True
   __UpperCAmelCase	:  List[Any]		=     True

   if hasattr(_UpperCAmelCase  ,  'use_cache'							):
     __UpperCAmelCase	:  int		=     True

   __UpperCAmelCase	:  List[str]		=     getattr(self.model_tester  ,  'encoder_seq_length'  ,  self.model_tester.seq_length							)
   __UpperCAmelCase	:  int		=     getattr(self.model_tester  ,  'key_length'  ,  _UpperCAmelCase							)

   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  Tuple		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
     model.build()
     __UpperCAmelCase	:  Union[str, Any]		=     len(model(_UpperCAmelCase							)							)

     for key in list(class_inputs_dict.keys()							):
       # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
       if key not in model.input_signature:
         del class_inputs_dict[key]
       # Check it's a tensor, in case the inputs dict has some bools in it too
       elif isinstance(class_inputs_dict[key]  ,  tf.Tensor							) and class_inputs_dict[key].dtype.is_integer:
         __UpperCAmelCase	:  Any		=     tf.cast(class_inputs_dict[key]  ,  tf.intaa							)

     if set(class_inputs_dict.keys()							) != set(model.input_signature.keys()							):
       continue  # Some models have inputs that the preparation functions don't create, we skip those

     with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(_UpperCAmelCase  ,  saved_model=_UpperCAmelCase							)
       __UpperCAmelCase	:  List[Any]		=     os.path.join(_UpperCAmelCase  ,  'saved_model'  ,  '1'							)
       __UpperCAmelCase	:  int		=     tf.keras.models.load_model(_UpperCAmelCase							)
       __UpperCAmelCase	:  List[str]		=     model(_UpperCAmelCase							)

       if self.is_encoder_decoder:
         __UpperCAmelCase	:  List[str]		=     outputs['encoder_hidden_states']
         __UpperCAmelCase	:  List[Any]		=     outputs['encoder_attentions']
       else:
         __UpperCAmelCase	:  Dict		=     outputs['hidden_states']
         __UpperCAmelCase	:  Optional[Any]		=     outputs['attentions']

       self.assertEqual(len(_UpperCAmelCase							)  ,  _UpperCAmelCase							)

       __UpperCAmelCase	:  Any		=     getattr(
           self.model_tester  ,  'expected_num_hidden_layers'  ,  self.model_tester.num_hidden_layers + 1							)

       self.assertEqual(len(_UpperCAmelCase							)  ,  _UpperCAmelCase							)
       self.assertListEqual(
           list(output_hidden_states[0].shape[-2:]							)  ,  [self.model_tester.seq_length, self.model_tester.hidden_size]  ,  )

       self.assertEqual(len(_UpperCAmelCase							)  ,  self.model_tester.num_hidden_layers							)
       self.assertListEqual(
           list(output_attentions[0].shape[-3:]							)  ,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]  ,  )




 @slow
 def     a       (			self							):
   tf.keras.mixed_precision.set_global_policy('mixed_float16'							)

   # try/finally block to ensure subsequent tests run in float32
   try:
     __UpperCAmelCase,			__UpperCAmelCase	:  List[str]		=     self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
       __UpperCAmelCase	:  List[str]		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)
       __UpperCAmelCase	:  Optional[int]		=     model_class(_UpperCAmelCase							)
       __UpperCAmelCase	:  int		=     model(_UpperCAmelCase							)

       self.assertIsNotNone(_UpperCAmelCase							)
   finally:
     tf.keras.mixed_precision.set_global_policy('float32'							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()
   # head_mask and decoder_head_mask has different shapes than other input args
   if "head_mask" in inputs_dict:
     del inputs_dict["head_mask"]
   if "decoder_head_mask" in inputs_dict:
     del inputs_dict["decoder_head_mask"]
   if "cross_attn_head_mask" in inputs_dict:
     del inputs_dict["cross_attn_head_mask"]
   __UpperCAmelCase	:  Union[str, Any]		=     {
       module_member
       for model_class in self.all_model_classes
       for module in (import_module(model_class.__module__							),)
       for module_member_name in dir(_UpperCAmelCase							)
       if module_member_name.endswith('MainLayer'							)
       for module_member in (getattr(_UpperCAmelCase  ,  _UpperCAmelCase							),)
       if isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							)
       and tf.keras.layers.Layer in module_member.__bases__
       and getattr(_UpperCAmelCase  ,  '_keras_serializable'  ,  _UpperCAmelCase							)
   }

   for main_layer_class in tf_main_layer_classes:
     # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
     if "T5" in main_layer_class.__name__:
       # Take the same values than in TFT5ModelTester for this shared layer
       __UpperCAmelCase	:  List[str]		=     TFSharedEmbeddings(self.model_tester.vocab_size  ,  self.model_tester.hidden_size  ,  name='shared'							)
       __UpperCAmelCase	:  Optional[Any]		=     False
       __UpperCAmelCase	:  Optional[int]		=     main_layer_class(_UpperCAmelCase  ,  embed_tokens=_UpperCAmelCase							)
     else:
       __UpperCAmelCase	:  Union[str, Any]		=     main_layer_class(_UpperCAmelCase							)

     __UpperCAmelCase	:  Union[str, Any]		=     {
         name: tf.keras.Input(tensor.shape[1:]  ,  dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
     }

     if hasattr(self.model_tester  ,  'num_labels'							):
       __UpperCAmelCase	:  str		=     self.model_tester.num_labels
     else:
       __UpperCAmelCase	:  Tuple		=     2

     __UpperCAmelCase	:  Tuple		=     tf.data.Dataset.from_tensor_slices(
         (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)							))							).batch(1							)

     __UpperCAmelCase	:  Optional[Any]		=     main_layer(_UpperCAmelCase							)[0]
     __UpperCAmelCase	:  Dict		=     tf.keras.layers.Dense(_UpperCAmelCase  ,  activation='softmax'  ,  name='outputs'							)(_UpperCAmelCase							)
     __UpperCAmelCase	:  Dict		=     tf.keras.models.Model(inputs=_UpperCAmelCase  ,  outputs=[outputs]							)

     model.compile(loss='binary_crossentropy'  ,  optimizer='adam'  ,  metrics=['binary_accuracy']							)
     model.fit(_UpperCAmelCase  ,  epochs=1							)

     with tempfile.TemporaryDirectory() as tmpdirname:
       __UpperCAmelCase	:  Optional[Any]		=     os.path.join(_UpperCAmelCase  ,  'keras_model.h5'							)
       model.save(_UpperCAmelCase							)
       if "T5" in main_layer_class.__name__:
         __UpperCAmelCase	:  int		=     tf.keras.models.load_model(
             _UpperCAmelCase  ,  custom_objects={
                 main_layer_class.__name__: main_layer_class,
                 'TFSharedEmbeddings': TFSharedEmbeddings,
             }  ,  )
       else:
         __UpperCAmelCase	:  List[Any]		=     tf.keras.models.load_model(
             _UpperCAmelCase  ,  custom_objects={main_layer_class.__name__: main_layer_class}							)
       assert isinstance(_UpperCAmelCase  ,  tf.keras.Model							)
       model(_UpperCAmelCase							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  str		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  Optional[int]		=     model_class(_UpperCAmelCase							)

     __UpperCAmelCase	:  Dict		=     copy.deepcopy(_UpperCAmelCase							)

     if not self.is_encoder_decoder:
       __UpperCAmelCase	:  Union[str, Any]		=     inputs['input_ids']
       del inputs["input_ids"]
     else:
       __UpperCAmelCase	:  Optional[int]		=     inputs['input_ids']
       __UpperCAmelCase	:  str		=     inputs.get('decoder_input_ids'  ,  _UpperCAmelCase							)
       del inputs["input_ids"]
       inputs.pop('decoder_input_ids'  ,  _UpperCAmelCase							)

     if not self.is_encoder_decoder:
       __UpperCAmelCase	:  List[str]		=     model.get_input_embeddings()(_UpperCAmelCase							)
     else:
       __UpperCAmelCase	:  Union[str, Any]		=     model.get_input_embeddings()(_UpperCAmelCase							)
       __UpperCAmelCase	:  Tuple		=     model.get_input_embeddings()(_UpperCAmelCase							)

     __UpperCAmelCase	:  Any		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)

     @tf.function
     def run_in_graph_mode():
       return model(_UpperCAmelCase							)

     __UpperCAmelCase	:  int		=     run_in_graph_mode()
     self.assertIsNotNone(_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   # special tokens cannot be bad tokens
   __UpperCAmelCase	:  Any		=     []
   if model.config.bos_token_id is not None:
     special_tokens.append(model.config.bos_token_id							)
   if model.config.pad_token_id is not None:
     special_tokens.append(model.config.pad_token_id							)
   if model.config.eos_token_id is not None:
     special_tokens.append(model.config.eos_token_id							)

   # create random bad tokens that are not special tokens
   __UpperCAmelCase	:  int		=     []
   while len(_UpperCAmelCase							) < num_bad_tokens:
     __UpperCAmelCase	:  Optional[Any]		=     tf.squeeze(ids_tensor((1, 1)  ,  self.model_tester.vocab_size							)  ,  0							).numpy()[0]
     if token not in special_tokens:
       bad_tokens.append(_UpperCAmelCase							)
   return bad_tokens




 def     a       (			self  ,  _UpperCAmelCase							):
   for token_id in output_ids[0].numpy().tolist():
     self.assertGreaterEqual(_UpperCAmelCase  ,  0							)
     self.assertLess(_UpperCAmelCase  ,  self.model_tester.vocab_size							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   # for all bad word tokens
   for bad_word_ids in bad_words_ids:
     # for all slices in batch
     for generated_ids_slice in generated_ids:
       # for all word idx
       for i in range(len(_UpperCAmelCase							)  ,  len(_UpperCAmelCase							)							):
         # if tokens match
         if generated_ids_slice[i - len(_UpperCAmelCase							) : i] == bad_word_ids:
           return True
   return False








import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
      from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor


class      lowerCamelCase_ (			_lowerCAmelCase):




 def     a       (			self							):
   __UpperCAmelCase	:  Union[str, Any]		=     self.config_class(**self.inputs_dict							)
   self.parent.assertTrue(hasattr(_UpperCAmelCase  ,  'hidden_sizes'							)							)
   self.parent.assertTrue(hasattr(_UpperCAmelCase  ,  'num_encoder_blocks'							)							)



class      lowerCamelCase_ :




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase=13  ,  _UpperCAmelCase=64  ,  _UpperCAmelCase=3  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase=[2, 2, 2, 2]  ,  _UpperCAmelCase=[8, 4, 2, 1]  ,  _UpperCAmelCase=[16, 32, 64, 128]  ,  _UpperCAmelCase=[1, 4, 8, 16]  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase="gelu"  ,  _UpperCAmelCase=0.1  ,  _UpperCAmelCase=0.0_2  ,  _UpperCAmelCase=3  ,  _UpperCAmelCase=None  ,  ):
   __UpperCAmelCase	:  Optional[int]		=     parent
   __UpperCAmelCase	:  str		=     batch_size
   __UpperCAmelCase	:  Optional[int]		=     image_size
   __UpperCAmelCase	:  Dict		=     num_channels
   __UpperCAmelCase	:  int		=     num_encoder_blocks
   __UpperCAmelCase	:  str		=     sr_ratios
   __UpperCAmelCase	:  List[Any]		=     depths
   __UpperCAmelCase	:  Optional[int]		=     hidden_sizes
   __UpperCAmelCase	:  List[str]		=     downsampling_rates
   __UpperCAmelCase	:  Dict		=     is_training
   __UpperCAmelCase	:  Optional[int]		=     use_labels
   __UpperCAmelCase	:  Optional[Any]		=     hidden_act
   __UpperCAmelCase	:  int		=     hidden_dropout_prob
   __UpperCAmelCase	:  Tuple		=     initializer_range
   __UpperCAmelCase	:  str		=     num_labels
   __UpperCAmelCase	:  Union[str, Any]		=     scope




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[int]		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

   __UpperCAmelCase	:  Optional[Any]		=     None
   if self.use_labels:
     __UpperCAmelCase	:  Optional[int]		=     ids_tensor([self.batch_size, self.image_size, self.image_size]  ,  self.num_labels							)

   __UpperCAmelCase	:  Tuple		=     PoolFormerConfig(
       image_size=self.image_size  ,  num_channels=self.num_channels  ,  num_encoder_blocks=self.num_encoder_blocks  ,  depths=self.depths  ,  hidden_sizes=self.hidden_sizes  ,  hidden_act=self.hidden_act  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  initializer_range=self.initializer_range  ,  )

   return config, pixel_values, labels




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Any		=     PoolFormerModel(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  int		=     model(_UpperCAmelCase							)
   __UpperCAmelCase	:  Dict		=     self.image_size // 3_2.0
   self.parent.assertEqual(
       result.last_hidden_state.shape  ,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)




 def     a       (			self							):
   __UpperCAmelCase	:  Tuple		=     self.prepare_config_and_inputs()
   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Union[str, Any]		=     config_and_inputs
   __UpperCAmelCase	:  str		=     {'pixel_values': pixel_values}
   return config, inputs_dict



@require_torch
class      lowerCamelCase_ (			_lowerCAmelCase	,				_lowerCAmelCase	,				unittest.TestCase):
 _lowerCAmelCase									=	(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
 _lowerCAmelCase									=	(
     {'''feature-extraction''': PoolFormerModel, '''image-classification''': PoolFormerForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False




 def     a       (			self							):
   __UpperCAmelCase	:  str		=     PoolFormerModelTester(self							)
   __UpperCAmelCase	:  List[str]		=     PoolFormerConfigTester(self  ,  config_class=_UpperCAmelCase							)




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




 def     a       (			self							):
   __UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_UpperCAmelCase							)




 @unittest.skip('PoolFormer does not use inputs_embeds'							)
 def     a       (			self							):
   pass




 @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'							)
 def     a       (			self							):
   pass




 def     a       (			self							):
   def check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
     __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
     model.to(_UpperCAmelCase							)
     model.eval()

     with torch.no_grad():
       __UpperCAmelCase	:  Dict		=     model(**self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)							)

     __UpperCAmelCase	:  Tuple		=     outputs.hidden_states

     __UpperCAmelCase	:  Tuple		=     self.model_tester.num_encoder_blocks
     self.assertEqual(len(_UpperCAmelCase							)  ,  _UpperCAmelCase							)

     # verify the first hidden states (first block)
     self.assertListEqual(
         list(hidden_states[0].shape[-3:]							)  ,  [
             self.model_tester.hidden_sizes[0],
             self.model_tester.image_size // 4,
             self.model_tester.image_size // 4,
         ]  ,  )

   __UpperCAmelCase,			__UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __UpperCAmelCase	:  Optional[int]		=     True
     check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     __UpperCAmelCase	:  int		=     True

     check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)




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

   __UpperCAmelCase,			__UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs_for_common()
   __UpperCAmelCase	:  Dict		=     True

   for model_class in self.all_model_classes:
     if model_class in get_values(_UpperCAmelCase							):
       continue
     __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
     model.to(_UpperCAmelCase							)
     model.train()
     __UpperCAmelCase	:  Any		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase  ,  return_labels=_UpperCAmelCase							)
     __UpperCAmelCase	:  Optional[Any]		=     model(**_UpperCAmelCase							).loss
     loss.backward()




 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     self.model_tester.prepare_config_and_inputs_for_common()

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

     __UpperCAmelCase	:  Any		=     ['pixel_values']
     self.assertListEqual(arg_names[:1]  ,  _UpperCAmelCase							)




 @slow
 def     a       (			self							):
   for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCAmelCase	:  Dict		=     PoolFormerModel.from_pretrained(_UpperCAmelCase							)
     self.assertIsNotNone(_UpperCAmelCase							)





def  _snake_case		()							->		str:
  __UpperCAmelCase	:  Union[str, Any]		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image







@require_torch
class      lowerCamelCase_ (			unittest.TestCase):




 @slow
 def     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     PoolFormerImageProcessor()
   __UpperCAmelCase	:  Tuple		=     PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'							).to(_UpperCAmelCase							)

   __UpperCAmelCase	:  Optional[Any]		=     image_processor(images=prepare_img()  ,  return_tensors='pt'							).to(_UpperCAmelCase							)

   # forward pass
   with torch.no_grad():
     __UpperCAmelCase	:  Tuple		=     model(**_UpperCAmelCase							)

   # verify the logits
   __UpperCAmelCase	:  Optional[Any]		=     torch.Size((1, 1_000)							)
   self.assertEqual(outputs.logits.shape  ,  _UpperCAmelCase							)

   __UpperCAmelCase	:  Dict		=     torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]							).to(_UpperCAmelCase							)
   self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,  _UpperCAmelCase  ,  atol=1E-4							)							)





