import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 a  :      Tuple            =    """platform"""

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				):
   if attention_mask is None:
      lowerCAmelCase_:							Optional[int]       =	np.where(input_ids != config.pad_token_id							,				1							,				0					)
   if decoder_attention_mask is None:
      lowerCAmelCase_:							Union[str, Any]       =	np.where(decoder_input_ids != config.pad_token_id							,				1							,				0					)
   if head_mask is None:
      lowerCAmelCase_:							Optional[int]       =	np.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      lowerCAmelCase_:							str       =	np.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      lowerCAmelCase_:							Any       =	np.ones((config.decoder_layers, config.decoder_attention_heads)					)
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }

class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=13       ,						lowerCamelCase__=7       ,						lowerCamelCase__=True       ,						lowerCamelCase__=False       ,						lowerCamelCase__=99       ,						lowerCamelCase__=16       ,						lowerCamelCase__=2       ,						lowerCamelCase__=4       ,						lowerCamelCase__=4       ,						lowerCamelCase__="gelu"       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=32       ,						lowerCamelCase__=2       ,						lowerCamelCase__=1       ,						lowerCamelCase__=0       ,						lowerCamelCase__=0.0_2       ,						):
       lowerCAmelCase_:							Union[str, Any]       =	parent
       lowerCAmelCase_:							Tuple       =	batch_size
       lowerCAmelCase_:							Any       =	seq_length
       lowerCAmelCase_:							Tuple       =	is_training
       lowerCAmelCase_:							Optional[int]       =	use_labels
       lowerCAmelCase_:							List[Any]       =	vocab_size
       lowerCAmelCase_:							str       =	hidden_size
       lowerCAmelCase_:							Union[str, Any]       =	num_hidden_layers
       lowerCAmelCase_:							List[str]       =	num_attention_heads
       lowerCAmelCase_:							Dict       =	intermediate_size
       lowerCAmelCase_:							int       =	hidden_act
       lowerCAmelCase_:							Any       =	hidden_dropout_prob
       lowerCAmelCase_:							str       =	attention_probs_dropout_prob
       lowerCAmelCase_:							Union[str, Any]       =	max_position_embeddings
       lowerCAmelCase_:							Any       =	eos_token_id
       lowerCAmelCase_:							Union[str, Any]       =	pad_token_id
       lowerCAmelCase_:							Union[str, Any]       =	bos_token_id
       lowerCAmelCase_:							Optional[int]       =	initializer_range




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,						self.vocab_size )       ,						3       ,						self.vocab_size )
       lowerCAmelCase_:							int       =	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,						dtype=np.intaa ))       ,						-1 )

       lowerCAmelCase_:							Optional[Any]       =	shift_tokens_right(lowerCamelCase__       ,						1       ,						2 )

       lowerCAmelCase_:							Optional[int]       =	BlenderbotSmallConfig(
           vocab_size=self.vocab_size       ,						d_model=self.hidden_size       ,						encoder_layers=self.num_hidden_layers       ,						decoder_layers=self.num_hidden_layers       ,						encoder_attention_heads=self.num_attention_heads       ,						decoder_attention_heads=self.num_attention_heads       ,						encoder_ffn_dim=self.intermediate_size       ,						decoder_ffn_dim=self.intermediate_size       ,						dropout=self.hidden_dropout_prob       ,						attention_dropout=self.attention_probs_dropout_prob       ,						max_position_embeddings=self.max_position_embeddings       ,						eos_token_id=self.eos_token_id       ,						bos_token_id=self.bos_token_id       ,						pad_token_id=self.pad_token_id       ,						initializer_range=self.initializer_range       ,						use_cache=lowerCamelCase__       ,						)
       lowerCAmelCase_:							Optional[Any]       =	prepare_blenderbot_inputs_dict(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       return config, inputs_dict




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Dict       =	self.prepare_config_and_inputs()
       return config, inputs_dict




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	20
       lowerCAmelCase_:							int       =	model_class_name(lowerCamelCase__ )

       lowerCAmelCase_:							Any       =	model.encode(inputs_dict["input_ids"] )

       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	(
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       lowerCAmelCase_:							List[str]       =	model.init_cache(decoder_input_ids.shape[0]       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,						dtype="i4" )

       lowerCAmelCase_:							Optional[Any]       =	jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]       ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,						)
       lowerCAmelCase_:							Optional[Any]       =	model.decode(
           decoder_input_ids[:, :-1]       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						past_key_values=lowerCamelCase__       ,						decoder_position_ids=lowerCamelCase__       ,						)

       lowerCAmelCase_:							Optional[Any]       =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,						dtype="i4" )
       lowerCAmelCase_:							Optional[int]       =	model.decode(
           decoder_input_ids[:, -1:]       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						past_key_values=outputs_cache.past_key_values       ,						decoder_position_ids=lowerCamelCase__       ,						)

       lowerCAmelCase_:							Any       =	model.decode(lowerCamelCase__       ,						lowerCamelCase__ )

       lowerCAmelCase_:							str       =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
       self.parent.assertTrue(diff < 1E-3       ,						msg=F'''Max diff is {diff}''' )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							str       =	20
       lowerCAmelCase_:							Union[str, Any]       =	model_class_name(lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	model.encode(inputs_dict["input_ids"] )

       lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	(
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       lowerCAmelCase_:							int       =	jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
           ]       ,						axis=-1       ,						)

       lowerCAmelCase_:							Any       =	model.init_cache(decoder_input_ids.shape[0]       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							List[Any]       =	jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]       ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,						)

       lowerCAmelCase_:							Tuple       =	model.decode(
           decoder_input_ids[:, :-1]       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						past_key_values=lowerCamelCase__       ,						decoder_position_ids=lowerCamelCase__       ,						)
       lowerCAmelCase_:							str       =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,						dtype="i4" )
       lowerCAmelCase_:							Tuple       =	model.decode(
           decoder_input_ids[:, -1:]       ,						lowerCamelCase__       ,						past_key_values=outputs_cache.past_key_values       ,						decoder_attention_mask=lowerCamelCase__       ,						decoder_position_ids=lowerCamelCase__       ,						)

       lowerCAmelCase_:							Union[str, Any]       =	model.decode(lowerCamelCase__       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__ )

       lowerCAmelCase_:							Optional[int]       =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
       self.parent.assertTrue(diff < 1E-3       ,						msg=F'''Max diff is {diff}''' )





@require_flax
class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    99




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	np.array(
           [
               [71, 82, 18, 33, 46, 91, 2],
               [68, 34, 26, 58, 30, 82, 2],
               [5, 97, 17, 39, 94, 40, 2],
               [76, 83, 94, 25, 70, 78, 2],
               [87, 59, 41, 35, 48, 66, 2],
               [55, 13, 16, 58, 5, 2, 1],  # note padding
               [64, 27, 31, 51, 12, 75, 2],
               [52, 64, 86, 17, 83, 39, 2],
               [48, 61, 9, 24, 71, 82, 2],
               [26, 1, 60, 48, 22, 13, 2],
               [21, 5, 62, 28, 14, 76, 2],
               [45, 98, 37, 86, 59, 48, 2],
               [70, 70, 50, 9, 28, 0, 2],
           ]       ,						dtype=np.intaa       ,						)

       lowerCAmelCase_:							Optional[int]       =	input_ids.shape[0]
       lowerCAmelCase_:							str       =	BlenderbotSmallConfig(
           vocab_size=self.vocab_size       ,						d_model=24       ,						encoder_layers=2       ,						decoder_layers=2       ,						encoder_attention_heads=2       ,						decoder_attention_heads=2       ,						encoder_ffn_dim=32       ,						decoder_ffn_dim=32       ,						max_position_embeddings=48       ,						eos_token_id=2       ,						pad_token_id=1       ,						bos_token_id=0       ,						)
       return config, input_ids, batch_size




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[Any]       =	self._get_config_and_data()
       lowerCAmelCase_:							Union[str, Any]       =	FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	lm_model(input_ids=lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["logits"].shape       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	BlenderbotSmallConfig(
           vocab_size=self.vocab_size       ,						d_model=14       ,						encoder_layers=2       ,						decoder_layers=2       ,						encoder_attention_heads=2       ,						decoder_attention_heads=2       ,						encoder_ffn_dim=8       ,						decoder_ffn_dim=8       ,						max_position_embeddings=48       ,						)
       lowerCAmelCase_:							Union[str, Any]       =	FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__ )
       lowerCAmelCase_:							str       =	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,						dtype=np.intaa )
       lowerCAmelCase_:							str       =	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,						dtype=np.intaa )
       lowerCAmelCase_:							Any       =	lm_model(input_ids=lowerCamelCase__       ,						decoder_input_ids=lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	(*summary.shape, config.vocab_size)
       self.assertEqual(outputs["logits"].shape       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							Tuple       =	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,						dtype=np.intaa )
       lowerCAmelCase_:							Optional[Any]       =	shift_tokens_right(lowerCamelCase__       ,						1       ,						2 )
       lowerCAmelCase_:							List[Any]       =	np.equal(lowerCamelCase__       ,						1 ).astype(np.floataa ).sum()
       lowerCAmelCase_:							Any       =	np.equal(lowerCamelCase__       ,						1 ).astype(np.floataa ).sum()
       self.assertEqual(shifted.shape       ,						input_ids.shape )
       self.assertEqual(lowerCamelCase__       ,						n_pad_before - 1 )
       self.assertTrue(np.equal(shifted[:, 0]       ,						2 ).all() )





@require_flax
class 					_lowercase    (     UpperCAmelCase__ ,  unittest.TestCase ,  UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[Any]          =    True
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    (
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	FlaxBlenderbotSmallModelTester(self )




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
             lowerCAmelCase_:							Dict       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__ )
             lowerCAmelCase_:							Union[str, Any]       =	model_class(lowerCamelCase__ )

             @jax.jit
             def encode_jitted(lowerCamelCase__       ,						lowerCamelCase__=None       ,						**lowerCamelCase__ ):
                return model.encode(input_ids=lowerCamelCase__       ,						attention_mask=lowerCamelCase__ )

             with self.subTest("JIT Enabled" ):
                lowerCAmelCase_:							Optional[int]       =	encode_jitted(**lowerCamelCase__ ).to_tuple()

             with self.subTest("JIT Disabled" ):
                with jax.disable_jit():
                   lowerCAmelCase_:							int       =	encode_jitted(**lowerCamelCase__ ).to_tuple()

             self.assertEqual(len(lowerCamelCase__ )       ,						len(lowerCamelCase__ ) )
             for jitted_output, output in zip(lowerCamelCase__       ,						lowerCamelCase__ ):
                self.assertEqual(jitted_output.shape       ,						output.shape )




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
             lowerCAmelCase_:							Dict       =	model_class(lowerCamelCase__ )
             lowerCAmelCase_:							Optional[int]       =	model.encode(inputs_dict["input_ids"]       ,						inputs_dict["attention_mask"] )

             lowerCAmelCase_:							Any       =	{
                 "decoder_input_ids": inputs_dict["decoder_input_ids"],
                 "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                 "encoder_outputs": encoder_outputs,
             }

             @jax.jit
             def decode_jitted(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
                return model.decode(
                    decoder_input_ids=lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						encoder_outputs=lowerCamelCase__       ,						)

             with self.subTest("JIT Enabled" ):
                lowerCAmelCase_:							Dict       =	decode_jitted(**lowerCamelCase__ ).to_tuple()

             with self.subTest("JIT Disabled" ):
                with jax.disable_jit():
                   lowerCAmelCase_:							Union[str, Any]       =	decode_jitted(**lowerCamelCase__ ).to_tuple()

             self.assertEqual(len(lowerCamelCase__ )       ,						len(lowerCamelCase__ ) )
             for jitted_output, output in zip(lowerCamelCase__       ,						lowerCamelCase__ ):
                self.assertEqual(jitted_output.shape       ,						output.shape )




    @slow
    def      _a    (    self ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_:							Any       =	model_class_name.from_pretrained("facebook/blenderbot_small-90M" )
          # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
          lowerCAmelCase_:							str       =	np.ones((1, 1) ) * model.config.eos_token_id
          lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__ )
          self.assertIsNotNone(lowerCamelCase__ )
import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, 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_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     LevitForImageClassification,
     LevitForImageClassificationWithTeacher,
     LevitModel,
 )
 from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	self.config_class(**self.inputs_dict )
       self.parent.assertTrue(hasattr(lowerCamelCase__       ,						"hidden_sizes" ) )
       self.parent.assertTrue(hasattr(lowerCamelCase__       ,						"num_attention_heads" ) )





class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=13       ,						lowerCamelCase__=64       ,						lowerCamelCase__=3       ,						lowerCamelCase__=3       ,						lowerCamelCase__=2       ,						lowerCamelCase__=1       ,						lowerCamelCase__=16       ,						lowerCamelCase__=[128, 256, 384]       ,						lowerCamelCase__=[4, 6, 8]       ,						lowerCamelCase__=[2, 3, 4]       ,						lowerCamelCase__=[16, 16, 16]       ,						lowerCamelCase__=0       ,						lowerCamelCase__=[2, 2, 2]       ,						lowerCamelCase__=[2, 2, 2]       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=2       ,						):
       lowerCAmelCase_:							int       =	parent
       lowerCAmelCase_:							Tuple       =	batch_size
       lowerCAmelCase_:							List[str]       =	image_size
       lowerCAmelCase_:							Tuple       =	num_channels
       lowerCAmelCase_:							Optional[int]       =	kernel_size
       lowerCAmelCase_:							int       =	stride
       lowerCAmelCase_:							Optional[int]       =	padding
       lowerCAmelCase_:							Tuple       =	hidden_sizes
       lowerCAmelCase_:							Union[str, Any]       =	num_attention_heads
       lowerCAmelCase_:							Tuple       =	depths
       lowerCAmelCase_:							Optional[int]       =	key_dim
       lowerCAmelCase_:							Optional[Any]       =	drop_path_rate
       lowerCAmelCase_:							List[str]       =	patch_size
       lowerCAmelCase_:							Any       =	attention_ratio
       lowerCAmelCase_:							Tuple       =	mlp_ratio
       lowerCAmelCase_:							Any       =	initializer_range
       lowerCAmelCase_:							List[str]       =	[
           ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
           ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
       ]
       lowerCAmelCase_:							Any       =	is_training
       lowerCAmelCase_:							Optional[int]       =	use_labels
       lowerCAmelCase_:							Dict       =	num_labels
       lowerCAmelCase_:							Any       =	initializer_range




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       lowerCAmelCase_:							Tuple       =	None
       if self.use_labels:
          lowerCAmelCase_:							int       =	ids_tensor([self.batch_size]       ,						self.num_labels )

       lowerCAmelCase_:							Tuple       =	self.get_config()
       return config, pixel_values, labels




    def      _a    (    self ):
       return LevitConfig(
           image_size=self.image_size       ,						num_channels=self.num_channels       ,						kernel_size=self.kernel_size       ,						stride=self.stride       ,						padding=self.padding       ,						patch_size=self.patch_size       ,						hidden_sizes=self.hidden_sizes       ,						num_attention_heads=self.num_attention_heads       ,						depths=self.depths       ,						key_dim=self.key_dim       ,						drop_path_rate=self.drop_path_rate       ,						mlp_ratio=self.mlp_ratio       ,						attention_ratio=self.attention_ratio       ,						initializer_range=self.initializer_range       ,						down_ops=self.down_ops       ,						)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	LevitModel(config=lowerCamelCase__ )
       model.to(lowerCamelCase__ )
       model.eval()
       lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__ )
       lowerCAmelCase_:							Tuple       =	(self.image_size, self.image_size)
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	image_size[0], image_size[1]
       for _ in range(4 ):
          lowerCAmelCase_:							Union[str, Any]       =	floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
          lowerCAmelCase_:							str       =	floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
       self.parent.assertEqual(
           result.last_hidden_state.shape       ,						(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1])       ,						)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	self.num_labels
       lowerCAmelCase_:							str       =	LevitForImageClassification(lowerCamelCase__ )
       model.to(lowerCamelCase__ )
       model.eval()
       lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__       ,						labels=lowerCamelCase__ )
       self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_labels) )




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.prepare_config_and_inputs()
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Dict       =	config_and_inputs
       lowerCAmelCase_:							Tuple       =	{"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class 					_lowercase    (     UpperCAmelCase__ ,  UpperCAmelCase__ ,  unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Any          =    (
        (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE:				int          =    (
        {
            'feature-extraction': LevitModel,
            'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

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




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	LevitModelTester(self )
       lowerCAmelCase_:							Union[str, Any]       =	ConfigTester(self       ,						config_class=lowerCamelCase__       ,						has_text_modality=lowerCamelCase__       ,						hidden_size=37 )




    def      _a    (    self ):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()




    def      _a    (    self ):
       return




    @unittest.skip(reason="Levit does not use inputs_embeds" )
    def      _a    (    self ):
       pass




    @unittest.skip(reason="Levit does not support input and output embeddings" )
    def      _a    (    self ):
       pass




    @unittest.skip(reason="Levit does not output attentions" )
    def      _a    (    self ):
       pass




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase_:							str       =	["pixel_values"]
          self.assertListEqual(arg_names[:1]       ,						lowerCamelCase__ )




    def      _a    (    self ):
       def check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							int       =	model_class(lowerCamelCase__ )
          model.to(lowerCamelCase__ )
          model.eval()

          with torch.no_grad():
             lowerCAmelCase_:							Dict       =	model(**self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__ ) )

          lowerCAmelCase_:							str       =	outputs.hidden_states

          lowerCAmelCase_:							List[Any]       =	len(self.model_tester.depths ) + 1
          self.assertEqual(len(lowerCamelCase__ )       ,						lowerCamelCase__ )

          lowerCAmelCase_:							List[Any]       =	(self.model_tester.image_size, self.model_tester.image_size)
          lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	image_size[0], image_size[1]
          for _ in range(4 ):
             lowerCAmelCase_:							Tuple       =	floor(
                 (
                     (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                     / self.model_tester.stride
                 )
                 + 1 )
             lowerCAmelCase_:							Optional[int]       =	floor(
                 (
                     (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                     / self.model_tester.stride
                 )
                 + 1 )
          # verify the first hidden states (first block)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:] )       ,						[
                  height * width,
                  self.model_tester.hidden_sizes[0],
              ]       ,						)

       lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowerCAmelCase_:							int       =	True
          check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowerCAmelCase_:							Union[str, Any]       =	True

          check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )




    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
    def      _a    (    self ):
       pass




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=False ):
       lowerCAmelCase_:							List[str]       =	super()._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )

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

       return inputs_dict




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )




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

       lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()
       lowerCAmelCase_:							int       =	True

       for model_class in self.all_model_classes:
          # LevitForImageClassificationWithTeacher supports inference-only
          if (
              model_class in get_values(lowerCamelCase__ )
              or model_class.__name__ == "LevitForImageClassificationWithTeacher"
          ):
             continue
          lowerCAmelCase_:							Tuple       =	model_class(lowerCamelCase__ )
          model.to(lowerCamelCase__ )
          model.train()
          lowerCAmelCase_:							Optional[int]       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )
          lowerCAmelCase_:							Tuple       =	model(**lowerCamelCase__ ).loss
          loss.backward()




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
          return

       lowerCAmelCase_:							Any       =	False
       lowerCAmelCase_:							Tuple       =	True

       for model_class in self.all_model_classes:
          if model_class in get_values(lowerCamelCase__ ) or not model_class.supports_gradient_checkpointing:
             continue
          # LevitForImageClassificationWithTeacher supports inference-only
          if model_class.__name__ == "LevitForImageClassificationWithTeacher":
             continue
          lowerCAmelCase_:							Any       =	model_class(lowerCamelCase__ )
          model.gradient_checkpointing_enable()
          model.to(lowerCamelCase__ )
          model.train()
          lowerCAmelCase_:							str       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )
          lowerCAmelCase_:							List[str]       =	model(**lowerCamelCase__ ).loss
          loss.backward()




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	self.model_tester.prepare_config_and_inputs_for_common()

       lowerCAmelCase_:							Optional[int]       =	[
           {"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__ == "LevitForImageClassificationWithTeacher"
          ):
             continue

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

                lowerCAmelCase_:							List[str]       =	model_class(lowerCamelCase__ )
                model.to(lowerCamelCase__ )
                model.train()

                lowerCAmelCase_:							List[Any]       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )

                if problem_type["num_labels"] > 1:
                   lowerCAmelCase_:							List[str]       =	inputs["labels"].unsqueeze(1 ).repeat(1       ,						problem_type["num_labels"] )

                lowerCAmelCase_:							List[Any]       =	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:
                   lowerCAmelCase_:							Dict       =	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 ):
       for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_:							Optional[int]       =	LevitModel.from_pretrained(lowerCamelCase__ )
          self.assertIsNotNone(lowerCamelCase__ )





def 		snake_case__	(		):
   lowerCAmelCase_:							Optional[int]       =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
   return image







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






    '''simple docstring'''




    @cached_property
    def      _a    (    self ):
       return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )




    @slow
    def      _a    (    self ):
       lowerCAmelCase_:							Any       =	LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
           lowerCamelCase__ )

       lowerCAmelCase_:							str       =	self.default_image_processor
       lowerCAmelCase_:							int       =	prepare_img()
       lowerCAmelCase_:							Optional[int]       =	image_processor(images=lowerCamelCase__       ,						return_tensors="pt" ).to(lowerCamelCase__ )

       # forward pass
       with torch.no_grad():
          lowerCAmelCase_:							Tuple       =	model(**lowerCamelCase__ )

       # verify the logits
       lowerCAmelCase_:							str       =	torch.Size((1, 1_000) )
       self.assertEqual(outputs.logits.shape       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Union[str, Any]       =	torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(lowerCamelCase__ )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,						lowerCamelCase__       ,						atol=1E-4 ) )