






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase       :       Dict        =				logging.get_logger(__name__)

class 						lowerCAmelCase   (       UpperCamelCase_					,				UpperCamelCase_	):

					'''simple docstring'''

					_A  :	Optional[int]     =					'''maskformer-swin'''

					_A  :	List[str]     =					{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}



					def __init__(     self :		Any						,   __a :		Optional[Any]=224						,   __a :		List[Any]=4						,   __a :		Union[str, Any]=3						,   __a :		str=96						,   __a :		List[Any]=[2, 2, 6, 2]						,   __a :		Tuple=[3, 6, 12, 24]						,   __a :		Dict=7						,   __a :		Optional[int]=4.0						,   __a :		Optional[Any]=True						,   __a :		List[Any]=0.0						,   __a :		str=0.0						,   __a :		int=0.1						,   __a :		Union[str, Any]="gelu"						,   __a :		Optional[Any]=False						,   __a :		int=0.02						,   __a :		List[Any]=1E-5						,   __a :		int=None						,   __a :		List[Any]=None						,   **__a :		Optional[Any]						,   )	->     int:


												"""simple docstring"""

												super().__init__(**UpperCamelCase__							)

												__lowercase					:							str			=   image_size
												__lowercase					:							int			=   patch_size
												__lowercase					:							Dict			=   num_channels
												__lowercase					:							Dict			=   embed_dim
												__lowercase					:							Optional[Any]			=   depths
												__lowercase					:							Optional[int]			=   len(UpperCamelCase__							)
												__lowercase					:							int			=   num_heads
												__lowercase					:							List[Any]			=   window_size
												__lowercase					:							Union[str, Any]			=   mlp_ratio
												__lowercase					:							Optional[int]			=   qkv_bias
												__lowercase					:							List[Any]			=   hidden_dropout_prob
												__lowercase					:							Optional[Any]			=   attention_probs_dropout_prob
												__lowercase					:							int			=   drop_path_rate
												__lowercase					:							Tuple			=   hidden_act
												__lowercase					:							Optional[Any]			=   use_absolute_embeddings
												__lowercase					:							Tuple			=   layer_norm_eps
												__lowercase					:							Optional[Any]			=   initializer_range
												# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
												# this indicates the channel dimension after the last stage of the model
												__lowercase					:							Dict			=   int(embed_dim * 2 ** (len(UpperCamelCase__							) - 1)							)
												__lowercase					:							Tuple			=   ['''stem'''] + [F"stage{idx}" for idx in range(1						,   len(UpperCamelCase__							) + 1							)]
												__lowercase					:							Dict			=   get_aligned_output_features_output_indices(
												    out_features=UpperCamelCase__						,   out_indices=UpperCamelCase__						,   stage_names=self.stage_names							)
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import tensorflow as tf

					from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					lowercase__						(   unittest.TestCase):


						@slow
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							AutoTokenizer.from_pretrained('''google/mt5-small'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer('''Hello there'''    ,		return_tensors='''tf'''						).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer('''Hi I am'''    ,		return_tensors='''tf'''						).input_ids

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						).loss
												SCREAMING_SNAKE_CASE							:    int							=							-tf.math.reduce_mean(UpperCamelCase__						).numpy()

												SCREAMING_SNAKE_CASE							:    List[str]							=							-21.22_8168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)





