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


__UpperCamelCase		:       Dict									=     logging.get_logger(__name__)





class 					lowercase__						(   UpperCamelCase_	,							UpperCamelCase_):
						UpperCamelCase_     =		"""maskformer-swin"""

						UpperCamelCase_     =		{
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}


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





												'''simple docstring'''





												super().__init__(**UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    str							=							image_size
												SCREAMING_SNAKE_CASE							:    int							=							patch_size
												SCREAMING_SNAKE_CASE							:    Dict							=							num_channels
												SCREAMING_SNAKE_CASE							:    Dict							=							embed_dim
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							depths
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							len(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    int							=							num_heads
												SCREAMING_SNAKE_CASE							:    List[Any]							=							window_size
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							mlp_ratio
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							qkv_bias
												SCREAMING_SNAKE_CASE							:    List[Any]							=							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:    int							=							drop_path_rate
												SCREAMING_SNAKE_CASE							:    Tuple							=							hidden_act
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							use_absolute_embeddings
												SCREAMING_SNAKE_CASE							:    Tuple							=							layer_norm_eps
												SCREAMING_SNAKE_CASE							:    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
												SCREAMING_SNAKE_CASE							:    Dict							=							int(embed_dim * 2 ** (len(UpperCamelCase__						) - 1)						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							['''stem'''] + [f"""stage{idx}""" for idx in range(1    ,		len(UpperCamelCase__						) + 1						)]
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    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						)





