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


SCREAMING_SNAKE_CASE__							=				logging.get_logger(__name__)
class 	A__   (						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE				):
							lowerCAmelCase__		:						Optional[Any]    			=							"maskformer-swin"

							lowerCAmelCase__		:						List[Any]    			=							{
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}





							def __init__( self  :   Optional[int]     , _UpperCAmelCase  :   int=2_24     , _UpperCAmelCase  :   Tuple=4     , _UpperCAmelCase  :   int=3     , _UpperCAmelCase  :   List[Any]=96     , _UpperCAmelCase  :   Union[str, Any]=[2, 2, 6, 2]     , _UpperCAmelCase  :   Any=[3, 6, 12, 24]     , _UpperCAmelCase  :   List[str]=7     , _UpperCAmelCase  :   List[str]=4.0     , _UpperCAmelCase  :   Optional[int]=True     , _UpperCAmelCase  :   Tuple=0.0     , _UpperCAmelCase  :   Any=0.0     , _UpperCAmelCase  :   Any=0.1     , _UpperCAmelCase  :   str="gelu"     , _UpperCAmelCase  :   Tuple=False     , _UpperCAmelCase  :   Union[str, Any]=0.02     , _UpperCAmelCase  :   str=1e-5     , _UpperCAmelCase  :   Optional[int]=None     , _UpperCAmelCase  :   str=None     , **_UpperCAmelCase  :   Union[str, Any]     , )						->		List[str]:



											"""simple docstring"""


											super().__init__(**_SCREAMING_SNAKE_CASE				)

											__lowercase								=					image_size
											__lowercase								=					patch_size
											__lowercase								=					num_channels
											__lowercase								=					embed_dim
											__lowercase								=					depths
											__lowercase								=					len(_SCREAMING_SNAKE_CASE				)
											__lowercase								=					num_heads
											__lowercase								=					window_size
											__lowercase								=					mlp_ratio
											__lowercase								=					qkv_bias
											__lowercase								=					hidden_dropout_prob
											__lowercase								=					attention_probs_dropout_prob
											__lowercase								=					drop_path_rate
											__lowercase								=					hidden_act
											__lowercase								=					use_absolute_embeddings
											__lowercase								=					layer_norm_eps
											__lowercase								=					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								=					int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE				) - 1)				)
											__lowercase								=					["stem"] + [f"""stage{idx}""" for idx in range(1     , len(_SCREAMING_SNAKE_CASE				) + 1				)]
											__lowercase								=					get_aligned_output_features_output_indices(
											    out_features=_SCREAMING_SNAKE_CASE     , out_indices=_SCREAMING_SNAKE_CASE     , stage_names=self.stage_names				)










"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)