






"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase	:	List[Any]				=      logging.get_logger(__name__)

_lowerCAmelCase	:	int				=      {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''poolformer'''
   def __init__(		self      :	Optional[int]							,			snake_case__      :	Optional[Any]=3							,			snake_case__      :	Union[str, Any]=1_6							,			snake_case__      :	Dict=1_6							,			snake_case__      :	int=3							,			snake_case__      :	Tuple=4.0							,			snake_case__      :	str=[2, 2, 6, 2]							,			snake_case__      :	List[Any]=[6_4, 1_2_8, 3_2_0, 5_1_2]							,			snake_case__      :	Union[str, Any]=[7, 3, 3, 3]							,			snake_case__      :	Union[str, Any]=[4, 2, 2, 2]							,			snake_case__      :	Optional[int]=[2, 1, 1, 1]							,			snake_case__      :	Any=4							,			snake_case__      :	Optional[int]=0.0							,			snake_case__      :	Dict="gelu"							,			snake_case__      :	List[Any]=True							,			snake_case__      :	Dict=1e-5							,			snake_case__      :	List[Any]=0.02							,			**snake_case__      :	Optional[Any]							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							num_channels
        UpperCAmelCase__						:    int							=							patch_size
        UpperCAmelCase__						:    Tuple							=							stride
        UpperCAmelCase__						:    List[str]							=							padding
        UpperCAmelCase__						:    List[str]							=							pool_size
        UpperCAmelCase__						:    str							=							hidden_sizes
        UpperCAmelCase__						:    Optional[int]							=							mlp_ratio
        UpperCAmelCase__						:    Union[str, Any]							=							depths
        UpperCAmelCase__						:    Union[str, Any]							=							patch_sizes
        UpperCAmelCase__						:    Optional[int]							=							strides
        UpperCAmelCase__						:    Optional[Any]							=							num_encoder_blocks
        UpperCAmelCase__						:    str							=							drop_path_rate
        UpperCAmelCase__						:    str							=							hidden_act
        UpperCAmelCase__						:    int							=							use_layer_scale
        UpperCAmelCase__						:    int							=							layer_scale_init_value
        UpperCAmelCase__						:    Any							=							initializer_range
        super().__init__(**snake_case__							)




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =version.parse('''1.11'''   )
   @property
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]							)




   @property
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        return 2e-3










"""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 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"							)
        UpperCAmelCase__						:    str							=							AutoTokenizer.from_pretrained("google/mt5-small"							)

        UpperCAmelCase__						:    str							=							tokenizer("Hello there"							,			return_tensors="np"							).input_ids
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer("Hi I am"							,			return_tensors="np"							).input_ids

        UpperCAmelCase__						:    Any							=							shift_tokens_right(snake_case__							,			model.config.pad_token_id							,			model.config.decoder_start_token_id							)

        UpperCAmelCase__						:    Any							=							model(snake_case__							,			decoder_input_ids=snake_case__							).logits
        UpperCAmelCase__						:    Any							=							optax.softmax_cross_entropy(snake_case__							,			onehot(snake_case__							,			logits.shape[-1]							)							).mean()

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

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


