



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__)

SCREAMING_SNAKE_CASE__       =      {
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}


class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_):
					"""simple docstring"""






					A__				:		str			       =    "bit"
					A__				:		Any			       =    ["preactivation", "bottleneck"]
					A__				:		Optional[int]			       =    ["SAME", "VALID"]



					def __init__(     self					:       Dict				,					_snake_case					:       List[str]=3				,					_snake_case					:       Optional[Any]=64				,					_snake_case					:       Dict=[2_56, 5_12, 10_24, 20_48]				,					_snake_case					:       Any=[3, 4, 6, 3]				,					_snake_case					:       Union[str, Any]="preactivation"				,					_snake_case					:       Optional[int]="relu"				,					_snake_case					:       Optional[Any]=None				,					_snake_case					:       str=32				,					_snake_case					:       Dict=0.0				,					_snake_case					:       Any=False				,					_snake_case					:       List[str]=32				,					_snake_case					:       Optional[int]=1				,					_snake_case					:       Optional[Any]=None				,					_snake_case					:       Optional[Any]=None				,					**_snake_case					:       int				,					):






								"""simple docstring"""


								super().__init__(**_snake_case					)
								if layer_type not in self.layer_types:
											raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types					)}'''					)
								if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
														A__       =    global_padding.upper()
											else:
														raise ValueError(F'''Padding strategy {global_padding} not supported'''					)
								A__       =    num_channels
								A__       =    embedding_size
								A__       =    hidden_sizes
								A__       =    depths
								A__       =    layer_type
								A__       =    hidden_act
								A__       =    global_padding
								A__       =    num_groups
								A__       =    drop_path_rate
								A__       =    embedding_dynamic_padding
								A__       =    output_stride
								A__       =    width_factor

								A__       =    ['stem'] + [F'''stage{idx}''' for idx in range(1				,					len(_snake_case					) + 1					)]
								A__     , A__       =    get_aligned_output_features_output_indices(
								    out_features=_snake_case				,					out_indices=_snake_case				,					stage_names=self.stage_names					)









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      ):
					"""simple docstring"""






					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'					)
								A__       =    AutoTokenizer.from_pretrained('google/mt5-small'					)

								A__       =    tokenizer('Hello there'				,					return_tensors='np'					).input_ids
								A__       =    tokenizer('Hi I am'				,					return_tensors='np'					).input_ids

								A__       =    shift_tokens_right(_snake_case				,					model.config.pad_token_id				,					model.config.decoder_start_token_id					)

								A__       =    model(_snake_case				,					decoder_input_ids=_snake_case					).logits
								A__       =    optax.softmax_cross_entropy(_snake_case				,					onehot(_snake_case				,					logits.shape[-1]					)					).mean()

								A__       =    -(labels.shape[-1] * loss.item())

								A__       =    -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




