




from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :     List[str]		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     str		    =       {
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''donut-swin'''

	A    							= {
	    '''num_attention_heads''': '''num_heads''',
	    '''num_hidden_layers''': '''num_layers''',
	}





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=224      ,     SCREAMING_SNAKE_CASE=4      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=96      ,     SCREAMING_SNAKE_CASE=[2, 2, 6, 2]      ,     SCREAMING_SNAKE_CASE=[3, 6, 12, 24]      ,     SCREAMING_SNAKE_CASE=7      ,     SCREAMING_SNAKE_CASE=4.0      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=0.0      ,     SCREAMING_SNAKE_CASE=0.0      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE="gelu"      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=1e-5      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					List[Any]:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						image_size
				SCREAMING_SNAKE_CASE_								=						patch_size
				SCREAMING_SNAKE_CASE_								=						num_channels
				SCREAMING_SNAKE_CASE_								=						embed_dim
				SCREAMING_SNAKE_CASE_								=						depths
				SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						num_heads
				SCREAMING_SNAKE_CASE_								=						window_size
				SCREAMING_SNAKE_CASE_								=						mlp_ratio
				SCREAMING_SNAKE_CASE_								=						qkv_bias
				SCREAMING_SNAKE_CASE_								=						hidden_dropout_prob
				SCREAMING_SNAKE_CASE_								=						attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE_								=						drop_path_rate
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						use_absolute_embeddings
				SCREAMING_SNAKE_CASE_								=						layer_norm_eps
				SCREAMING_SNAKE_CASE_								=						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_								=						int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE      ) - 1)      )






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       a_       (     unittest.TestCase     ):





	@slow
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('google/mt5-small'      )

				SCREAMING_SNAKE_CASE_								=						tokenizer('Hello there'      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						tokenizer('Hi I am'      ,     return_tensors='tf'      ).input_ids

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      ).loss
				SCREAMING_SNAKE_CASE_								=						-tf.math.reduce_mean(SCREAMING_SNAKE_CASE      ).numpy()

				SCREAMING_SNAKE_CASE_								=						-2_1.2_2_8_1_6_8
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )
