




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  __magic_name__						(  unittest.TestCase):
    '''simple docstring'''







    @slow
    def     _A							(							self:   List[str]		):
           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(_lowerCamelCase		,					labels=_lowerCamelCase		).loss
           SCREAMING_SNAKE_CASE_   	=    -tf.math.reduce_mean(_lowerCamelCase		).numpy()

           SCREAMING_SNAKE_CASE_   	=    -21.22_81_68
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2E-4		)








import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
			import jax






@require_flax
class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					FlaxAutoencoderKL


				@property
				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    4
											SCREAMING_SNAKE_CASE_   	=    3
											SCREAMING_SNAKE_CASE_   	=    (32, 32)

											SCREAMING_SNAKE_CASE_   	=    jax.random.PRNGKey(0		)
											SCREAMING_SNAKE_CASE_   	=    jax.random.uniform(_lowerCamelCase		,					((batch_size, num_channels) + sizes)		)

											return {"sample": image, "prng_key": prng_key}





				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    {
											    '''block_out_channels''': [32, 64],
											    '''in_channels''': 3,
											    '''out_channels''': 3,
											    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
											    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
											    '''latent_channels''': 4,
											}
											SCREAMING_SNAKE_CASE_   	=    self.dummy_input
											return init_dict, inputs_dict


