





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
							import jax
							import jax.numpy as jnp





@slow
@require_flax
class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										return f"""gaussian_noise_s={seed}_shape={"_".join([str(_lowercase					) for s in shape]					)}.npy"""






				def 			snake_case_    (  self					):

										"""simple docstring"""




										super().tearDown()
										gc.collect()






				def 			snake_case_    (  self			, _lowerCAmelCase=0			, _lowerCAmelCase=(4, 4, 64, 64)			, _lowerCAmelCase=False					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   jnp.bfloataa if fpaa else jnp.floataa
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   jnp.array(load_hf_numpy(self.get_file_format(_lowercase			, _lowercase					)					)			, dtype=_lowercase					)
										return image






				def 			snake_case_    (  self			, _lowerCAmelCase=False			, _lowerCAmelCase="CompVis/stable-diffusion-v1-4"					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   jnp.bfloataa if fpaa else jnp.floataa
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   '''bf16''' if fpaa else None

										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Any           =   FlaxUNetaDConditionModel.from_pretrained(
										    _lowercase			, subfolder='''unet'''			, dtype=_lowercase			, revision=_lowercase					)
										return model, params






				def 			snake_case_    (  self			, _lowerCAmelCase=0			, _lowerCAmelCase=(4, 77, 768)			, _lowerCAmelCase=False					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   jnp.bfloataa if fpaa else jnp.floataa
										__SCREAMING_SNAKE_CASE:				int           =   jnp.array(load_hf_numpy(self.get_file_format(_lowercase			, _lowercase					)					)			, dtype=_lowercase					)
										return hidden_states






				@parameterized.expand(
				    [
				        # fmt: off
				        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
				        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
				        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
				        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
				        # fmt: on
				    ]					)
				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''			, fpaa=_lowercase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   self.get_latents(_lowercase			, fpaa=_lowercase					)
										__SCREAMING_SNAKE_CASE:				Any           =   self.get_encoder_hidden_states(_lowercase			, fpaa=_lowercase					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   model.apply(
										    {'''params''': params}			, _lowercase			, jnp.array(_lowercase			, dtype=jnp.intaa					)			, encoder_hidden_states=_lowercase			, ).sample

										assert sample.shape == latents.shape

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())					)			, dtype=jnp.floataa					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   jnp.array(_lowercase			, dtype=jnp.floataa					)

										# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
										assert jnp.allclose(_lowercase			, _lowercase			, atol=1e-2					)






				@parameterized.expand(
				    [
				        # fmt: off
				        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
				        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
				        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
				        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
				        # fmt: on
				    ]					)
				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				int           =   self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''			, fpaa=_lowercase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   self.get_latents(_lowercase			, shape=(4, 4, 96, 96)			, fpaa=_lowercase					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_encoder_hidden_states(_lowercase			, shape=(4, 77, 1024)			, fpaa=_lowercase					)

										__SCREAMING_SNAKE_CASE:				Dict           =   model.apply(
										    {'''params''': params}			, _lowercase			, jnp.array(_lowercase			, dtype=jnp.intaa					)			, encoder_hidden_states=_lowercase			, ).sample

										assert sample.shape == latents.shape

										__SCREAMING_SNAKE_CASE:				List[str]           =   jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())					)			, dtype=jnp.floataa					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   jnp.array(_lowercase			, dtype=jnp.floataa					)

										# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
										assert jnp.allclose(_lowercase			, _lowercase			, atol=1e-2					)






import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						__magic_name__					(							snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:List[Any]										=      LayoutLMTokenizer
     UpperCamelCase_	:Dict										=      LayoutLMTokenizerFast
     UpperCamelCase_	:List[str]										=      True
     UpperCamelCase_	:Dict										=      True






     def 	UpperCAmelCase_				(				self	)->     str:
         super().setUp()

         UpperCamelCase_			  =						[
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["vocab_file"]	)
         with open(self.vocab_file		,						"w"		,						encoding="utf-8"	) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     List[str]:
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						"UNwant\u00E9d,running"
         UpperCamelCase_			  =						"unwanted, running"
         return input_text, output_text






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						self.tokenizer_class(self.vocab_file	)

         UpperCamelCase_			  =						tokenizer.tokenize("UNwant\u00E9d,running"	)
         self.assertListEqual(_lowercase		,						["un", "##want", "##ed", ",", "runn", "##ing"]	)
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase	)		,						[7, 4, 5, 10, 8, 9]	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         pass
