




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 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						Optional[int]		):
       return F"gaussian_noise_s={seed}_shape={'_'.join([str(_lowerCAmelCase		) for s in shape]		)}.npy"



 def  lowerCAmelCase_    (				self    :						Tuple		):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()



 def  lowerCAmelCase_    (				self    :						int							,							_lowerCAmelCase    :						int=0							,							_lowerCAmelCase    :						Dict=(4, 4, 64, 64)							,							_lowerCAmelCase    :						List[str]=False		):
       SCREAMING_SNAKE_CASE_      		=     jnp.bfloataa if fpaa else jnp.floataa
       SCREAMING_SNAKE_CASE_      		=     jnp.array(load_hf_numpy(self.get_file_format(_lowerCAmelCase							,							_lowerCAmelCase		)		)							,							dtype=_lowerCAmelCase		)
       return image



 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						List[Any]=False							,							_lowerCAmelCase    :						Optional[Any]="CompVis/stable-diffusion-v1-4"		):
       SCREAMING_SNAKE_CASE_      		=     jnp.bfloataa if fpaa else jnp.floataa
       SCREAMING_SNAKE_CASE_      		=     'bf16' if fpaa else None

       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     FlaxUNetaDConditionModel.from_pretrained(
           _lowerCAmelCase							,							subfolder='unet'							,							dtype=_lowerCAmelCase							,							revision=_lowerCAmelCase		)
       return model, params



 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Tuple=0							,							_lowerCAmelCase    :						List[str]=(4, 77, 768)							,							_lowerCAmelCase    :						List[str]=False		):
       SCREAMING_SNAKE_CASE_      		=     jnp.bfloataa if fpaa else jnp.floataa
       SCREAMING_SNAKE_CASE_      		=     jnp.array(load_hf_numpy(self.get_file_format(_lowerCAmelCase							,							_lowerCAmelCase		)		)							,							dtype=_lowerCAmelCase		)
       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, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
         # fmt: on
     ]		)
 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'							,							fpaa=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.get_latents(_lowerCAmelCase							,							fpaa=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.get_encoder_hidden_states(_lowerCAmelCase							,							fpaa=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     model.apply(
           {'params': params}							,							_lowerCAmelCase							,							jnp.array(_lowerCAmelCase							,							dtype=jnp.intaa		)							,							encoder_hidden_states=_lowerCAmelCase							,							).sample

       assert sample.shape == latents.shape

       SCREAMING_SNAKE_CASE_      		=     jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)							,							dtype=jnp.floataa		)
       SCREAMING_SNAKE_CASE_      		=     jnp.array(_lowerCAmelCase							,							dtype=jnp.floataa		)

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
       assert jnp.allclose(_lowerCAmelCase							,							_lowerCAmelCase							,							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, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
         # fmt: on
     ]		)
 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						Dict		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.get_unet_model(model_id='stabilityai/stable-diffusion-2'							,							fpaa=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.get_latents(_lowerCAmelCase							,							shape=(4, 4, 96, 96)							,							fpaa=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.get_encoder_hidden_states(_lowerCAmelCase							,							shape=(4, 77, 1_024)							,							fpaa=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     model.apply(
           {'params': params}							,							_lowerCAmelCase							,							jnp.array(_lowerCAmelCase							,							dtype=jnp.intaa		)							,							encoder_hidden_states=_lowerCAmelCase							,							).sample

       assert sample.shape == latents.shape

       SCREAMING_SNAKE_CASE_      		=     jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)							,							dtype=jnp.floataa		)
       SCREAMING_SNAKE_CASE_      		=     jnp.array(_lowerCAmelCase							,							dtype=jnp.floataa		)

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







"""simple docstring"""





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       __A		(   SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				OpenAIGPTTokenizer
					UpperCAmelCase__								=				OpenAIGPTTokenizerFast
					UpperCAmelCase__								=				True
					UpperCAmelCase__								=				False

					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[Any]:
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											__magic_name__:	str							=       [
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """w</w>""",
											    """r</w>""",
											    """t</w>""",
											    """lo""",
											    """low""",
											    """er</w>""",
											    """low</w>""",
											    """lowest</w>""",
											    """newer</w>""",
											    """wider</w>""",
											    """<unk>""",
											]
											__magic_name__:	Any							=       dict(zip(__snake_case     ,		range(len(__snake_case					)					)					)					)
											__magic_name__:	List[str]							=       ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

											__magic_name__:	Optional[Any]							=       os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["""vocab_file"""]					)
											__magic_name__:	Any							=       os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["""merges_file"""]					)
											with open(self.vocab_file     ,		"""w"""					) as fp:
																	fp.write(json.dumps(__snake_case					)					)
											with open(self.merges_file     ,		"""w"""					) as fp:
																	fp.write("""\n""".join(__snake_case					)					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Optional[Any]					)			->							Optional[int]:
											return "lower newer", "lower newer"

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Union[str, Any]:
											__magic_name__:	Optional[Any]							=       OpenAIGPTTokenizer(self.vocab_file     ,		self.merges_file					)

											__magic_name__:	Union[str, Any]							=       """lower"""
											__magic_name__:	Optional[int]							=       ["""low""", """er</w>"""]
											__magic_name__:	List[Any]							=       tokenizer.tokenize(__snake_case					)
											self.assertListEqual(__snake_case     ,		__snake_case					)

											__magic_name__:	Optional[int]							=       tokens + ["""<unk>"""]
											__magic_name__:	List[str]							=       [1_4, 1_5, 2_0]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case					)     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Any=1_5					)			->							Tuple:
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																							__magic_name__:	str							=       self.rust_tokenizer_class.from_pretrained(__snake_case     ,		**__snake_case					)

																							# Simple input
																							__magic_name__:	Dict							=       """This is a simple input"""
																							__magic_name__:	Any							=       ["""This is a simple input 1""", """This is a simple input 2"""]
																							__magic_name__:	int							=       ("""This is a simple input""", """This is a pair""")
																							__magic_name__:	int							=       [
																							    ("""This is a simple input 1""", """This is a simple input 2"""),
																							    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																							]

																							# Simple input tests
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Simple input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Simple input
																							self.assertRaises(
																							    __snake_case     ,		tokenizer_r.batch_encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""     ,		)

																							# Pair input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Pair input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Pair input
																							self.assertRaises(
																							    __snake_case     ,		tokenizer_r.batch_encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""     ,		)






					def   lowerCamelCase__   (			self  :      Dict					)			->							Any:
											pass



@require_ftfy
@require_spacy
@require_tokenizers
class       __A		(   SCREAMING_SNAKE_CASE_			):
					pass



