




import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()

class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    "hf-internal-testing/tiny-stable-diffusion-pipe"
      lowerCAmelCase__	:       List[str]			=    1
      lowerCAmelCase__	:       str			=    77
      lowerCAmelCase__	:       Union[str, Any]			=    32
      lowerCAmelCase__	:       int			=    4
      lowerCAmelCase__	:       Tuple			=    64
      lowerCAmelCase__	:       Optional[Any]			=    torch.manual_seed(0      )







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       Dict=0.99_99 ):
          __UpperCamelCase     						=  UNetaDConditionModel.from_pretrained(self.model_id       ,				subfolder='''unet''' )
          __UpperCamelCase     						=  unet.to(snake_case )
          __UpperCamelCase     						=  EMAModel(unet.parameters()       ,				decay=snake_case       ,				model_cls=snake_case       ,				model_config=unet.config )
          return unet, ema_unet







      def 					snake_case		(   self    :       Optional[int] ):
          __UpperCamelCase     						=  torch.randn(
              self.batch_size       ,				self.num_in_channels       ,				self.latent_height       ,				self.latent_width       ,				generator=self.generator ).to(snake_case )
          __UpperCamelCase     						=  torch.randint(0       ,				1000       ,				size=(self.batch_size,)       ,				generator=self.generator ).to(snake_case )
          __UpperCamelCase     						=  torch.randn(
              self.batch_size       ,				self.prompt_length       ,				self.text_encoder_hidden_dim       ,				generator=self.generator ).to(snake_case )
          return noisy_latents, timesteps, encoder_hidden_states







      def 					snake_case		(   self    :       Dict       ,				snake_case    :       Dict ):
          __UpperCamelCase     						=  {}
          for k, param in unet.state_dict().items():
              __UpperCamelCase     						=  torch.randn_like(snake_case ) + (param * torch.randn_like(snake_case ))
              updated_state_dict.update({k: updated_param} )
          unet.load_state_dict(snake_case )
          return unet







      def 					snake_case		(   self    :       int ):
          __UpperCamelCase						,					__UpperCamelCase     						=  self.get_models()
          # Take the first (hypothetical) EMA step.
          ema_unet.step(unet.parameters() )
          assert ema_unet.optimization_step == 1

          # Take two more.
          for _ in range(2 ):
              ema_unet.step(unet.parameters() )
          assert ema_unet.optimization_step == 3







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase						,					__UpperCamelCase     						=  self.get_models()
          # Since the `unet` is not being updated (i.e., backprop'd)
          # there won't be any difference between the `params` of `unet`
          # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
          ema_unet.step(unet.parameters() )
          __UpperCamelCase     						=  list(unet.parameters() )
          for s_param, param in zip(ema_unet.shadow_params       ,				snake_case ):
              assert torch.allclose(snake_case       ,				snake_case )

          # The above holds true even if we call `ema.step()` multiple times since
          # `unet` params are still not being updated.
          for _ in range(4 ):
              ema_unet.step(unet.parameters() )
          for s_param, param in zip(ema_unet.shadow_params       ,				snake_case ):
              assert torch.allclose(snake_case       ,				snake_case )







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase						,					__UpperCamelCase     						=  self.get_models()
          # Here we simulate the parameter updates for `unet`. Since there might
          # be some parameters which are initialized to zero we take extra care to
          # initialize their values to something non-zero before the multiplication.
          __UpperCamelCase     						=  self.simulate_backprop(snake_case )

          # Take the EMA step.
          ema_unet.step(unet_pseudo_updated_step_one.parameters() )

          # Now the EMA'd parameters won't be equal to the original model parameters.
          __UpperCamelCase     						=  list(unet_pseudo_updated_step_one.parameters() )
          for s_param, param in zip(ema_unet.shadow_params       ,				snake_case ):
              assert ~torch.allclose(snake_case       ,				snake_case )

          # Ensure this is the case when we take multiple EMA steps.
          for _ in range(4 ):
              ema_unet.step(unet.parameters() )
          for s_param, param in zip(ema_unet.shadow_params       ,				snake_case ):
              assert ~torch.allclose(snake_case       ,				snake_case )







      def 					snake_case		(   self    :       List[str] ):
          # If we call EMA step after a backpropagation consecutively for two times,
          # the shadow params from those two steps should be different.
          __UpperCamelCase						,					__UpperCamelCase     						=  self.get_models()

          # First backprop + EMA
          __UpperCamelCase     						=  self.simulate_backprop(snake_case )
          ema_unet.step(unet_step_one.parameters() )
          __UpperCamelCase     						=  ema_unet.shadow_params

          # Second backprop + EMA
          __UpperCamelCase     						=  self.simulate_backprop(snake_case )
          ema_unet.step(unet_step_two.parameters() )
          __UpperCamelCase     						=  ema_unet.shadow_params

          for step_one, step_two in zip(snake_case       ,				snake_case ):
              assert ~torch.allclose(snake_case       ,				snake_case )







      def 					snake_case		(   self    :       Optional[int] ):
          # If there's no decay even if there are backprops, EMA steps
          # won't take any effect i.e., the shadow params would remain the
          # same.
          __UpperCamelCase						,					__UpperCamelCase     						=  self.get_models(decay=0.0 )
          __UpperCamelCase     						=  self.simulate_backprop(snake_case )
          ema_unet.step(unet_step_one.parameters() )
          __UpperCamelCase     						=  ema_unet.shadow_params

          __UpperCamelCase     						=  self.simulate_backprop(snake_case )
          ema_unet.step(unet_step_two.parameters() )
          __UpperCamelCase     						=  ema_unet.shadow_params

          for step_one, step_two in zip(snake_case       ,				snake_case ):
              assert torch.allclose(snake_case       ,				snake_case )







      @skip_mps
      def 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase						,					__UpperCamelCase     						=  self.get_models()
          __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  self.get_dummy_inputs()

          with tempfile.TemporaryDirectory() as tmpdir:
              ema_unet.save_pretrained(snake_case )
              __UpperCamelCase     						=  UNetaDConditionModel.from_pretrained(snake_case       ,				model_cls=snake_case )
              __UpperCamelCase     						=  loaded_unet.to(unet.device )

          # Since no EMA step has been performed the outputs should match.
          __UpperCamelCase     						=  unet(snake_case       ,				snake_case       ,				snake_case ).sample
          __UpperCamelCase     						=  loaded_unet(snake_case       ,				snake_case       ,				snake_case ).sample

          assert torch.allclose(snake_case       ,				snake_case       ,				atol=1E-4 )












import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin
class 				__snake_case     (_a  ,		unittest.TestCase						):
				lowerCAmelCase__			=   SpeechaTextaTokenizer
				lowerCAmelCase__			=   False



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			Optional[int]:





								'''simple docstring'''




								super().setUp()

								_lowerCAmelCase			:			Optional[int]        =					"""<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab""".split(""" """					)
								_lowerCAmelCase			:			Optional[int]        =					["""he re</w> 123""", """here a 1456"""]
								_lowerCAmelCase			:			Union[str, Any]        =					dict(zip(_UpperCAmelCase						,		range(len(_UpperCAmelCase					)					)					)					)

								_lowerCAmelCase			:			Any        =					{"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

								_lowerCAmelCase			:			Union[str, Any]        =					tempfile.mkdtemp()
								_lowerCAmelCase			:			Tuple        =					os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""vocab_file"""]					)
								_lowerCAmelCase			:			Any        =					os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""merges_file"""]					)

								with open(self.vocab_file						,		"""w"""						,		encoding="""utf-8"""					) as fp:
												fp.write(json.dumps(_UpperCAmelCase					) + """\n"""					)

								with open(self.merges_file						,		"""w"""					) as fp:
												fp.write("""\n""".join(_UpperCAmelCase					)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					list(self.get_tokenizer().get_vocab().keys()					)

								self.assertEqual(vocab_keys[0]						,		"""<s>"""					)
								self.assertEqual(vocab_keys[1]						,		"""<pad>"""					)
								self.assertEqual(vocab_keys[-1]						,		"""vocab"""					)
								self.assertEqual(len(_UpperCAmelCase					)						,		14					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			int:





								'''simple docstring'''




								self.assertEqual(self.get_tokenizer().vocab_size						,		14					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

								# make sure @@ is correctly concatenated
								_lowerCAmelCase			:			int        =					[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
								_lowerCAmelCase			:			List[Any]        =					tokenizer.decode(_UpperCAmelCase					)

								self.assertTrue(output_string == """herecouple words ofthe"""					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			str        =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

								with tempfile.TemporaryDirectory() as tmp_dirname:
												tokenizer.save_pretrained(_UpperCAmelCase					)
												os.remove(os.path.join(_UpperCAmelCase						,		"""merges.txt"""					)					)

												# load tokenizer without merges file should not throw an error
												_lowerCAmelCase			:			Any        =					SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase					)

								with tempfile.TemporaryDirectory() as tmp_dirname:
												# save tokenizer and load again
												tokenizer.save_pretrained(_UpperCAmelCase					)
												_lowerCAmelCase			:			int        =					SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase					)

								self.assertIsNotNone(_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Optional[Any]:





								'''simple docstring'''




								if not self.test_slow_tokenizer:
												return

								_lowerCAmelCase			:			Dict        =					inspect.signature(self.tokenizer_class.__init__					)
								_lowerCAmelCase			:			Union[str, Any]        =					self.get_tokenizer()

								for parameter_name, parameter in signature.parameters.items():
												if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																self.assertIn(_UpperCAmelCase						,		tokenizer.init_kwargs					)

