import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
						from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

						from .test_feature_extraction_speechta import floats_list


lowerCAmelCase__  	=      get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
@require_torch
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   =tempfile.mkdtemp()

		_lowercase   =SpeechTaTokenizer(lowerCAmelCase_       )
		tokenizer.save_pretrained(self.tmpdirname       )

		_lowercase   ={
		    "feature_size": 1,
		    "padding_value": 0.0,
		    "sampling_rate": 16000,
		    "do_normalize": False,
		    "num_mel_bins": 80,
		    "hop_length": 16,
		    "win_length": 64,
		    "win_function": "hann_window",
		    "fmin": 80,
		    "fmax": 7600,
		    "mel_floor": 1e-10,
		    "reduction_factor": 2,
		    "return_attention_mask": True,
		}

		_lowercase   =os.path.join(self.tmpdirname       ,  lowerCAmelCase_       )
		with open(self.feature_extraction_file       ,  "w"       ,  encoding="utf-8"       ) as fp:
			fp.write(json.dumps(lowerCAmelCase_       ) + "\n"       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		shutil.rmtree(self.tmpdirname       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_tokenizer()
		_lowercase   =self.get_feature_extractor()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		processor.save_pretrained(self.tmpdirname       )
		_lowercase   =SpeechTaProcessor.from_pretrained(self.tmpdirname       )

		self.assertEqual(processor.tokenizer.get_vocab()       ,  tokenizer.get_vocab()       )
		self.assertIsInstance(processor.tokenizer       ,  lowerCAmelCase_       )

		self.assertEqual(processor.feature_extractor.to_json_string()       ,  feature_extractor.to_json_string()       )
		self.assertIsInstance(processor.feature_extractor       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,  feature_extractor=self.get_feature_extractor()       )
		processor.save_pretrained(self.tmpdirname       )

		_lowercase   =self.get_tokenizer(bos_token="(BOS)"       ,  eos_token="(EOS)"       )
		_lowercase   =self.get_feature_extractor(do_normalize=lowerCAmelCase_       ,  padding_value=1.0       )

		_lowercase   =SpeechTaProcessor.from_pretrained(
		    self.tmpdirname       ,  bos_token="(BOS)"       ,  eos_token="(EOS)"       ,  do_normalize=lowerCAmelCase_       ,  padding_value=1.0       )

		self.assertEqual(processor.tokenizer.get_vocab()       ,  tokenizer_add_kwargs.get_vocab()       )
		self.assertIsInstance(processor.tokenizer       ,  lowerCAmelCase_       )

		self.assertEqual(processor.feature_extractor.to_json_string()       ,  feature_extractor_add_kwargs.to_json_string()       )
		self.assertIsInstance(processor.feature_extractor       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   =floats_list((3, 1000)       )

		_lowercase   =feature_extractor(audio=lowerCAmelCase_       ,  return_tensors="np"       )
		_lowercase   =processor(audio=lowerCAmelCase_       ,  return_tensors="np"       )

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()       ,  input_processor[key].sum()       ,  delta=1e-2       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   =floats_list((3, 1000)       )

		_lowercase   =feature_extractor(audio_target=lowerCAmelCase_       ,  return_tensors="np"       )
		_lowercase   =processor(audio_target=lowerCAmelCase_       ,  return_tensors="np"       )

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()       ,  input_processor[key].sum()       ,  delta=1e-2       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   ="This is a test string"

		_lowercase   =processor(text=lowerCAmelCase_       )
		_lowercase   =tokenizer(lowerCAmelCase_       )

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]       ,  encoded_processor[key]       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   ="This is a test string"

		_lowercase   =processor(text_target=lowerCAmelCase_       )
		_lowercase   =tokenizer(lowerCAmelCase_       )

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]       ,  encoded_processor[key]       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		_lowercase   =processor.batch_decode(lowerCAmelCase_       )
		_lowercase   =tokenizer.batch_decode(lowerCAmelCase_       )

		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		self.assertListEqual(
		    processor.model_input_names       ,  feature_extractor.model_input_names       ,  msg="`processor` and `feature_extractor` model input names do not match"       ,  )










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):
       _a			       =    '''hf-internal-testing/tiny-stable-diffusion-pipe'''
       _a			       =    1
       _a			       =    77
       _a			       =    32
       _a			       =    4
       _a			       =    _a			       =    64
       _a			       =    torch.manual_seed(0)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       int=0.99_99					):
         lowercase     :Optional[int]   						=    UNetaDConditionModel.from_pretrained(self.model_id							,       subfolder="unet"					)
         lowercase     :Union[str, Any]   						=    unet.to(_lowerCAmelCase					)
         lowercase     :Any   						=    EMAModel(unet.parameters()							,       decay=_lowerCAmelCase							,       model_cls=_lowerCAmelCase							,       model_config=unet.config					)
         return unet, ema_unet






       def        SCREAMING_SNAKE_CASE     ( self:       List[str]					):
         lowercase     :Optional[Any]   						=    torch.randn(
             self.batch_size							,       self.num_in_channels							,       self.latent_height							,       self.latent_width							,       generator=self.generator					).to(_lowerCAmelCase					)
         lowercase     :Dict   						=    torch.randint(0							,       10_00							,       size=(self.batch_size,)							,       generator=self.generator					).to(_lowerCAmelCase					)
         lowercase     :List[Any]   						=    torch.randn(
             self.batch_size							,       self.prompt_length							,       self.text_encoder_hidden_dim							,       generator=self.generator					).to(_lowerCAmelCase					)
         return noisy_latents, timesteps, encoder_hidden_states






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Optional[Any]					):
         lowercase     :Any   						=    {}
         for k, param in unet.state_dict().items():
           lowercase     :Optional[int]   						=    torch.randn_like(_lowerCAmelCase					) + (param * torch.randn_like(_lowerCAmelCase					))
           updated_state_dict.update({k: updated_param}					)
         unet.load_state_dict(_lowerCAmelCase					)
         return unet






       def        SCREAMING_SNAKE_CASE     ( self:       int					):
         lowercase						,     lowercase     :List[str]   						=    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        SCREAMING_SNAKE_CASE     ( self:       Any					):
         lowercase						,     lowercase     :str   						=    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()					)
         lowercase     :Tuple   						=    list(unet.parameters()					)
         for s_param, param in zip(ema_unet.shadow_params							,       _lowerCAmelCase					):
           assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)

         # 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							,       _lowerCAmelCase					):
           assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         lowercase						,     lowercase     :Optional[int]   						=    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.
         lowercase     :List[str]   						=    self.simulate_backprop(_lowerCAmelCase					)

         # 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.
         lowercase     :str   						=    list(unet_pseudo_updated_step_one.parameters()					)
         for s_param, param in zip(ema_unet.shadow_params							,       _lowerCAmelCase					):
           assert ~torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)

         # 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							,       _lowerCAmelCase					):
           assert ~torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]					):
         # If we call EMA step after a backpropagation consecutively for two times,
         # the shadow params from those two steps should be different.
         lowercase						,     lowercase     :Optional[Any]   						=    self.get_models()

         # First backprop + EMA
         lowercase     :Union[str, Any]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_one.parameters()					)
         lowercase     :int   						=    ema_unet.shadow_params

         # Second backprop + EMA
         lowercase     :Optional[int]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_two.parameters()					)
         lowercase     :Optional[int]   						=    ema_unet.shadow_params

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






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         # 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.
         lowercase						,     lowercase     :Any   						=    self.get_models(decay=0.0					)
         lowercase     :List[Any]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_one.parameters()					)
         lowercase     :List[str]   						=    ema_unet.shadow_params

         lowercase     :str   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_two.parameters()					)
         lowercase     :List[str]   						=    ema_unet.shadow_params

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






       @skip_mps
       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         lowercase						,     lowercase     :Optional[Any]   						=    self.get_models()
         lowercase						,     lowercase						,     lowercase     :str   						=    self.get_dummy_inputs()

         with tempfile.TemporaryDirectory() as tmpdir:
           ema_unet.save_pretrained(_lowerCAmelCase					)
           lowercase     :Optional[int]   						=    UNetaDConditionModel.from_pretrained(_lowerCAmelCase							,       model_cls=_lowerCAmelCase					)
           lowercase     :Tuple   						=    loaded_unet.to(unet.device					)

         # Since no EMA step has been performed the outputs should match.
         lowercase     :Dict   						=    unet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					).sample
         lowercase     :Any   						=    loaded_unet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					).sample

         assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase							,       atol=1e-4					)



