
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list




@require_torchaudio
@require_sentencepiece
class 						lowercase__  (						unittest.TestCase  ):
	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= 'laion/clap-htsat-unfused'
						SCREAMING_SNAKE_CASE__													= tempfile.mkdtemp()
	def      A_				(			self							:   List[Any]       ,   **UpperCAmelCase_							:   Any							):
						return RobertaTokenizer.from_pretrained(self.checkpoint       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   List[Any]       ,   **UpperCAmelCase_							:   str							):
						return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   Dict							):
						shutil.rmtree(self.tmpdirname							)
	def      A_				(			self							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()
						SCREAMING_SNAKE_CASE__													= self.get_feature_extractor()

						SCREAMING_SNAKE_CASE__													= ClapProcessor(tokenizer=UpperCAmelCase_       ,   feature_extractor=UpperCAmelCase_							)

						processor.save_pretrained(self.tmpdirname							)
						SCREAMING_SNAKE_CASE__													= ClapProcessor.from_pretrained(self.tmpdirname							)

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

						self.assertEqual(processor.feature_extractor.to_json_string()       ,   feature_extractor.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor       ,   UpperCAmelCase_							)
	def      A_				(			self							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= ClapProcessor(tokenizer=self.get_tokenizer()       ,   feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						SCREAMING_SNAKE_CASE__													= self.get_tokenizer(bos_token='(BOS)'       ,   eos_token='(EOS)'							)
						SCREAMING_SNAKE_CASE__													= self.get_feature_extractor(do_normalize=UpperCAmelCase_       ,   padding_value=1.0							)

						SCREAMING_SNAKE_CASE__													= ClapProcessor.from_pretrained(
						    self.tmpdirname       ,   bos_token='(BOS)'       ,   eos_token='(EOS)'       ,   do_normalize=UpperCAmelCase_       ,   padding_value=1.0							)

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

						self.assertEqual(processor.feature_extractor.to_json_string()       ,   feature_extractor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= self.get_feature_extractor()
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__													= ClapProcessor(tokenizer=UpperCAmelCase_       ,   feature_extractor=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= floats_list((3, 1000)							)

						SCREAMING_SNAKE_CASE__													= feature_extractor(UpperCAmelCase_       ,   return_tensors='np'							)
						SCREAMING_SNAKE_CASE__													= processor(audios=UpperCAmelCase_       ,   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      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= self.get_feature_extractor()
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__													= ClapProcessor(tokenizer=UpperCAmelCase_       ,   feature_extractor=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= 'This is a test string'

						SCREAMING_SNAKE_CASE__													= processor(text=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tokenizer(UpperCAmelCase_							)

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]       ,   encoded_processor[key]							)
	def      A_				(			self							:   int							):
						SCREAMING_SNAKE_CASE__													= self.get_feature_extractor()
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__													= ClapProcessor(tokenizer=UpperCAmelCase_       ,   feature_extractor=UpperCAmelCase_							)

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

						SCREAMING_SNAKE_CASE__													= processor.batch_decode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.batch_decode(UpperCAmelCase_							)

						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)





	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= self.get_feature_extractor()
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__													= ClapProcessor(tokenizer=UpperCAmelCase_       ,   feature_extractor=UpperCAmelCase_							)

						self.assertListEqual(
						    processor.model_input_names[2:]       ,   feature_extractor.model_input_names       ,   msg='`processor` and `feature_extractor` model input names do not match'       ,   )







import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= []
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=UpperCAmelCase_       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= FlaxTransformeraDModel(
											    in_channels=self.out_channels       ,   n_heads=self.num_attention_heads       ,   d_head=self.out_channels // self.num_attention_heads       ,   depth=1       ,   use_linear_projection=self.use_linear_projection       ,   only_cross_attention=self.only_cross_attention       ,   use_memory_efficient_attention=self.use_memory_efficient_attention       ,   dtype=self.dtype       ,   )
											attentions.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets
						SCREAMING_SNAKE_CASE__													= attentions

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= FlaxDownsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   int       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   int=True							):
						SCREAMING_SNAKE_CASE__													= ()

						for resnet, attn in zip(self.resnets       ,   self.attentions							):
											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= attn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											output_states += (hidden_states,)

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= self.downsamplers_a(UpperCAmelCase_							)
											output_states += (hidden_states,)

						return hidden_states, output_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=UpperCAmelCase_       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= resnets

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= FlaxDownsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   List[str]       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   List[Any]=True							):
						SCREAMING_SNAKE_CASE__													= ()

						for resnet in self.resnets:
											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											output_states += (hidden_states,)

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= self.downsamplers_a(UpperCAmelCase_							)
											output_states += (hidden_states,)

						return hidden_states, output_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= []
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if (i == self.num_layers - 1) else self.out_channels
											SCREAMING_SNAKE_CASE__													= self.prev_output_channel if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= FlaxTransformeraDModel(
											    in_channels=self.out_channels       ,   n_heads=self.num_attention_heads       ,   d_head=self.out_channels // self.num_attention_heads       ,   depth=1       ,   use_linear_projection=self.use_linear_projection       ,   only_cross_attention=self.only_cross_attention       ,   use_memory_efficient_attention=self.use_memory_efficient_attention       ,   dtype=self.dtype       ,   )
											attentions.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets
						SCREAMING_SNAKE_CASE__													= attentions

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= FlaxUpsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   str       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Union[str, Any]=True							):
						for resnet, attn in zip(self.resnets       ,   self.attentions							):
											# pop res hidden states
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[-1]
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[:-1]
											SCREAMING_SNAKE_CASE__													= jnp.concatenate((hidden_states, res_hidden_states)       ,   axis=-1							)

											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= attn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= self.upsamplers_a(UpperCAmelCase_							)

						return hidden_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if (i == self.num_layers - 1) else self.out_channels
											SCREAMING_SNAKE_CASE__													= self.prev_output_channel if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= FlaxUpsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   Tuple       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Dict=True							):
						for resnet in self.resnets:
											# pop res hidden states
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[-1]
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[:-1]
											SCREAMING_SNAKE_CASE__													= jnp.concatenate((hidden_states, res_hidden_states)       ,   axis=-1							)

											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= self.upsamplers_a(UpperCAmelCase_							)

						return hidden_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   int				       =1
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   Optional[int]							):
						# there is always at least one resnet
						SCREAMING_SNAKE_CASE__													= [
						    FlaxResnetBlockaD(
						        in_channels=self.in_channels       ,   out_channels=self.in_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
						]

						SCREAMING_SNAKE_CASE__													= []

						for _ in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= FlaxTransformeraDModel(
											    in_channels=self.in_channels       ,   n_heads=self.num_attention_heads       ,   d_head=self.in_channels // self.num_attention_heads       ,   depth=1       ,   use_linear_projection=self.use_linear_projection       ,   use_memory_efficient_attention=self.use_memory_efficient_attention       ,   dtype=self.dtype       ,   )
											attentions.append(UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=self.in_channels       ,   out_channels=self.in_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets
						SCREAMING_SNAKE_CASE__													= attentions





	def __call__(			self							:   str       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   Optional[int]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   Any=True							):
						SCREAMING_SNAKE_CASE__													= self.resnets[0](UpperCAmelCase_       ,   UpperCAmelCase_							)
						for attn, resnet in zip(self.attentions       ,   self.resnets[1:]							):
											SCREAMING_SNAKE_CASE__													= attn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)

						return hidden_states





