
'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


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

    from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=3							,		lowerCAmelCase_=32							,		lowerCAmelCase_=3							,		lowerCAmelCase_=10							,		lowerCAmelCase_=[10, 20, 30, 40]							,		lowerCAmelCase_=[1, 1, 2, 1]							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_="relu"							,		lowerCAmelCase_=3							,		lowerCAmelCase_=None							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							parent
       a_      :	int      =							batch_size
       a_      :	Any      =							image_size
       a_      :	Union[str, Any]      =							num_channels
       a_      :	Any      =							embeddings_size
       a_      :	Tuple      =							hidden_sizes
       a_      :	int      =							depths
       a_      :	Any      =							is_training
       a_      :	Tuple      =							use_labels
       a_      :	Union[str, Any]      =							hidden_act
       a_      :	Optional[int]      =							num_labels
       a_      :	Union[str, Any]      =							scope
       a_      :	Any      =							len(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       a_      :	Any      =							self.get_config()

       return config, pixel_values


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return RegNetConfig(
           num_channels=self.num_channels							,		embeddings_size=self.embeddings_size							,		hidden_sizes=self.hidden_sizes							,		depths=self.depths							,		hidden_act=self.hidden_act							,		num_labels=self.num_labels							,		image_size=self.image_size							,		)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							FlaxRegNetModel(config=lowerCAmelCase_ )
       a_      :	List[str]      =							model(lowerCAmelCase_ )

       # Output shape (b, c, h, w)
       self.parent.assertEqual(
           result.last_hidden_state.shape							,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,		)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							self.num_labels
       a_      :	Any      =							FlaxRegNetForImageClassification(config=lowerCAmelCase_ )
       a_      :	Optional[Any]      =							model(lowerCAmelCase_ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels) )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.prepare_config_and_inputs()
       a_      , a_      :	Optional[Any]      =							config_and_inputs
       a_      :	int      =							{"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_flax
class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

     a_    	=					False
     a_    	=					False
     a_    	=					False


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							FlaxRegNetModelTester(self )
       a_      :	Any      =							ConfigTester(self							,		config_class=lowerCAmelCase_							,		has_text_modality=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_ )


     @unittest.skip(reason="""RegNet does not use inputs_embeds""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     @unittest.skip(reason="""RegNet does not support input and output embeddings""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Tuple      =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         a_      :	str      =							model_class(lowerCAmelCase_ )
         a_      :	Any      =							inspect.signature(model.__call__ )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         a_      :	List[str]      =							[*signature.parameters.keys()]

         a_      :	List[str]      =							["""pixel_values"""]
         self.assertListEqual(arg_names[:1]							,		lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       def check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):
         a_      :	List[str]      =							model_class(lowerCAmelCase_ )

         a_      :	List[str]      =							model(**self._prepare_for_class(lowerCAmelCase_							,		lowerCAmelCase_ ) )

         a_      :	List[str]      =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         a_      :	Any      =							self.model_tester.num_stages
         self.assertEqual(len(lowerCAmelCase_ )							,		expected_num_stages + 1 )

       a_      , a_      :	List[str]      =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         a_      :	Optional[int]      =							True
         check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         a_      :	List[Any]      =							True

         check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	str      =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__ ):
           a_      :	Dict      =							self._prepare_for_class(lowerCAmelCase_							,		lowerCAmelCase_ )
           a_      :	int      =							model_class(lowerCAmelCase_ )

           @jax.jit
           def model_jitted(lowerCAmelCase_							,		**lowerCAmelCase_ ):
             return model(pixel_values=lowerCAmelCase_							,		**lowerCAmelCase_ )

           with self.subTest("""JIT Enabled""" ):
             a_      :	Dict      =							model_jitted(**lowerCAmelCase_ ).to_tuple()

           with self.subTest("""JIT Disabled""" ):
             with jax.disable_jit():
               a_      :	Union[str, Any]      =							model_jitted(**lowerCAmelCase_ ).to_tuple()

           self.assertEqual(len(lowerCAmelCase_ )							,		len(lowerCAmelCase_ ) )
           for jitted_output, output in zip(lowerCAmelCase_							,		lowerCAmelCase_ ):
             self.assertEqual(jitted_output.shape							,		output.shape )







def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	Tuple      =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
  return image




@require_flax
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     @cached_property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040""" ) if is_vision_available() else None






     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040""" )

       a_      :	List[str]      =							self.default_image_processor
       a_      :	Dict      =							prepare_img()
       a_      :	str      =							image_processor(images=lowerCAmelCase_							,		return_tensors="""np""" )

       a_      :	int      =							model(**lowerCAmelCase_ )

       # verify the logits
       a_      :	List[str]      =							(1, 10_00)
       self.assertEqual(outputs.logits.shape							,		lowerCAmelCase_ )

       a_      :	int      =							jnp.array([-0.4180, -1.5051, -3.4836] )

       self.assertTrue(jnp.allclose(outputs.logits[0, :3]							,		lowerCAmelCase_							,		atol=1E-4 ) )




'''simple docstring'''

import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							"""ylacombe/bark-small"""
       a_      :	Dict      =							tempfile.mkdtemp()
       a_      :	Union[str, Any]      =							"""en_speaker_1"""
       a_      :	Dict      =							"""This is a test string"""
       a_      :	Optional[int]      =							"""speaker_embeddings_path.json"""
       a_      :	int      =							"""speaker_embeddings"""


     def 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       return AutoTokenizer.from_pretrained(self.checkpoint							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       shutil.rmtree(self.tmpdirname )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.get_tokenizer()

       a_      :	Optional[Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       processor.save_pretrained(self.tmpdirname )
       a_      :	List[str]      =							BarkProcessor.from_pretrained(self.tmpdirname )

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


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		)
       processor.save_pretrained(
           self.tmpdirname							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		speaker_embeddings_directory=self.speaker_embeddings_directory							,		)

       a_      :	List[str]      =							self.get_tokenizer(bos_token="""(BOS)"""							,		eos_token="""(EOS)""" )

       a_      :	Any      =							BarkProcessor.from_pretrained(
           self.tmpdirname							,		self.speaker_embeddings_dict_path							,		bos_token="""(BOS)"""							,		eos_token="""(EOS)"""							,		)

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		)

       a_      :	Dict      =							35
       a_      :	List[Any]      =							2
       a_      :	Optional[int]      =							8

       a_      :	int      =							{
           """semantic_prompt""": np.ones(lowerCAmelCase_ ),
           """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ),
           """fine_prompt""": np.ones((nb_codebooks_total, seq_len) ),
       }

       # test providing already loaded voice_preset
       a_      :	Optional[Any]      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )

       a_      :	Tuple      =							inputs["""history_prompt"""]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()							,		processed_voice_preset.get(lowerCAmelCase_							,		np.array([] ) ).tolist() )

       # test loading voice preset from npz file
       a_      :	Any      =							os.path.join(self.tmpdirname							,		"""file.npz""" )
       np.savez(lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )
       a_      :	List[Any]      =							inputs["""history_prompt"""]

       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()							,		processed_voice_preset.get(lowerCAmelCase_							,		np.array([] ) ).tolist() )

       # test loading voice preset from the hub
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=self.voice_preset )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.get_tokenizer()

       a_      :	Union[str, Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       a_      :	Optional[int]      =							processor(text=self.input_string )

       a_      :	Optional[int]      =							tokenizer(
           self.input_string							,		padding="""max_length"""							,		max_length=2_56							,		add_special_tokens=lowerCAmelCase_							,		return_attention_mask=lowerCAmelCase_							,		return_token_type_ids=lowerCAmelCase_							,		)

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


