


"""simple docstring"""






from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

UpperCAmelCase    :     str   						=		logging.get_logger(__name__)





@add_end_docstrings(A				)
class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : Tuple ,       **UpperCamelCase    : Any  ):
												'''simple docstring'''






												super().__init__(**UpperCamelCase  )

												requires_backends(self ,       """vision"""  )
												self.check_model_type(
												    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
												    if self.framework == """tf"""
												    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING  )






					def __call__(       self    : Union[str, Any] ,       UpperCamelCase    : Union[str, List[str], "Image", List["Image"]] ,       **UpperCamelCase    : List[str]  ):
												'''simple docstring'''






												return super().__call__(UpperCamelCase ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Tuple ,       **UpperCamelCase    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					{}
												if "candidate_labels" in kwargs:
																			__UpperCAmelCase   :						Any      							=					kwargs["""candidate_labels"""]
												if "hypothesis_template" in kwargs:
																			__UpperCAmelCase   :						List[Any]      							=					kwargs["""hypothesis_template"""]

												return preprocess_params, {}, {}






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : Optional[int] ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : List[str]="This is a photo of {}."  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					load_image(UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					self.image_processor(images=[image] ,       return_tensors=self.framework  )
												__UpperCAmelCase   :						Union[str, Any]      							=					candidate_labels
												__UpperCAmelCase   :						str      							=					[hypothesis_template.format(UpperCamelCase  ) for x in candidate_labels]
												__UpperCAmelCase   :						str      							=					self.tokenizer(UpperCamelCase ,       return_tensors=self.framework ,       padding=UpperCamelCase  )
												__UpperCAmelCase   :						List[str]      							=					[text_inputs]
												return inputs






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					model_inputs.pop("""candidate_labels"""  )
												__UpperCAmelCase   :						str      							=					model_inputs.pop("""text_inputs"""  )
												if isinstance(text_inputs[0] ,       UpperCamelCase  ):
																			__UpperCAmelCase   :						Dict      							=					text_inputs[0]
												else:
																			# Batching case.
																			__UpperCAmelCase   :						int      							=					text_inputs[0][0]

												__UpperCAmelCase   :						Dict      							=					self.model(**UpperCamelCase ,       **UpperCamelCase  )

												__UpperCAmelCase   :						Dict      							=					{
												    """candidate_labels""": candidate_labels,
												    """logits""": outputs.logits_per_image,
												}
												return model_outputs






					def lowerCamelCase__							(       self    : List[str] ,       UpperCamelCase    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					model_outputs.pop("""candidate_labels"""  )
												__UpperCAmelCase   :						List[str]      							=					model_outputs["""logits"""][0]
												if self.framework == "pt":
																			__UpperCAmelCase   :						Optional[Any]      							=					logits.softmax(dim=-1  ).squeeze(-1  )
																			__UpperCAmelCase   :						Any      							=					probs.tolist()
																			if not isinstance(UpperCamelCase ,       UpperCamelCase  ):
																										__UpperCAmelCase   :						Any      							=					[scores]
												elif self.framework == "tf":
																			__UpperCAmelCase   :						Any      							=					stable_softmax(UpperCamelCase ,       axis=-1  )
																			__UpperCAmelCase   :						Tuple      							=					probs.numpy().tolist()
												else:
																			raise ValueError(f'''Unsupported framework: {self.framework}'''  )

												__UpperCAmelCase   :						int      							=					[
												    {"""score""": score, """label""": candidate_label}
												    for score, candidate_label in sorted(zip(UpperCamelCase ,       UpperCamelCase  ) ,       key=lambda UpperCamelCase  : -x[0]  )
												]
												return result




"""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      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Dict ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												shutil.rmtree(self.tmpdirname  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Any      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												processor.save_pretrained(self.tmpdirname  )
												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor.from_pretrained(self.tmpdirname  )

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






					@slow
					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, 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 ,       )

												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer(bos_token="""(BOS)""" ,       eos_token="""(EOS)"""  )

												__UpperCAmelCase   :						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    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       )

												__UpperCAmelCase   :						List[str]      							=					35
												__UpperCAmelCase   :						Tuple      							=					2
												__UpperCAmelCase   :						Union[str, Any]      							=					8

												__UpperCAmelCase   :						Optional[Any]      							=					{
												    """semantic_prompt""": np.ones(UpperCamelCase  ),
												    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
												    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
												}

												# test providing already loaded voice_preset
												__UpperCAmelCase   :						Dict      							=					processor(text=self.input_string ,       voice_preset=UpperCamelCase  )

												__UpperCAmelCase   :						Union[str, Any]      							=					inputs["""history_prompt"""]
												for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist() ,       processed_voice_preset.get(UpperCamelCase ,       np.array([]  )  ).tolist()  )

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

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

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






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												__UpperCAmelCase   :						List[str]      							=					processor(text=self.input_string  )

												__UpperCAmelCase   :						Tuple      							=					tokenizer(
												    self.input_string ,       padding="""max_length""" ,       max_length=256 ,       add_special_tokens=UpperCamelCase ,       return_attention_mask=UpperCamelCase ,       return_token_type_ids=UpperCamelCase ,       )

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