





'''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       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       _snake_case     		=				'''ylacombe/bark-small'''
       _snake_case     		=				tempfile.mkdtemp()
       _snake_case     		=				'''en_speaker_1'''
       _snake_case     		=				'''This is a test string'''
       _snake_case     		=				'''speaker_embeddings_path.json'''
       _snake_case     		=				'''speaker_embeddings'''

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return AutoTokenizer.from_pretrained(self.checkpoint						,   **UpperCamelCase__	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				BarkProcessor(tokenizer=UpperCamelCase__	)

       processor.save_pretrained(self.tmpdirname	)
       _snake_case     		=				BarkProcessor.from_pretrained(self.tmpdirname	)

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

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				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						,   )

       _snake_case     		=				self.get_tokenizer(bos_token="(BOS)"						,   eos_token="(EOS)"	)

       _snake_case     		=				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 	UpperCamelCase(					self	):
       _snake_case     		=				BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint						,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path						,   )

       _snake_case     		=				35
       _snake_case     		=				2
       _snake_case     		=				8

       _snake_case     		=				{
           '''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
       _snake_case     		=				processor(text=self.input_string						,   voice_preset=UpperCamelCase__	)

       _snake_case     		=				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
       _snake_case     		=				os.path.join(self.tmpdirname						,   "file.npz"	)
       np.savez(UpperCamelCase__						,   **UpperCamelCase__	)
       _snake_case     		=				processor(text=self.input_string						,   voice_preset=UpperCamelCase__	)
       _snake_case     		=				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
       _snake_case     		=				processor(text=self.input_string						,   voice_preset=self.voice_preset	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				BarkProcessor(tokenizer=UpperCamelCase__	)

       _snake_case     		=				processor(text=self.input_string	)

       _snake_case     		=				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()	)









'''simple docstring'''



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_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__magic_name__   :						Optional[int]    =			logging.get_logger(__name__)


@add_end_docstrings(__UpperCamelCase			)
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def __init__(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       super().__init__(*lowerCamelCase						,   **lowerCamelCase	)
       requires_backends(self						,   "vision"	)
       self.check_model_type(
           TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING	)

     def 	UpperCamelCase(					self						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase=None	):
       _snake_case     		=				{}
       _snake_case     		=				{}

       if prompt is not None:
         _snake_case     		=				prompt

       if generate_kwargs is not None:
         _snake_case     		=				generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _snake_case     		=				{}
         if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
           raise ValueError(
               "'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
               " please use only one"	)
         _snake_case     		=				max_new_tokens
       return preprocess_params, forward_kwargs, {}

     def __call__(					self						,   lowerCamelCase						,   **lowerCamelCase	):
       return super().__call__(lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       _snake_case     		=				load_image(lowerCamelCase	)

       if prompt is not None:
         if not isinstance(lowerCamelCase						,   lowerCamelCase	):
           raise ValueError(
               F'''Received an invalid text input, got - {type(lowerCamelCase	)} - but expected a single string. '''
               "Note also that one single text can be provided for conditional image to text generation."	)

         _snake_case     		=				self.model.config.model_type

         if model_type == "git":
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)
           _snake_case     		=				self.tokenizer(text=lowerCamelCase						,   add_special_tokens=lowerCamelCase	).input_ids
           _snake_case     		=				[self.tokenizer.cls_token_id] + input_ids
           _snake_case     		=				torch.tensor(lowerCamelCase	).unsqueeze(0	)
           model_inputs.update({"input_ids": input_ids}	)

         elif model_type == "pix2struct":
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   header_text=lowerCamelCase						,   return_tensors=self.framework	)

         elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)
           _snake_case     		=				self.tokenizer(lowerCamelCase						,   return_tensors=self.framework	)
           model_inputs.update(lowerCamelCase	)

         else:
           raise ValueError(F'''Model type {model_type} does not support conditional text generation'''	)

       else:
         _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)

       if self.model.config.model_type == "git" and prompt is None:
         _snake_case     		=				None

       return model_inputs

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
       # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
       if (
           "input_ids" in model_inputs
           and isinstance(model_inputs["input_ids"]						,   lowerCamelCase	)
           and all(x is None for x in model_inputs["input_ids"]	)
       ):
         _snake_case     		=				None

       if generate_kwargs is None:
         _snake_case     		=				{}
       # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
       #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
       #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
       #  in the `_prepare_model_inputs` method.
       _snake_case     		=				model_inputs.pop(self.model.main_input_name	)
       _snake_case     		=				self.model.generate(lowerCamelCase						,   **lowerCamelCase						,   **lowerCamelCase	)
       return model_outputs






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				[]
       for output_ids in model_outputs:
         _snake_case     		=				{
             "generated_text": self.tokenizer.decode(
                 lowerCamelCase						,   skip_special_tokens=lowerCamelCase						,   )
         }
         records.append(lowerCamelCase	)
       return records


