






'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCamelCase							=logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase							="\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"



def 							snake_case					(     a_							: str    ,   a_							: Any    ,   a_							: int=8	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   Tuple					    =							height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      UpperCamelCase_				:   List[str]					    =							width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor



def 							snake_case					(     a_							: List[Any]    ,   a_							: str=512    ,   a_							: Optional[Any]=512	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							pil_image.resize((w, h)    ,   resample=Image.BICUBIC    ,   reducing_gap=1	)
      UpperCamelCase_				:   int					    =							np.array(pil_image.convert("""RGB"""	)	)
      UpperCamelCase_				:   Optional[int]					    =							arr.astype(np.floataa	) / 127.5 - 1
      UpperCamelCase_				:   Any					    =							np.transpose(a_    ,   [2, 0, 1]	)
      UpperCamelCase_				:   Tuple					    =							torch.from_numpy(a_	).unsqueeze(0	)
      return image




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     ):
       super().__init__()

       self.register_modules(
           unet=__lowerCAmelCase			,     scheduler=__lowerCAmelCase			,     movq=__lowerCAmelCase			,     )
       UpperCamelCase_				:   str					    =							2 ** (len(self.movq.config.block_out_channels ) - 1)
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       # get the original timestep using init_timestep
       UpperCamelCase_				:   Tuple					    =							min(int(num_inference_steps * strength )			,     __lowerCAmelCase )

       UpperCamelCase_				:   Tuple					    =							max(num_inference_steps - init_timestep			,     0 )
       UpperCamelCase_				:   Optional[int]					    =							self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=None ):
       if not isinstance(__lowerCAmelCase			,     (torch.Tensor, PIL.Image.Image, list) ):
             raise ValueError(
                 F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowerCAmelCase )}" )

       UpperCamelCase_				:   Any					    =							image.to(device=__lowerCAmelCase			,     dtype=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							batch_size * num_images_per_prompt

       if image.shape[1] == 4:
             UpperCamelCase_				:   int					    =							image

       else:
             if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ) and len(__lowerCAmelCase ) != batch_size:
                   raise ValueError(
                       F"You have passed a list of generators of length {len(__lowerCAmelCase )}, but requested an effective batch"
                       F" size of {batch_size}. Make sure the batch size matches the length of the generators." )

             elif isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   UpperCamelCase_				:   Any					    =							[
                       self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(__lowerCAmelCase )
                   ]
                   UpperCamelCase_				:   List[Any]					    =							torch.cat(__lowerCAmelCase			,     dim=0 )
             else:
                   UpperCamelCase_				:   str					    =							self.movq.encode(__lowerCAmelCase ).latent_dist.sample(__lowerCAmelCase )

             UpperCamelCase_				:   Optional[Any]					    =							self.movq.config.scaling_factor * init_latents

       UpperCamelCase_				:   Optional[Any]					    =							torch.cat([init_latents]			,     dim=0 )

       UpperCamelCase_				:   Tuple					    =							init_latents.shape
       UpperCamelCase_				:   Union[str, Any]					    =							randn_tensor(__lowerCAmelCase			,     generator=__lowerCAmelCase			,     device=__lowerCAmelCase			,     dtype=__lowerCAmelCase )

       # get latents
       UpperCamelCase_				:   Tuple					    =							self.scheduler.add_noise(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )

       UpperCamelCase_				:   int					    =							init_latents

       return latents
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=0 ):
       if is_accelerate_available():
             from accelerate import cpu_offload
       else:
             raise ImportError("""Please install accelerate via `pip install accelerate`""" )

       UpperCamelCase_				:   int					    =							torch.device(F"cuda:{gpu_id}" )

       UpperCamelCase_				:   int					    =							[
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                   cpu_offload(__lowerCAmelCase			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=0 ):
       if is_accelerate_available() and is_accelerate_version(""">="""			,     """0.17.0.dev0""" ):
             from accelerate import cpu_offload_with_hook
       else:
             raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )

       UpperCamelCase_				:   str					    =							torch.device(F"cuda:{gpu_id}" )

       if self.device.type != "cpu":
             self.to("""cpu"""			,     silence_dtype_warnings=__lowerCAmelCase )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       UpperCamelCase_				:   List[Any]					    =							None
       for cpu_offloaded_model in [self.unet, self.movq]:
             UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							cpu_offload_with_hook(__lowerCAmelCase			,     __lowerCAmelCase			,     prev_module_hook=__lowerCAmelCase )

       # We'll offload the last model manually.
       UpperCamelCase_				:   List[str]					    =							hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def   _UpperCAmelCase       (							self ):
       if not hasattr(self.unet			,     """_hf_hook""" ):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(__lowerCAmelCase			,     """_hf_hook""" )
                 and hasattr(module._hf_hook			,     """execution_device""" )
                 and module._hf_hook.execution_device is not None
             ):
                   return torch.device(module._hf_hook.execution_device )
       return self.device





 @torch.no_grad()
 @replace_example_docstring(__lowerCAmelCase )
 def __call__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = 5_12			,     __lowerCAmelCase = 5_12			,     __lowerCAmelCase = 1_00			,     __lowerCAmelCase = 4.0			,     __lowerCAmelCase = 0.3			,     __lowerCAmelCase = 1			,     __lowerCAmelCase = None			,     __lowerCAmelCase = "pil"			,     __lowerCAmelCase = True			,     ):
       UpperCamelCase_				:   Optional[Any]					    =							self._execution_device

       UpperCamelCase_				:   Tuple					    =							guidance_scale > 1.0

       if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   List[str]					    =							torch.cat(__lowerCAmelCase			,     dim=0 )
       UpperCamelCase_				:   List[Any]					    =							image_embeds.shape[0]
       if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   List[Any]					    =							torch.cat(__lowerCAmelCase			,     dim=0 )

       if do_classifier_free_guidance:
             UpperCamelCase_				:   Optional[int]					    =							image_embeds.repeat_interleave(__lowerCAmelCase			,     dim=0 )
             UpperCamelCase_				:   str					    =							negative_image_embeds.repeat_interleave(__lowerCAmelCase			,     dim=0 )

       UpperCamelCase_				:   int					    =							torch.cat([negative_image_embeds, image_embeds]			,     dim=0 ).to(dtype=self.unet.dtype			,     device=__lowerCAmelCase )

       if not isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   List[str]					    =							[image]
       if not all(isinstance(__lowerCAmelCase			,     (PIL.Image.Image, torch.Tensor) ) for i in image ):
             raise ValueError(
                 F"Input is in incorrect format: {[type(__lowerCAmelCase ) for i in image]}. Currently, we only support  PIL image and pytorch tensor" )

       UpperCamelCase_				:   Optional[int]					    =							torch.cat([prepare_image(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ) for i in image]			,     dim=0 )
       UpperCamelCase_				:   Optional[Any]					    =							image.to(dtype=image_embeds.dtype			,     device=__lowerCAmelCase )

       UpperCamelCase_				:   Tuple					    =							self.movq.encode(__lowerCAmelCase )["""latents"""]
       UpperCamelCase_				:   Tuple					    =							latents.repeat_interleave(__lowerCAmelCase			,     dim=0 )
       self.scheduler.set_timesteps(__lowerCAmelCase			,     device=__lowerCAmelCase )
       UpperCamelCase_    ,			UpperCamelCase_				:   Any					    =							self.get_timesteps(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							timesteps[:1].repeat(batch_size * num_images_per_prompt )
       UpperCamelCase_    ,			UpperCamelCase_				:   List[Any]					    =							downscale_height_and_width(__lowerCAmelCase			,     __lowerCAmelCase			,     self.movq_scale_factor )
       UpperCamelCase_				:   Union[str, Any]					    =							self.prepare_latents(
           __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     image_embeds.dtype			,     __lowerCAmelCase			,     __lowerCAmelCase )
       for i, t in enumerate(self.progress_bar(__lowerCAmelCase ) ):
             # expand the latents if we are doing classifier free guidance
             UpperCamelCase_				:   str					    =							torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

             UpperCamelCase_				:   Tuple					    =							{"""image_embeds""": image_embeds}
             UpperCamelCase_				:   List[str]					    =							self.unet(
                 sample=__lowerCAmelCase			,     timestep=__lowerCAmelCase			,     encoder_hidden_states=__lowerCAmelCase			,     added_cond_kwargs=__lowerCAmelCase			,     return_dict=__lowerCAmelCase			,     )[0]

             if do_classifier_free_guidance:
                   UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							noise_pred.split(latents.shape[1]			,     dim=1 )
                   UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							noise_pred.chunk(2 )
                   UpperCamelCase_    ,			UpperCamelCase_				:   int					    =							variance_pred.chunk(2 )
                   UpperCamelCase_				:   Tuple					    =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   UpperCamelCase_				:   str					    =							torch.cat([noise_pred, variance_pred_text]			,     dim=1 )

             if not (
                 hasattr(self.scheduler.config			,     """variance_type""" )
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                   UpperCamelCase_    ,			UpperCamelCase_				:   Optional[int]					    =							noise_pred.split(latents.shape[1]			,     dim=1 )

             # compute the previous noisy sample x_t -> x_t-1
             UpperCamelCase_				:   Tuple					    =							self.scheduler.step(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     generator=__lowerCAmelCase			,     )[0]

       # post-processing
       UpperCamelCase_				:   Any					    =							self.movq.decode(__lowerCAmelCase			,     force_not_quantize=__lowerCAmelCase )["""sample"""]

       if output_type not in ["pt", "np", "pil"]:
             raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )

       if output_type in ["np", "pil"]:
             UpperCamelCase_				:   Tuple					    =							image * 0.5 + 0.5
             UpperCamelCase_				:   Optional[Any]					    =							image.clamp(0			,     1 )
             UpperCamelCase_				:   Tuple					    =							image.cpu().permute(0			,     2			,     3			,     1 ).float().numpy()

       if output_type == "pil":
             UpperCamelCase_				:   Dict					    =							self.numpy_to_pil(__lowerCAmelCase )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=__lowerCAmelCase )














'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
      from huggingface_hub import snapshot_download
      from pyctcdecode import BeamSearchDecoderCTC

      from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
      from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
      from transformers import WavaVecaForCTC




@require_pyctcdecode
class     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							"""| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
       UpperCamelCase_				:   int					    =							dict(zip(__lowerCAmelCase			,     range(len(__lowerCAmelCase ) ) ) )

       UpperCamelCase_				:   Tuple					    =							{
           """unk_token""": """<unk>""",
           """bos_token""": """<s>""",
           """eos_token""": """</s>""",
       }
       UpperCamelCase_				:   Optional[Any]					    =							{
           """feature_size""": 1,
           """padding_value""": 0.0,
           """sampling_rate""": 1_60_00,
           """return_attention_mask""": False,
           """do_normalize""": True,
       }

       UpperCamelCase_				:   int					    =							tempfile.mkdtemp()
       UpperCamelCase_				:   Tuple					    =							os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["""vocab_file"""] )
       UpperCamelCase_				:   List[Any]					    =							os.path.join(self.tmpdirname			,     __lowerCAmelCase )
       with open(self.vocab_file			,     """w"""			,     encoding="""utf-8""" ) as fp:
             fp.write(json.dumps(__lowerCAmelCase ) + """\n""" )

       with open(self.feature_extraction_file			,     """w"""			,     encoding="""utf-8""" ) as fp:
             fp.write(json.dumps(__lowerCAmelCase ) + """\n""" )

       # load decoder from hub
       UpperCamelCase_				:   Union[str, Any]					    =							"""hf-internal-testing/ngram-beam-search-decoder"""
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   str					    =							self.add_kwargs_tokens_map.copy()
       kwargs.update(__lowerCAmelCase )
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       shutil.rmtree(self.tmpdirname )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.get_tokenizer()
       UpperCamelCase_				:   str					    =							self.get_feature_extractor()
       UpperCamelCase_				:   Tuple					    =							self.get_decoder()

       UpperCamelCase_				:   int					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       processor.save_pretrained(self.tmpdirname )
       UpperCamelCase_				:   Dict					    =							WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )

       # tokenizer
       self.assertEqual(processor.tokenizer.get_vocab()			,     tokenizer.get_vocab() )
       self.assertIsInstance(processor.tokenizer			,     __lowerCAmelCase )

       # feature extractor
       self.assertEqual(processor.feature_extractor.to_json_string()			,     feature_extractor.to_json_string() )
       self.assertIsInstance(processor.feature_extractor			,     __lowerCAmelCase )

       # decoder
       self.assertEqual(processor.decoder._alphabet.labels			,     decoder._alphabet.labels )
       self.assertEqual(
           processor.decoder.model_container[decoder._model_key]._unigram_set			,     decoder.model_container[decoder._model_key]._unigram_set			,     )
       self.assertIsInstance(processor.decoder			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							WavaVecaProcessorWithLM(
           tokenizer=self.get_tokenizer()			,     feature_extractor=self.get_feature_extractor()			,     decoder=self.get_decoder() )
       processor.save_pretrained(self.tmpdirname )

       # make sure that error is thrown when decoder alphabet doesn't match
       UpperCamelCase_				:   Optional[Any]					    =							WavaVecaProcessorWithLM.from_pretrained(
           self.tmpdirname			,     alpha=5.0			,     beta=3.0			,     score_boundary=-7.0			,     unk_score_offset=3 )

       # decoder
       self.assertEqual(processor.language_model.alpha			,     5.0 )
       self.assertEqual(processor.language_model.beta			,     3.0 )
       self.assertEqual(processor.language_model.score_boundary			,     -7.0 )
       self.assertEqual(processor.language_model.unk_score_offset			,     3 )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.get_tokenizer()
       # add token to trigger raise
       tokenizer.add_tokens(["""xx"""] )
       with self.assertRaisesRegex(__lowerCAmelCase			,     """include""" ):
             WavaVecaProcessorWithLM(
                 tokenizer=__lowerCAmelCase			,     feature_extractor=self.get_feature_extractor()			,     decoder=self.get_decoder() )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							self.get_feature_extractor()
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizer()
       UpperCamelCase_				:   Any					    =							self.get_decoder()

       UpperCamelCase_				:   List[Any]					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   List[Any]					    =							floats_list((3, 10_00) )

       UpperCamelCase_				:   Tuple					    =							feature_extractor(__lowerCAmelCase			,     return_tensors="""np""" )
       UpperCamelCase_				:   str					    =							processor(__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   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							self.get_feature_extractor()
       UpperCamelCase_				:   List[Any]					    =							self.get_tokenizer()
       UpperCamelCase_				:   List[Any]					    =							self.get_decoder()

       UpperCamelCase_				:   Optional[Any]					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							"""This is a test string"""

       UpperCamelCase_				:   Optional[Any]					    =							processor(text=__lowerCAmelCase )

       UpperCamelCase_				:   int					    =							tokenizer(__lowerCAmelCase )

       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]			,     encoded_processor[key] )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=(2, 10, 16)			,     __lowerCAmelCase=77 ):
       np.random.seed(__lowerCAmelCase )
       return np.random.rand(*__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.get_feature_extractor()
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizer()
       UpperCamelCase_				:   Optional[int]					    =							self.get_decoder()

       UpperCamelCase_				:   int					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							self._get_dummy_logits(shape=(10, 16)			,     seed=13 )

       UpperCamelCase_				:   Any					    =							processor.decode(__lowerCAmelCase )

       UpperCamelCase_				:   Any					    =							decoder.decode_beams(__lowerCAmelCase )[0]

       self.assertEqual(decoded_decoder[0]			,     decoded_processor.text )
       self.assertEqual("""</s> <s> </s>"""			,     decoded_processor.text )
       self.assertEqual(decoded_decoder[-2]			,     decoded_processor.logit_score )
       self.assertEqual(decoded_decoder[-1]			,     decoded_processor.lm_score )
 @parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.get_feature_extractor()
       UpperCamelCase_				:   str					    =							self.get_tokenizer()
       UpperCamelCase_				:   List[Any]					    =							self.get_decoder()

       UpperCamelCase_				:   Optional[Any]					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							self._get_dummy_logits()

       # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
       #       otherwise, the LM won't be available to the pool's sub-processes.
       # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
       if pool_context is None:
             UpperCamelCase_				:   List[Any]					    =							processor.batch_decode(__lowerCAmelCase )
       else:
             with get_context(__lowerCAmelCase ).Pool() as pool:
                   UpperCamelCase_				:   Any					    =							processor.batch_decode(__lowerCAmelCase			,     __lowerCAmelCase )

       UpperCamelCase_				:   Tuple					    =							list(__lowerCAmelCase )

       with get_context("""fork""" ).Pool() as p:
             UpperCamelCase_				:   Optional[int]					    =							decoder.decode_beams_batch(__lowerCAmelCase			,     __lowerCAmelCase )

       UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Union[str, Any]					    =							[], [], []
       for beams in decoded_beams:
             texts_decoder.append(beams[0][0] )
             logit_scores_decoder.append(beams[0][-2] )
             lm_scores_decoder.append(beams[0][-1] )

       self.assertListEqual(__lowerCAmelCase			,     decoded_processor.text )
       self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]			,     decoded_processor.text )
       self.assertListEqual(__lowerCAmelCase			,     decoded_processor.logit_score )
       self.assertListEqual(__lowerCAmelCase			,     decoded_processor.lm_score )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							self.get_feature_extractor()
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizer()
       UpperCamelCase_				:   Tuple					    =							self.get_decoder()

       UpperCamelCase_				:   Optional[Any]					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							self._get_dummy_logits()

       UpperCamelCase_				:   Dict					    =							15
       UpperCamelCase_				:   str					    =							-20.0
       UpperCamelCase_				:   Dict					    =							-4.0

       UpperCamelCase_				:   Union[str, Any]					    =							processor.batch_decode(
           __lowerCAmelCase			,     beam_width=__lowerCAmelCase			,     beam_prune_logp=__lowerCAmelCase			,     token_min_logp=__lowerCAmelCase			,     )
       UpperCamelCase_				:   Any					    =							decoded_processor_out.text

       UpperCamelCase_				:   Tuple					    =							list(__lowerCAmelCase )

       with get_context("""fork""" ).Pool() as pool:
             UpperCamelCase_				:   str					    =							decoder.decode_beams_batch(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     beam_width=__lowerCAmelCase			,     beam_prune_logp=__lowerCAmelCase			,     token_min_logp=__lowerCAmelCase			,     )

       UpperCamelCase_				:   str					    =							[d[0][0] for d in decoded_decoder_out]
       UpperCamelCase_				:   List[str]					    =							[d[0][2] for d in decoded_decoder_out]
       UpperCamelCase_				:   Union[str, Any]					    =							[d[0][3] for d in decoded_decoder_out]

       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
       self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""]			,     __lowerCAmelCase )

       self.assertTrue(np.array_equal(__lowerCAmelCase			,     decoded_processor_out.logit_score ) )
       self.assertTrue(np.allclose([-20.0_54, -18.4_47]			,     __lowerCAmelCase			,     atol=1E-3 ) )

       self.assertTrue(np.array_equal(__lowerCAmelCase			,     decoded_processor_out.lm_score ) )
       self.assertTrue(np.allclose([-15.5_54, -13.94_74]			,     __lowerCAmelCase			,     atol=1E-3 ) )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.get_feature_extractor()
       UpperCamelCase_				:   Union[str, Any]					    =							self.get_tokenizer()
       UpperCamelCase_				:   int					    =							self.get_decoder()

       UpperCamelCase_				:   Dict					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   str					    =							self._get_dummy_logits()

       UpperCamelCase_				:   Optional[int]					    =							2.0
       UpperCamelCase_				:   List[str]					    =							5.0
       UpperCamelCase_				:   Optional[Any]					    =							-20.0
       UpperCamelCase_				:   Optional[Any]					    =							True

       UpperCamelCase_				:   Union[str, Any]					    =							processor.batch_decode(
           __lowerCAmelCase			,     alpha=__lowerCAmelCase			,     beta=__lowerCAmelCase			,     unk_score_offset=__lowerCAmelCase			,     lm_score_boundary=__lowerCAmelCase			,     )
       UpperCamelCase_				:   List[str]					    =							decoded_processor_out.text

       UpperCamelCase_				:   List[str]					    =							list(__lowerCAmelCase )
       decoder.reset_params(
           alpha=__lowerCAmelCase			,     beta=__lowerCAmelCase			,     unk_score_offset=__lowerCAmelCase			,     lm_score_boundary=__lowerCAmelCase			,     )

       with get_context("""fork""" ).Pool() as pool:
             UpperCamelCase_				:   int					    =							decoder.decode_beams_batch(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     )

       UpperCamelCase_				:   Any					    =							[d[0][0] for d in decoded_decoder_out]

       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
       self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]			,     __lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							processor.decoder.model_container[processor.decoder._model_key]
       self.assertEqual(lm_model.alpha			,     2.0 )
       self.assertEqual(lm_model.beta			,     5.0 )
       self.assertEqual(lm_model.unk_score_offset			,     -20.0 )
       self.assertEqual(lm_model.score_boundary			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )

       UpperCamelCase_				:   Optional[int]					    =							processor.decoder.model_container[processor.decoder._model_key]
       UpperCamelCase_				:   int					    =							Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()

       UpperCamelCase_				:   Any					    =							os.listdir(__lowerCAmelCase )
       UpperCamelCase_				:   Optional[Any]					    =							["""alphabet.json""", """language_model"""]

       downloaded_decoder_files.sort()
       expected_decoder_files.sort()

       # test that only decoder relevant files from
       # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
       # are downloaded and none of the rest (e.g. README.md, ...)
       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							snapshot_download("""hf-internal-testing/processor_with_lm""" )

       UpperCamelCase_				:   Union[str, Any]					    =							WavaVecaProcessorWithLM.from_pretrained(__lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							processor.decoder.model_container[processor.decoder._model_key]
       UpperCamelCase_				:   Tuple					    =							Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()

       UpperCamelCase_				:   Union[str, Any]					    =							os.listdir(__lowerCAmelCase )
       UpperCamelCase_				:   str					    =							os.listdir(__lowerCAmelCase )

       local_decoder_files.sort()
       expected_decoder_files.sort()

       # test that both decoder form hub and local files in cache are the same
       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Any					    =							WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
       UpperCamelCase_				:   Tuple					    =							AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )

       UpperCamelCase_				:   Dict					    =							floats_list((3, 10_00) )

       UpperCamelCase_				:   List[Any]					    =							processor_wavaveca(__lowerCAmelCase			,     return_tensors="""np""" )
       UpperCamelCase_				:   Tuple					    =							processor_auto(__lowerCAmelCase			,     return_tensors="""np""" )

       for key in input_wavaveca.keys():
             self.assertAlmostEqual(input_wavaveca[key].sum()			,     input_auto[key].sum()			,     delta=1E-2 )

       UpperCamelCase_				:   Optional[int]					    =							self._get_dummy_logits()

       UpperCamelCase_				:   Dict					    =							processor_wavaveca.batch_decode(__lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							processor_auto.batch_decode(__lowerCAmelCase )

       self.assertListEqual(decoded_wavaveca.text			,     decoded_auto.text )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							self.get_feature_extractor()
       UpperCamelCase_				:   int					    =							self.get_tokenizer()
       UpperCamelCase_				:   List[Any]					    =							self.get_decoder()

       UpperCamelCase_				:   List[str]					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       self.assertListEqual(
           processor.model_input_names			,     feature_extractor.model_input_names			,     msg="""`processor` and `feature_extractor` model input names do not match"""			,     )
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							[d[key] for d in offsets]
       return retrieved_list
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
       UpperCamelCase_				:   int					    =							self._get_dummy_logits()[0]

       UpperCamelCase_				:   List[Any]					    =							processor.decode(__lowerCAmelCase			,     output_word_offsets=__lowerCAmelCase )
       # check Wav2Vec2CTCTokenizerOutput keys for word
       self.assertEqual(len(outputs.keys() )			,     4 )
       self.assertTrue("""text""" in outputs )
       self.assertTrue("""word_offsets""" in outputs )
       self.assertTrue(isinstance(__lowerCAmelCase			,     __lowerCAmelCase ) )

       self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""]			,     """word""" ) )			,     outputs.text )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]			,     """word""" )			,     ["""<s>""", """<s>""", """</s>"""] )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]			,     """start_offset""" )			,     [0, 2, 4] )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]			,     """end_offset""" )			,     [1, 3, 5] )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Any					    =							WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
       UpperCamelCase_				:   Union[str, Any]					    =							self._get_dummy_logits()

       UpperCamelCase_				:   Dict					    =							processor.batch_decode(__lowerCAmelCase			,     output_word_offsets=__lowerCAmelCase )

       # check Wav2Vec2CTCTokenizerOutput keys for word
       self.assertEqual(len(outputs.keys() )			,     4 )
       self.assertTrue("""text""" in outputs )
       self.assertTrue("""word_offsets""" in outputs )
       self.assertTrue(isinstance(__lowerCAmelCase			,     __lowerCAmelCase ) )

       self.assertListEqual(
           [""" """.join(self.get_from_offsets(__lowerCAmelCase			,     """word""" ) ) for o in outputs["""word_offsets"""]]			,     outputs.text )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]			,     """word""" )			,     ["""<s>""", """<s>""", """</s>"""] )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]			,     """start_offset""" )			,     [0, 2, 4] )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]			,     """end_offset""" )			,     [1, 3, 5] )





 @slow
 @require_torch
 @require_torchaudio
 def   _UpperCAmelCase       (							self ):
       import torch

       UpperCamelCase_				:   str					    =							load_dataset("""common_voice"""			,     """en"""			,     split="""train"""			,     streaming=__lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							ds.cast_column("""audio"""			,     datasets.Audio(sampling_rate=1_60_00 ) )
       UpperCamelCase_				:   Union[str, Any]					    =							iter(__lowerCAmelCase )
       UpperCamelCase_				:   int					    =							next(__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
       UpperCamelCase_				:   Tuple					    =							WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )

       # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
       UpperCamelCase_				:   Dict					    =							processor(sample["""audio"""]["""array"""]			,     return_tensors="""pt""" ).input_values

       with torch.no_grad():
             UpperCamelCase_				:   List[Any]					    =							model(__lowerCAmelCase ).logits.cpu().numpy()

       UpperCamelCase_				:   Tuple					    =							processor.decode(logits[0]			,     output_word_offsets=__lowerCAmelCase )

       UpperCamelCase_				:   List[Any]					    =							model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
       UpperCamelCase_				:   Dict					    =							[
           {
               """start_time""": d["""start_offset"""] * time_offset,
               """end_time""": d["""end_offset"""] * time_offset,
               """word""": d["""word"""],
           }
           for d in output["""word_offsets"""]
       ]

       UpperCamelCase_				:   Optional[Any]					    =							"""WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""

       # output words
       self.assertEqual(""" """.join(self.get_from_offsets(__lowerCAmelCase			,     """word""" ) )			,     __lowerCAmelCase )
       self.assertEqual(""" """.join(self.get_from_offsets(__lowerCAmelCase			,     """word""" ) )			,     output.text )

       # output times
       UpperCamelCase_				:   str					    =							torch.tensor(self.get_from_offsets(__lowerCAmelCase			,     """start_time""" ) )
       UpperCamelCase_				:   Union[str, Any]					    =							torch.tensor(self.get_from_offsets(__lowerCAmelCase			,     """end_time""" ) )

       # fmt: off
       UpperCamelCase_				:   Union[str, Any]					    =							torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99] )
       UpperCamelCase_				:   Union[str, Any]					    =							torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94] )
       # fmt: on

       self.assertTrue(torch.allclose(__lowerCAmelCase			,     __lowerCAmelCase			,     atol=0.01 ) )
       self.assertTrue(torch.allclose(__lowerCAmelCase			,     __lowerCAmelCase			,     atol=0.01 ) )






