


"""simple docstring"""



import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline






def       lowerCamelCase					(a_						:Dataset				,				a_						:Dict[str, str])     ->							List[Any]:
       lowercase		:Dict       							=				args.log_outputs
       lowercase		:Any       							=				'''_'''.join(args.dataset.split('''/''') + [args.config, args.split])

       # load metric
       lowercase		:str       							=				load_metric('''wer''')
       lowercase		:Tuple       							=				load_metric('''cer''')

       # compute metrics
       lowercase		:List[Any]       							=				wer.compute(references=result['''target''']				,				predictions=result['''prediction'''])
       lowercase		:Any       							=				cer.compute(references=result['''target''']				,				predictions=result['''prediction'''])

       # print & log results
       lowercase		:str       							=				F"""WER: {wer_result}\nCER: {cer_result}"""
       print(a_)

       with open(F"""{dataset_id}_eval_results.txt"""				,				'''w''') as f:
              f.write(a_)

       # log all results in text file. Possibly interesting for analysis
       if log_outputs is not None:
              lowercase		:Optional[int]       							=				F"""log_{dataset_id}_predictions.txt"""
              lowercase		:Any       							=				F"""log_{dataset_id}_targets.txt"""

              with open(a_				,				'''w''') as p, open(a_				,				'''w''') as t:
                     # mapping function to write output
                     def write_to_file(a_						:Optional[int]				,				a_						:Any):
                            p.write(F"""{i}""" + '''\n''')
                            p.write(batch['''prediction'''] + '''\n''')
                            t.write(F"""{i}""" + '''\n''')
                            t.write(batch['''target'''] + '''\n''')

                     result.map(a_				,				with_indices=a_)






def       lowerCamelCase					(a_						:str)     ->							str:
       lowercase		:int       							=				'''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

       lowercase		:Optional[int]       							=				re.sub(a_				,				''''''				,				text.lower())

       # In addition, we can normalize the target text, e.g. removing new lines characters etc...
       # note that order is important here!
       lowercase		:Union[str, Any]       							=				['''\n\n''', '''\n''', '''   ''', '''  ''']

       for t in token_sequences_to_ignore:
              lowercase		:Optional[int]       							=				''' '''.join(text.split(a_))

       return text






def       lowerCamelCase					(a_						:str)     ->							Optional[Any]:
       # load dataset
       lowercase		:Any       							=				load_dataset(args.dataset				,				args.config				,				split=args.split				,				use_auth_token=a_)

       # for testing: only process the first two examples as a test
       # dataset = dataset.select(range(10))

       # load processor
       lowercase		:int       							=				AutoFeatureExtractor.from_pretrained(args.model_id)
       lowercase		:Optional[int]       							=				feature_extractor.sampling_rate

       # resample audio
       lowercase		:Tuple       							=				dataset.cast_column('''audio'''				,				Audio(sampling_rate=a_))

       # load eval pipeline
       if args.device is None:
              lowercase		:Optional[Any]       							=				0 if torch.cuda.is_available() else -1
       lowercase		:Any       							=				pipeline('''automatic-speech-recognition'''				,				model=args.model_id				,				device=args.device)

       # map function to decode audio
       def map_to_pred(a_						:str):
              lowercase		:Tuple       							=				asr(
                  batch['''audio''']['''array''']				,				chunk_length_s=args.chunk_length_s				,				stride_length_s=args.stride_length_s)

              lowercase		:Tuple       							=				prediction['''text''']
              lowercase		:List[str]       							=				normalize_text(batch['''sentence'''])
              return batch

       # run inference on all examples
       lowercase		:Optional[int]       							=				dataset.map(a_				,				remove_columns=dataset.column_names)

       # compute and log_results
       # do not change function below
       log_results(a_				,				a_)


if __name__ == "__main__":
 UpperCAmelCase      		=			argparse.ArgumentParser()

 parser.add_argument(
     '''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
 )
 parser.add_argument(
     '''--dataset''',
     type=str,
     required=True,
     help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
 )
 parser.add_argument(
     '''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'`  for Common Voice'''
 )
 parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
 parser.add_argument(
     '''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
 )
 parser.add_argument(
     '''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
 )
 parser.add_argument(
     '''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
 )
 parser.add_argument(
     '''--device''',
     type=int,
     default=None,
     help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
 )
 UpperCAmelCase      		=			parser.parse_args()

 main(args)





"""simple docstring"""



import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Tuple        =   PriorTransformer
       __A			:      Any        =   "hidden_states"






       @property
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				4
              lowercase		:List[Any]       							=				8
              lowercase		:Any       							=				7

              lowercase		:Optional[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Union[str, Any]       							=				floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Optional[Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Union[str, Any]       							=				4
              lowercase		:Optional[int]       							=				8
              lowercase		:str       							=				7

              lowercase		:Dict       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:Union[str, Any]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Tuple       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (4, 8)






       @property
       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              return (4, 8)






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				{
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              lowercase		:List[str]       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				PriorTransformer.from_pretrained(
                  '''hf-internal-testing/prior-dummy'''  ,     output_loading_info=snake_case__				)
              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:List[Any]       							=				model(**self.dummy_input				)[0]

              assert hidden_states is not None, "Make sure output is not None"






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:List[Any]       							=				self.prepare_init_args_and_inputs_for_common()

              lowercase		:str       							=				self.model_class(**snake_case__				)
              lowercase		:Optional[Any]       							=				inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowercase		:List[Any]       							=				[*signature.parameters.keys()]

              lowercase		:Optional[Any]       							=				['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]  ,     snake_case__				)







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              lowercase		:List[Any]       							=				model.to(snake_case__				)

              if hasattr(snake_case__  ,     '''set_default_attn_processor'''				):
                     model.set_default_attn_processor()

              lowercase		:List[str]       							=				self.get_dummy_seed_input()

              with torch.no_grad():
                     lowercase		:List[Any]       							=				model(**snake_case__				)[0]

              lowercase		:Dict       							=				output[0, :5].flatten().cpu()
              print(snake_case__				)

              # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
              # the expected output slices are not the same for CPU and GPU.
              lowercase		:str       							=				torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]				)
              self.assertTrue(torch_all_close(snake_case__  ,     snake_case__  ,     rtol=1e-2				)				)







@slow
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    int=1  ,     snake_case__  :    int=7_6_8  ,     snake_case__  :    Optional[Any]=7_7  ,     snake_case__  :    Union[str, Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Any       							=				batch_size
              lowercase		:Dict       							=				embedding_dim
              lowercase		:int       							=				num_embeddings

              lowercase		:int       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[str]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Optional[Any]       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()







       @parameterized.expand(
           [
               # fmt: off
               [1_3, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
               [3_7, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
               # fmt: on
           ]				)
       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Dict  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:str       							=				PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''  ,     subfolder='''prior'''				)
              model.to(snake_case__				)
              lowercase		:Union[str, Any]       							=				self.get_dummy_seed_input(seed=snake_case__				)

              with torch.no_grad():
                     lowercase		:int       							=				model(**snake_case__				)[0]

              assert list(sample.shape				) == [1, 7_6_8]

              lowercase		:List[Any]       							=				sample[0, :8].flatten().cpu()
              print(snake_case__				)
              lowercase		:Union[str, Any]       							=				torch.tensor(snake_case__				)

              assert torch_all_close(snake_case__  ,     snake_case__  ,     atol=1e-3				)

