




from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin


@dataclass
class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      42


class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__     ):

    '''simple docstring'''






    @register_to_config
    def __init__(			self      :	Dict					, UpperCAmelCase__      :	int = 32					, UpperCAmelCase__      :	int = 64					, UpperCAmelCase__      :	int = 20					, UpperCAmelCase__      :	int = 768					, UpperCAmelCase__      :	str=77					, UpperCAmelCase__      :	Optional[int]=4					, UpperCAmelCase__      :	float = 0.0					, UpperCAmelCase__      :	str = "silu"					, UpperCAmelCase__      :	Optional[str] = None					, UpperCAmelCase__      :	Optional[str] = None					, UpperCAmelCase__      :	Optional[str] = "linear"					, UpperCAmelCase__      :	Optional[str] = "prd"					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	Optional[int] = None					, )						->List[str]:





           '''simple docstring'''

           super().__init__()
           A__       =    num_attention_heads
           A__       =    attention_head_dim
           A__       =    num_attention_heads * attention_head_dim
           A__       =    additional_embeddings

           A__       =    time_embed_dim or inner_dim
           A__       =    embedding_proj_dim or embedding_dim
           A__       =    clip_embed_dim or embedding_dim

           A__       =    Timesteps(UpperCAmelCase__					, UpperCAmelCase__					, 0)
           A__       =    TimestepEmbedding(UpperCAmelCase__					, UpperCAmelCase__					, out_dim=UpperCAmelCase__					, act_fn=UpperCAmelCase__)

           A__       =    nn.Linear(UpperCAmelCase__					, UpperCAmelCase__)

           if embedding_proj_norm_type is None:
                  A__       =    None
           elif embedding_proj_norm_type == "layer":
                  A__       =    nn.LayerNorm(UpperCAmelCase__)
           else:
                  raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}""")

           A__       =    nn.Linear(UpperCAmelCase__					, UpperCAmelCase__)

           if encoder_hid_proj_type is None:
                  A__       =    None
           elif encoder_hid_proj_type == "linear":
                  A__       =    nn.Linear(UpperCAmelCase__					, UpperCAmelCase__)
           else:
                  raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}""")

           A__       =    nn.Parameter(torch.zeros(1					, num_embeddings + additional_embeddings					, UpperCAmelCase__))

           if added_emb_type == "prd":
                  A__       =    nn.Parameter(torch.zeros(1					, 1					, UpperCAmelCase__))
           elif added_emb_type is None:
                  A__       =    None
           else:
                  raise ValueError(
                      f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.""")

           A__       =    nn.ModuleList(
               [
                   BasicTransformerBlock(
                       UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, dropout=UpperCAmelCase__					, activation_fn='''gelu'''					, attention_bias=UpperCAmelCase__					, )
                   for d in range(UpperCAmelCase__)
               ])

           if norm_in_type == "layer":
                  A__       =    nn.LayerNorm(UpperCAmelCase__)
           elif norm_in_type is None:
                  A__       =    None
           else:
                  raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}.""")

           A__       =    nn.LayerNorm(UpperCAmelCase__)

           A__       =    nn.Linear(UpperCAmelCase__					, UpperCAmelCase__)

           A__       =    torch.full(
               [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]					, -10000.0)
           causal_attention_mask.triu_(1)
           A__       =    causal_attention_mask[None, ...]
           self.register_buffer('''causal_attention_mask'''					, UpperCAmelCase__					, persistent=UpperCAmelCase__)

           A__       =    nn.Parameter(torch.zeros(1					, UpperCAmelCase__))
           A__       =    nn.Parameter(torch.zeros(1					, UpperCAmelCase__))


    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Dict[str, AttentionProcessor]:





           '''simple docstring'''

           A__       =    {}

           def fn_recursive_add_processors(UpperCAmelCase__      :	str					, UpperCAmelCase__      :	torch.nn.Module					, UpperCAmelCase__      :	Dict[str, AttentionProcessor]):
                  if hasattr(UpperCAmelCase__					, '''set_processor'''):
                         A__       =    module.processor

                  for sub_name, child in module.named_children():
                         fn_recursive_add_processors(f"""{name}.{sub_name}"""					, UpperCAmelCase__					, UpperCAmelCase__)

                  return processors

           for name, module in self.named_children():
                  fn_recursive_add_processors(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__)

           return processors


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	Union[AttentionProcessor, Dict[str, AttentionProcessor]])						->Optional[Any]:





           '''simple docstring'''

           A__       =    len(self.attn_processors.keys())

           if isinstance(UpperCAmelCase__					, UpperCAmelCase__) and len(UpperCAmelCase__) != count:
                  raise ValueError(
                      f"""A dict of processors was passed, but the number of processors {len(UpperCAmelCase__)} does not match the"""
                      f""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""")

           def fn_recursive_attn_processor(UpperCAmelCase__      :	str					, UpperCAmelCase__      :	torch.nn.Module					, UpperCAmelCase__      :	List[str]):
                  if hasattr(UpperCAmelCase__					, '''set_processor'''):
                         if not isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                                module.set_processor(UpperCAmelCase__)
                         else:
                                module.set_processor(processor.pop(f"""{name}.processor"""))

                  for sub_name, child in module.named_children():
                         fn_recursive_attn_processor(f"""{name}.{sub_name}"""					, UpperCAmelCase__					, UpperCAmelCase__)

           for name, module in self.named_children():
                  fn_recursive_attn_processor(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Dict:





           '''simple docstring'''

           self.set_attn_processor(AttnProcessor())


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	List[Any]					, UpperCAmelCase__      :	Union[torch.Tensor, float, int]					, UpperCAmelCase__      :	torch.FloatTensor					, UpperCAmelCase__      :	Optional[torch.FloatTensor] = None					, UpperCAmelCase__      :	Optional[torch.BoolTensor] = None					, UpperCAmelCase__      :	bool = True					, )						->Tuple:





           '''simple docstring'''

           A__       =    hidden_states.shape[0]

           A__       =    timestep
           if not torch.is_tensor(UpperCAmelCase__):
                  A__       =    torch.tensor([timesteps]					, dtype=torch.long					, device=hidden_states.device)
           elif torch.is_tensor(UpperCAmelCase__) and len(timesteps.shape) == 0:
                  A__       =    timesteps[None].to(hidden_states.device)

           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           A__       =    timesteps * torch.ones(UpperCAmelCase__					, dtype=timesteps.dtype					, device=timesteps.device)

           A__       =    self.time_proj(UpperCAmelCase__)

           # timesteps does not contain any weights and will always return f32 tensors
           # but time_embedding might be fp16, so we need to cast here.
           A__       =    timesteps_projected.to(dtype=self.dtype)
           A__       =    self.time_embedding(UpperCAmelCase__)

           if self.embedding_proj_norm is not None:
                  A__       =    self.embedding_proj_norm(UpperCAmelCase__)

           A__       =    self.embedding_proj(UpperCAmelCase__)
           if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
                  A__       =    self.encoder_hidden_states_proj(UpperCAmelCase__)
           elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
                  raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set''')

           A__       =    self.proj_in(UpperCAmelCase__)

           A__       =    self.positional_embedding.to(hidden_states.dtype)

           A__       =    []
           A__       =    0

           if encoder_hidden_states is not None:
                  additional_embeds.append(UpperCAmelCase__)
                  additional_embeddings_len += encoder_hidden_states.shape[1]

           if len(proj_embeddings.shape) == 2:
                  A__       =    proj_embeddings[:, None, :]

           if len(hidden_states.shape) == 2:
                  A__       =    hidden_states[:, None, :]

           A__       =    additional_embeds + [
               proj_embeddings,
               time_embeddings[:, None, :],
               hidden_states,
           ]

           if self.prd_embedding is not None:
                  A__       =    self.prd_embedding.to(hidden_states.dtype).expand(UpperCAmelCase__					, -1					, -1)
                  additional_embeds.append(UpperCAmelCase__)

           A__       =    torch.cat(
               UpperCAmelCase__					, dim=1					, )

           # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
           A__       =    additional_embeddings_len + proj_embeddings.shape[1] + 1
           if positional_embeddings.shape[1] < hidden_states.shape[1]:
                  A__       =    F.pad(
                      UpperCAmelCase__					, (
                          0,
                          0,
                          additional_embeddings_len,
                          self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                      )					, value=0.0					, )

           A__       =    hidden_states + positional_embeddings

           if attention_mask is not None:
                  A__       =    (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
                  A__       =    F.pad(UpperCAmelCase__					, (0, self.additional_embeddings)					, value=0.0)
                  A__       =    (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
                  A__       =    attention_mask.repeat_interleave(self.config.num_attention_heads					, dim=0)

           if self.norm_in is not None:
                  A__       =    self.norm_in(UpperCAmelCase__)

           for block in self.transformer_blocks:
                  A__       =    block(UpperCAmelCase__					, attention_mask=UpperCAmelCase__)

           A__       =    self.norm_out(UpperCAmelCase__)

           if self.prd_embedding is not None:
                  A__       =    hidden_states[:, -1]
           else:
                  A__       =    hidden_states[:, additional_embeddings_len:]

           A__       =    self.proj_to_clip_embeddings(UpperCAmelCase__)

           if not return_dict:
                  return (predicted_image_embedding,)

           return PriorTransformerOutput(predicted_image_embedding=UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	Any)						->Dict:





           '''simple docstring'''

           A__       =    (prior_latents * self.clip_std) + self.clip_mean
           return prior_latents








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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    args.log_outputs
       A__       =    '''_'''.join(args.dataset.split('''/'''						) + [args.config, args.split]						)

       # load metric
       A__       =    load_metric('''wer'''						)
       A__       =    load_metric('''cer'''						)

       # compute metrics
       A__       =    wer.compute(references=result['''target'''] ,				predictions=result['''prediction''']						)
       A__       =    cer.compute(references=result['''target'''] ,				predictions=result['''prediction''']						)

       # print & log results
       A__       =    f"""WER: {wer_result}\nCER: {cer_result}"""
       print(lowercase_						)

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

       # log all results in text file. Possibly interesting for analysis
       if log_outputs is not None:
              A__       =    f"""log_{dataset_id}_predictions.txt"""
              A__       =    f"""log_{dataset_id}_targets.txt"""

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

                     result.map(lowercase_ ,				with_indices=lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

       A__       =    re.sub(lowercase_ ,				'''''' ,				text.lower()						)

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

       for t in token_sequences_to_ignore:
              A__       =    ''' '''.join(text.split(lowercase_						)						)

       return text







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       A__       =    load_dataset(args.dataset ,				args.config ,				split=args.split ,				use_auth_token=lowercase_						)

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

       # load processor
       A__       =    AutoFeatureExtractor.from_pretrained(args.model_id						)
       A__       =    feature_extractor.sampling_rate

       # resample audio
       A__       =    dataset.cast_column('''audio''' ,				Audio(sampling_rate=lowercase_						)						)

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

       # map function to decode audio
       def map_to_pred(lowercase_						):
              A__       =    asr(
                  batch['''audio''']['''array'''] ,				chunk_length_s=args.chunk_length_s ,				stride_length_s=args.stride_length_s						)

              A__       =    prediction['''text''']
              A__       =    normalize_text(batch['''sentence''']						)
              return batch

       # run inference on all examples
       A__       =    dataset.map(lowercase_ ,				remove_columns=dataset.column_names						)

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


if __name__ == "__main__":
  _lowerCamelCase	:		List[Any]							       =   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.""",
  )
  _lowerCamelCase	:		str							       =   parser.parse_args()

  main(args)


