
"""simple docstring"""







from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)




@flax.struct.dataclass
class  A__       (	_lowerCamelCase):
		A_						:       jnp.ndarray




@flax_register_to_config
class  A__       (	nn.Module       ,     _lowerCamelCase       ,     _lowerCamelCase):
		A_						:       int				   =	3_2
		A_						:       int				   =	4
		A_						:       int				   =	4
		A_						:       Tuple[str]				   =	(
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "DownBlock2D",
		)
		A_						:       Tuple[str]				   =	("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
		A_						:       Union[bool, Tuple[bool]]				   =	False
		A_						:       Tuple[int]				   =	(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
		A_						:       int				   =	2
		A_						:       Union[int, Tuple[int]]				   =	8
		A_						:       Optional[Union[int, Tuple[int]]]				   =	None
		A_						:       int				   =	1_2_8_0
		A_						:       float				   =	0.0
		A_						:       bool				   =	False
		A_						:       jnp.dtype				   =	jnp.floataa
		A_						:       bool				   =	True
		A_						:       int				   =	0
		A_						:       bool				   =	False

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			# init input tensors
			__lowerCAmelCase			:						Optional[Any]									=      (1, self.in_channels, self.sample_size, self.sample_size)
			__lowerCAmelCase			:						int									=      jnp.zeros(_SCREAMING_SNAKE_CASE       ,		dtype=jnp.floataa )
			__lowerCAmelCase			:						str									=      jnp.ones((1,)       ,		dtype=jnp.intaa )
			__lowerCAmelCase			:						Any									=      jnp.zeros((1, 1, self.cross_attention_dim)       ,		dtype=jnp.floataa )

			__lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      jax.random.split(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Dict									=      {'params': params_rng, 'dropout': dropout_rng}

			return self.init(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )["params"]

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[int]									=      self.block_out_channels
			__lowerCAmelCase			:						List[str]									=      block_out_channels[0] * 4

			if self.num_attention_heads is not None:
				raise ValueError(
				    'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.' )

			# If `num_attention_heads` is not defined (which is the case for most models)
			# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
			# The reason for this behavior is to correct for incorrectly named variables that were introduced
			# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
			# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
			# which is why we correct for the naming here.
			__lowerCAmelCase			:						Dict									=      self.num_attention_heads or self.attention_head_dim

			# input
			__lowerCAmelCase			:						Union[str, Any]									=      nn.Conv(
			    block_out_channels[0]       ,		kernel_size=(3, 3)       ,		strides=(1, 1)       ,		padding=((1, 1), (1, 1))       ,		dtype=self.dtype       ,		)

			# time
			__lowerCAmelCase			:						Union[str, Any]									=      FlaxTimesteps(
			    block_out_channels[0]       ,		flip_sin_to_cos=self.flip_sin_to_cos       ,		freq_shift=self.config.freq_shift )
			__lowerCAmelCase			:						Optional[int]									=      FlaxTimestepEmbedding(_SCREAMING_SNAKE_CASE       ,		dtype=self.dtype )

			__lowerCAmelCase			:						Optional[Any]									=      self.only_cross_attention
			if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Union[str, Any]									=      (only_cross_attention,) * len(self.down_block_types )

			if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Tuple									=      (num_attention_heads,) * len(self.down_block_types )

			# down
			__lowerCAmelCase			:						str									=      []
			__lowerCAmelCase			:						Optional[int]									=      block_out_channels[0]
			for i, down_block_type in enumerate(self.down_block_types ):
				__lowerCAmelCase			:						Optional[Any]									=      output_channel
				__lowerCAmelCase			:						Dict									=      block_out_channels[i]
				__lowerCAmelCase			:						Optional[int]									=      i == len(_SCREAMING_SNAKE_CASE ) - 1

				if down_block_type == "CrossAttnDownBlock2D":
					__lowerCAmelCase			:						Any									=      FlaxCrossAttnDownBlockaD(
					    in_channels=_SCREAMING_SNAKE_CASE       ,		out_channels=_SCREAMING_SNAKE_CASE       ,		dropout=self.dropout       ,		num_layers=self.layers_per_block       ,		num_attention_heads=num_attention_heads[i]       ,		add_downsample=not is_final_block       ,		use_linear_projection=self.use_linear_projection       ,		only_cross_attention=only_cross_attention[i]       ,		use_memory_efficient_attention=self.use_memory_efficient_attention       ,		dtype=self.dtype       ,		)
				else:
					__lowerCAmelCase			:						List[Any]									=      FlaxDownBlockaD(
					    in_channels=_SCREAMING_SNAKE_CASE       ,		out_channels=_SCREAMING_SNAKE_CASE       ,		dropout=self.dropout       ,		num_layers=self.layers_per_block       ,		add_downsample=not is_final_block       ,		dtype=self.dtype       ,		)

				down_blocks.append(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      down_blocks

			# mid
			__lowerCAmelCase			:						List[str]									=      FlaxUNetMidBlockaDCrossAttn(
			    in_channels=block_out_channels[-1]       ,		dropout=self.dropout       ,		num_attention_heads=num_attention_heads[-1]       ,		use_linear_projection=self.use_linear_projection       ,		use_memory_efficient_attention=self.use_memory_efficient_attention       ,		dtype=self.dtype       ,		)

			# up
			__lowerCAmelCase			:						Union[str, Any]									=      []
			__lowerCAmelCase			:						int									=      list(reversed(_SCREAMING_SNAKE_CASE ) )
			__lowerCAmelCase			:						Dict									=      list(reversed(_SCREAMING_SNAKE_CASE ) )
			__lowerCAmelCase			:						Any									=      list(reversed(_SCREAMING_SNAKE_CASE ) )
			__lowerCAmelCase			:						Optional[int]									=      reversed_block_out_channels[0]
			for i, up_block_type in enumerate(self.up_block_types ):
				__lowerCAmelCase			:						Optional[int]									=      output_channel
				__lowerCAmelCase			:						Any									=      reversed_block_out_channels[i]
				__lowerCAmelCase			:						Optional[Any]									=      reversed_block_out_channels[min(i + 1       ,		len(_SCREAMING_SNAKE_CASE ) - 1 )]

				__lowerCAmelCase			:						Optional[int]									=      i == len(_SCREAMING_SNAKE_CASE ) - 1

				if up_block_type == "CrossAttnUpBlock2D":
					__lowerCAmelCase			:						Union[str, Any]									=      FlaxCrossAttnUpBlockaD(
					    in_channels=_SCREAMING_SNAKE_CASE       ,		out_channels=_SCREAMING_SNAKE_CASE       ,		prev_output_channel=_SCREAMING_SNAKE_CASE       ,		num_layers=self.layers_per_block + 1       ,		num_attention_heads=reversed_num_attention_heads[i]       ,		add_upsample=not is_final_block       ,		dropout=self.dropout       ,		use_linear_projection=self.use_linear_projection       ,		only_cross_attention=only_cross_attention[i]       ,		use_memory_efficient_attention=self.use_memory_efficient_attention       ,		dtype=self.dtype       ,		)
				else:
					__lowerCAmelCase			:						Tuple									=      FlaxUpBlockaD(
					    in_channels=_SCREAMING_SNAKE_CASE       ,		out_channels=_SCREAMING_SNAKE_CASE       ,		prev_output_channel=_SCREAMING_SNAKE_CASE       ,		num_layers=self.layers_per_block + 1       ,		add_upsample=not is_final_block       ,		dropout=self.dropout       ,		dtype=self.dtype       ,		)

				up_blocks.append(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						str									=      output_channel
			__lowerCAmelCase			:						int									=      up_blocks

			# out
			__lowerCAmelCase			:						Tuple									=      nn.GroupNorm(num_groups=32       ,		epsilon=1E-5 )
			__lowerCAmelCase			:						Optional[Any]									=      nn.Conv(
			    self.out_channels       ,		kernel_size=(3, 3)       ,		strides=(1, 1)       ,		padding=((1, 1), (1, 1))       ,		dtype=self.dtype       ,		)







		def __call__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE = True       ,		_SCREAMING_SNAKE_CASE = False       ,		):
			# 1. time
			if not isinstance(_SCREAMING_SNAKE_CASE       ,		jnp.ndarray ):
				__lowerCAmelCase			:						Optional[Any]									=      jnp.array([timesteps]       ,		dtype=jnp.intaa )
			elif isinstance(_SCREAMING_SNAKE_CASE       ,		jnp.ndarray ) and len(timesteps.shape ) == 0:
				__lowerCAmelCase			:						Optional[int]									=      timesteps.astype(dtype=jnp.floataa )
				__lowerCAmelCase			:						List[str]									=      jnp.expand_dims(_SCREAMING_SNAKE_CASE       ,		0 )

			__lowerCAmelCase			:						Dict									=      self.time_proj(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      self.time_embedding(_SCREAMING_SNAKE_CASE )

			# 2. pre-process
			__lowerCAmelCase			:						Union[str, Any]									=      jnp.transpose(_SCREAMING_SNAKE_CASE       ,		(0, 2, 3, 1) )
			__lowerCAmelCase			:						Union[str, Any]									=      self.conv_in(_SCREAMING_SNAKE_CASE )

			# 3. down
			__lowerCAmelCase			:						str									=      (sample,)
			for down_block in self.down_blocks:
				if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
					__lowerCAmelCase			,   __lowerCAmelCase			:						Optional[int]									=      down_block(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		deterministic=not train )
				else:
					__lowerCAmelCase			,   __lowerCAmelCase			:						Tuple									=      down_block(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		deterministic=not train )
				down_block_res_samples += res_samples

			if down_block_additional_residuals is not None:
				__lowerCAmelCase			:						str									=      ()

				for down_block_res_sample, down_block_additional_residual in zip(
				    _SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
					down_block_res_sample += down_block_additional_residual
					new_down_block_res_samples += (down_block_res_sample,)

				__lowerCAmelCase			:						Any									=      new_down_block_res_samples

			# 4. mid
			__lowerCAmelCase			:						Union[str, Any]									=      self.mid_block(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		deterministic=not train )

			if mid_block_additional_residual is not None:
				sample += mid_block_additional_residual

			# 5. up
			for up_block in self.up_blocks:
				__lowerCAmelCase			:						Dict									=      down_block_res_samples[-(self.layers_per_block + 1) :]
				__lowerCAmelCase			:						str									=      down_block_res_samples[: -(self.layers_per_block + 1)]
				if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
					__lowerCAmelCase			:						int									=      up_block(
					    _SCREAMING_SNAKE_CASE       ,		temb=_SCREAMING_SNAKE_CASE       ,		encoder_hidden_states=_SCREAMING_SNAKE_CASE       ,		res_hidden_states_tuple=_SCREAMING_SNAKE_CASE       ,		deterministic=not train       ,		)
				else:
					__lowerCAmelCase			:						Dict									=      up_block(_SCREAMING_SNAKE_CASE       ,		temb=_SCREAMING_SNAKE_CASE       ,		res_hidden_states_tuple=_SCREAMING_SNAKE_CASE       ,		deterministic=not train )

        # 6. post-process
			__lowerCAmelCase			:						str									=      self.conv_norm_out(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[Any]									=      nn.silu(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Tuple									=      self.conv_out(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						int									=      jnp.transpose(_SCREAMING_SNAKE_CASE       ,		(0, 3, 1, 2) )

			if not return_dict:
				return (sample,)

			return FlaxUNetaDConditionOutput(sample=_SCREAMING_SNAKE_CASE )





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)


