

"""simple docstring"""


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				int       				=  """blenderbot-small"""
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  ["""past_key_values"""]
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

				def __init__(     self							:    Optional[int]					,  UpperCamelCase__							:    str=5_0_2_6_5					,  UpperCamelCase__							:    Optional[int]=5_1_2					,  UpperCamelCase__							:    Any=8					,  UpperCamelCase__							:    Any=2_0_4_8					,  UpperCamelCase__							:    Union[str, Any]=1_6					,  UpperCamelCase__							:    Tuple=8					,  UpperCamelCase__							:    List[str]=2_0_4_8					,  UpperCamelCase__							:    Tuple=1_6					,  UpperCamelCase__							:    int=0.0					,  UpperCamelCase__							:    Optional[Any]=0.0					,  UpperCamelCase__							:    Any=True					,  UpperCamelCase__							:    Dict=True					,  UpperCamelCase__							:    str="gelu"					,  UpperCamelCase__							:    Union[str, Any]=5_1_2					,  UpperCamelCase__							:    str=0.1					,  UpperCamelCase__							:    Optional[Any]=0.0					,  UpperCamelCase__							:    List[str]=0.0					,  UpperCamelCase__							:    int=0.02					,  UpperCamelCase__							:    Dict=1					,  UpperCamelCase__							:    Dict=False					,  UpperCamelCase__							:    Optional[Any]=0					,  UpperCamelCase__							:    int=1					,  UpperCamelCase__							:    Any=2					,  UpperCamelCase__							:    str=2					,  **UpperCamelCase__							:    Tuple					,  )->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      vocab_size
						__lowerCAmelCase:	Optional[Any]   				=      max_position_embeddings
						__lowerCAmelCase:	str   				=      d_model
						__lowerCAmelCase:	Any   				=      encoder_ffn_dim
						__lowerCAmelCase:	Tuple   				=      encoder_layers
						__lowerCAmelCase:	Union[str, Any]   				=      encoder_attention_heads
						__lowerCAmelCase:	Any   				=      decoder_ffn_dim
						__lowerCAmelCase:	Any   				=      decoder_layers
						__lowerCAmelCase:	Union[str, Any]   				=      decoder_attention_heads
						__lowerCAmelCase:	List[str]   				=      dropout
						__lowerCAmelCase:	List[str]   				=      attention_dropout
						__lowerCAmelCase:	List[Any]   				=      activation_dropout
						__lowerCAmelCase:	int   				=      activation_function
						__lowerCAmelCase:	Optional[int]   				=      init_std
						__lowerCAmelCase:	Union[str, Any]   				=      encoder_layerdrop
						__lowerCAmelCase:	str   				=      decoder_layerdrop
						__lowerCAmelCase:	int   				=      use_cache
						__lowerCAmelCase:	Tuple   				=      encoder_layers
						__lowerCAmelCase:	Dict   				=      scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=UpperCamelCase__					,  bos_token_id=UpperCamelCase__					,  eos_token_id=UpperCamelCase__					,  is_encoder_decoder=UpperCamelCase__					,  decoder_start_token_id=UpperCamelCase__					,  forced_eos_token_id=UpperCamelCase__					,  **UpperCamelCase__					,  )





class    snake_case     (    __snake_case  ):

				@property
				def 							lowercase_					(     self							:    str)->       Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


						if self.task in ["default", "seq2seq-lm"]:
								__lowerCAmelCase:	int   				=      OrderedDict(
								    [
								        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
								        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
								    ])

								if self.use_past:
										__lowerCAmelCase:	Any   				=      {0: "batch"}
										__lowerCAmelCase:	List[Any]   				=      {0: "batch", 1: "past_decoder_sequence + sequence"}
								else:
										__lowerCAmelCase:	int   				=      {0: "batch", 1: "decoder_sequence"}
										__lowerCAmelCase:	Union[str, Any]   				=      {0: "batch", 1: "decoder_sequence"}

								if self.use_past:
										self.fill_with_past_key_values_(UpperCamelCase__					,  direction="inputs")
						elif self.task == "causal-lm":
								# TODO: figure this case out.
								__lowerCAmelCase:	Any   				=      OrderedDict(
								    [
								        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
								        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
								    ])
								if self.use_past:
										__lowerCAmelCase				,			__lowerCAmelCase:	str   				=      self.num_layers
										for i in range(UpperCamelCase__):
												__lowerCAmelCase:	List[str]   				=      {0: "batch", 2: "past_sequence + sequence"}
												__lowerCAmelCase:	Dict   				=      {0: "batch", 2: "past_sequence + sequence"}
						else:
								__lowerCAmelCase:	Dict   				=      OrderedDict(
								    [
								        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
								        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
								        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
								        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
								    ])

						return common_inputs

				@property
				def 							lowercase_					(     self							:    List[str])->       Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


						if self.task in ["default", "seq2seq-lm"]:
								__lowerCAmelCase:	int   				=      super().outputs
						else:
								__lowerCAmelCase:	Dict   				=      super(UpperCamelCase__					,  self).outputs
								if self.use_past:
										__lowerCAmelCase				,			__lowerCAmelCase:	Tuple   				=      self.num_layers
										for i in range(UpperCamelCase__):
												__lowerCAmelCase:	Any   				=      {0: "batch", 2: "past_sequence + sequence"}
												__lowerCAmelCase:	Any   				=      {0: "batch", 2: "past_sequence + sequence"}
						return common_outputs

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    PreTrainedTokenizer					,  UpperCamelCase__							:    int = -1					,  UpperCamelCase__							:    int = -1					,  UpperCamelCase__							:    bool = False					,  UpperCamelCase__							:    Optional[TensorType] = None					,  )->       Mapping[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)

						# Generate decoder inputs
						__lowerCAmelCase:	int   				=      seq_length if not self.use_past else 1
						__lowerCAmelCase:	Any   				=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
						__lowerCAmelCase:	Tuple   				=      dict(**UpperCamelCase__					,  **UpperCamelCase__)

						if self.use_past:
								if not is_torch_available():
										raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
								else:
										import torch
								__lowerCAmelCase				,			__lowerCAmelCase:	str   				=      common_inputs["input_ids"].shape
								__lowerCAmelCase:	Any   				=      common_inputs["decoder_input_ids"].shape[1]
								__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      self.num_attention_heads
								__lowerCAmelCase:	int   				=      (
								    batch,
								    num_encoder_attention_heads,
								    encoder_seq_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)
								__lowerCAmelCase:	List[str]   				=      decoder_seq_length + 3
								__lowerCAmelCase:	Tuple   				=      (
								    batch,
								    num_decoder_attention_heads,
								    decoder_past_length,
								    self._config.hidden_size // num_decoder_attention_heads,
								)

								__lowerCAmelCase:	Tuple   				=      torch.cat(
								    [common_inputs["decoder_attention_mask"], torch.ones(UpperCamelCase__					,  UpperCamelCase__)]					,  dim=1)

								__lowerCAmelCase:	List[Any]   				=      []
								# If the number of encoder and decoder layers are present in the model configuration, both are considered
								__lowerCAmelCase				,			__lowerCAmelCase:	int   				=      self.num_layers
								__lowerCAmelCase:	List[str]   				=      min(UpperCamelCase__					,  UpperCamelCase__)
								__lowerCAmelCase:	Optional[int]   				=      max(UpperCamelCase__					,  UpperCamelCase__) - min_num_layers
								__lowerCAmelCase:	Dict   				=      "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

								for _ in range(UpperCamelCase__):
										common_inputs["past_key_values"].append(
										    (
										        torch.zeros(UpperCamelCase__),
										        torch.zeros(UpperCamelCase__),
										        torch.zeros(UpperCamelCase__),
										        torch.zeros(UpperCamelCase__),
										    ))
								# TODO: test this.
								__lowerCAmelCase:	Dict   				=      encoder_shape if remaining_side_name == "encoder" else decoder_shape
								for _ in range(UpperCamelCase__					,  UpperCamelCase__):
										common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase__), torch.zeros(UpperCamelCase__)))
						return common_inputs

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    PreTrainedTokenizer					,  UpperCamelCase__							:    int = -1					,  UpperCamelCase__							:    int = -1					,  UpperCamelCase__							:    bool = False					,  UpperCamelCase__							:    Optional[TensorType] = None					,  )->       Mapping[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)

						if self.use_past:
								if not is_torch_available():
										raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
								else:
										import torch
								__lowerCAmelCase				,			__lowerCAmelCase:	Tuple   				=      common_inputs["input_ids"].shape
								# Not using the same length for past_key_values
								__lowerCAmelCase:	Optional[int]   				=      seqlen + 2
								__lowerCAmelCase				,			__lowerCAmelCase:	Optional[Any]   				=      self.num_layers
								__lowerCAmelCase				,			__lowerCAmelCase:	Dict   				=      self.num_attention_heads
								__lowerCAmelCase:	Tuple   				=      (
								    batch,
								    num_encoder_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)

								__lowerCAmelCase:	Tuple   				=      common_inputs["attention_mask"].dtype
								__lowerCAmelCase:	Optional[Any]   				=      torch.cat(
								    [common_inputs["attention_mask"], torch.ones(UpperCamelCase__					,  UpperCamelCase__					,  dtype=UpperCamelCase__)]					,  dim=1)
								__lowerCAmelCase:	List[Any]   				=      [
								    (torch.zeros(UpperCamelCase__), torch.zeros(UpperCamelCase__)) for _ in range(UpperCamelCase__)
								]
						return common_inputs

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    PreTrainedTokenizer					,  UpperCamelCase__							:    int = -1					,  UpperCamelCase__							:    int = -1					,  UpperCamelCase__							:    bool = False					,  UpperCamelCase__							:    Optional[TensorType] = None					,  )->       Mapping[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      compute_effective_axis_dimension(
						    UpperCamelCase__					,  fixed_dimension=OnnxConfig.default_fixed_batch					,  num_token_to_add=0)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__lowerCAmelCase:	str   				=      tokenizer.num_special_tokens_to_add(UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      compute_effective_axis_dimension(
						    UpperCamelCase__					,  fixed_dimension=OnnxConfig.default_fixed_sequence					,  num_token_to_add=UpperCamelCase__)

						# Generate dummy inputs according to compute batch and sequence
						__lowerCAmelCase:	List[Any]   				=      [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
						__lowerCAmelCase:	List[Any]   				=      dict(tokenizer(UpperCamelCase__					,  return_tensors=UpperCamelCase__))
						return common_inputs

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    PreTrainedTokenizer					,  UpperCamelCase__							:    int = -1					,  UpperCamelCase__							:    int = -1					,  UpperCamelCase__							:    bool = False					,  UpperCamelCase__							:    Optional[TensorType] = None					,  )->       Mapping[str, Any]:



						'''simple docstring'''


						if self.task in ["default", "seq2seq-lm"]:
								__lowerCAmelCase:	List[str]   				=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
								    UpperCamelCase__					,  batch_size=UpperCamelCase__					,  seq_length=UpperCamelCase__					,  is_pair=UpperCamelCase__					,  framework=UpperCamelCase__)

						elif self.task == "causal-lm":
								__lowerCAmelCase:	Optional[int]   				=      self._generate_dummy_inputs_for_causal_lm(
								    UpperCamelCase__					,  batch_size=UpperCamelCase__					,  seq_length=UpperCamelCase__					,  is_pair=UpperCamelCase__					,  framework=UpperCamelCase__)
						else:
								__lowerCAmelCase:	Optional[Any]   				=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    UpperCamelCase__					,  batch_size=UpperCamelCase__					,  seq_length=UpperCamelCase__					,  is_pair=UpperCamelCase__					,  framework=UpperCamelCase__)

						return common_inputs




				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    Optional[int])->       List[Any]:



						'''simple docstring'''


						if self.task in ["default", "seq2seq-lm"]:
								__lowerCAmelCase:	Optional[int]   				=      super()._flatten_past_key_values_(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)
						else:
								__lowerCAmelCase:	Optional[Any]   				=      super(UpperCamelCase__					,  self)._flatten_past_key_values_(
								    UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)





"""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    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	int   				=      args.log_outputs
		__lowerCAmelCase:	Tuple   				=      "_".join(args.dataset.split("/"			) + [args.config, args.split]			)

		# load metric
		__lowerCAmelCase:	str   				=      load_metric("wer"			)
		__lowerCAmelCase:	int   				=      load_metric("cer"			)

		# compute metrics
		__lowerCAmelCase:	Optional[Any]   				=      wer.compute(references=result["target"] ,			predictions=result["prediction"]			)
		__lowerCAmelCase:	Dict   				=      cer.compute(references=result["target"] ,			predictions=result["prediction"]			)

		# print & log results
		__lowerCAmelCase:	Optional[int]   				=      F"WER: {wer_result}\nCER: {cer_result}"
		print(__SCREAMING_SNAKE_CASE			)

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

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

				with open(__SCREAMING_SNAKE_CASE ,			"w"			) as p, open(__SCREAMING_SNAKE_CASE ,			"w"			) as t:
						# mapping function to write output
						def write_to_file(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			):
								p.write(F"{i}" + "\n"			)
								p.write(batch["prediction"] + "\n"			)
								t.write(F"{i}" + "\n"			)
								t.write(batch["target"] + "\n"			)

						result.map(__SCREAMING_SNAKE_CASE ,			with_indices=__SCREAMING_SNAKE_CASE			)






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

		__lowerCAmelCase:	List[Any]   				=      re.sub(__SCREAMING_SNAKE_CASE ,			"" ,			text.lower()			)

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

		for t in token_sequences_to_ignore:
				__lowerCAmelCase:	str   				=      " ".join(text.split(__SCREAMING_SNAKE_CASE			)			)

		return text






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Dict:
		# load dataset
		__lowerCAmelCase:	List[str]   				=      load_dataset(args.dataset ,			args.config ,			split=args.split ,			use_auth_token=__SCREAMING_SNAKE_CASE			)

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

		# load processor
		__lowerCAmelCase:	Dict   				=      AutoFeatureExtractor.from_pretrained(args.model_id			)
		__lowerCAmelCase:	str   				=      feature_extractor.sampling_rate

		# resample audio
		__lowerCAmelCase:	Tuple   				=      dataset.cast_column("audio" ,			Audio(sampling_rate=__SCREAMING_SNAKE_CASE			)			)

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

		# map function to decode audio
		def map_to_pred(__SCREAMING_SNAKE_CASE			):
				__lowerCAmelCase:	Union[str, Any]   				=      asr(
				    batch["audio"]["array"] ,			chunk_length_s=args.chunk_length_s ,			stride_length_s=args.stride_length_s			)

				__lowerCAmelCase:	Any   				=      prediction["text"]
				__lowerCAmelCase:	List[str]   				=      normalize_text(batch["sentence"]			)
				return batch

		# run inference on all examples
		__lowerCAmelCase:	str   				=      dataset.map(__SCREAMING_SNAKE_CASE ,			remove_columns=dataset.column_names			)

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


if __name__ == "__main__":
			__A						       =      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.",
			)
			__A						       =      parser.parse_args()

			main(args)


