

"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel





@require_tf
class    snake_case     :
				SCREAMING_SNAKE_CASE_       :				Dict       				=  BlenderbotConfig
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  {}
				SCREAMING_SNAKE_CASE_       :				Any       				=  """gelu"""

				def __init__(     self							:    List[Any]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    List[str]=1_3					,  UpperCamelCase__							:    Tuple=7					,  UpperCamelCase__							:    Any=True					,  UpperCamelCase__							:    str=False					,  UpperCamelCase__							:    str=9_9					,  UpperCamelCase__							:    int=3_2					,  UpperCamelCase__							:    Any=2					,  UpperCamelCase__							:    Union[str, Any]=4					,  UpperCamelCase__							:    Tuple=3_7					,  UpperCamelCase__							:    Tuple=0.1					,  UpperCamelCase__							:    Optional[Any]=0.1					,  UpperCamelCase__							:    List[str]=2_0					,  UpperCamelCase__							:    int=2					,  UpperCamelCase__							:    str=1					,  UpperCamelCase__							:    int=0					,  )->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      parent
						__lowerCAmelCase:	Optional[int]   				=      batch_size
						__lowerCAmelCase:	int   				=      seq_length
						__lowerCAmelCase:	Optional[Any]   				=      is_training
						__lowerCAmelCase:	Optional[Any]   				=      use_labels
						__lowerCAmelCase:	int   				=      vocab_size
						__lowerCAmelCase:	Tuple   				=      hidden_size
						__lowerCAmelCase:	str   				=      num_hidden_layers
						__lowerCAmelCase:	List[str]   				=      num_attention_heads
						__lowerCAmelCase:	Dict   				=      intermediate_size

						__lowerCAmelCase:	Union[str, Any]   				=      hidden_dropout_prob
						__lowerCAmelCase:	Optional[Any]   				=      attention_probs_dropout_prob
						__lowerCAmelCase:	List[Any]   				=      max_position_embeddings
						__lowerCAmelCase:	Tuple   				=      eos_token_id
						__lowerCAmelCase:	List[Any]   				=      pad_token_id
						__lowerCAmelCase:	Tuple   				=      bos_token_id

				def 							lowercase_					(     self							:    Union[str, Any])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      ids_tensor([self.batch_size, self.seq_length - 1]					,  self.vocab_size)
						__lowerCAmelCase:	Any   				=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)					,  1)
						__lowerCAmelCase:	Tuple   				=      tf.concat([input_ids, eos_tensor]					,  axis=1)

						__lowerCAmelCase:	List[Any]   				=      ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size)

						__lowerCAmelCase:	Dict   				=      self.config_cls(
						    vocab_size=self.vocab_size					,  d_model=self.hidden_size					,  encoder_layers=self.num_hidden_layers					,  decoder_layers=self.num_hidden_layers					,  encoder_attention_heads=self.num_attention_heads					,  decoder_attention_heads=self.num_attention_heads					,  encoder_ffn_dim=self.intermediate_size					,  decoder_ffn_dim=self.intermediate_size					,  dropout=self.hidden_dropout_prob					,  attention_dropout=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  eos_token_ids=[2]					,  bos_token_id=self.bos_token_id					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.pad_token_id					,  **self.config_updates					,  )
						__lowerCAmelCase:	Optional[Any]   				=      prepare_blenderbot_inputs_dict(snake_case__					,  snake_case__					,  snake_case__)
						return config, inputs_dict




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



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      TFBlenderbotModel(config=snake_case__).get_decoder()
						__lowerCAmelCase:	int   				=      inputs_dict['''input_ids''']

						__lowerCAmelCase:	Optional[Any]   				=      input_ids[:1, :]
						__lowerCAmelCase:	List[str]   				=      inputs_dict['''attention_mask'''][:1, :]
						__lowerCAmelCase:	Dict   				=      inputs_dict['''head_mask''']
						__lowerCAmelCase:	Optional[Any]   				=      1

						# first forward pass
						__lowerCAmelCase:	List[Any]   				=      model(snake_case__					,  attention_mask=snake_case__					,  head_mask=snake_case__					,  use_cache=snake_case__)

						__lowerCAmelCase:	Optional[Any]   				=      outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						__lowerCAmelCase:	str   				=      ids_tensor((self.batch_size, 3)					,  config.vocab_size)
						__lowerCAmelCase:	int   				=      tf.cast(ids_tensor((self.batch_size, 3)					,  2)					,  tf.inta)

						# append to next input_ids and
						__lowerCAmelCase:	Optional[int]   				=      tf.concat([input_ids, next_tokens]					,  axis=-1)
						__lowerCAmelCase:	Any   				=      tf.concat([attention_mask, next_attn_mask]					,  axis=-1)

						__lowerCAmelCase:	int   				=      model(snake_case__					,  attention_mask=snake_case__)[0]
						__lowerCAmelCase:	Optional[int]   				=      model(snake_case__					,  attention_mask=snake_case__					,  past_key_values=snake_case__)[0]

						self.parent.assertEqual(next_tokens.shape[1]					,  output_from_past.shape[1])

						# select random slice
						__lowerCAmelCase:	str   				=      int(ids_tensor((1,)					,  output_from_past.shape[-1]))
						__lowerCAmelCase:	Tuple   				=      output_from_no_past[:, -3:, random_slice_idx]
						__lowerCAmelCase:	int   				=      output_from_past[:, :, random_slice_idx]

						# test that outputs are equal for slice
						tf.debugging.assert_near(snake_case__					,  snake_case__					,  rtol=1e-3)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			)     ->     Any:





		if attention_mask is None:
				__lowerCAmelCase:	Union[str, Any]   				=      tf.cast(tf.math.not_equal(__lowerCAmelCase ,			config.pad_token_id			) ,			tf.inta			)
		if decoder_attention_mask is None:
				__lowerCAmelCase:	Optional[Any]   				=      tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape ,			dtype=tf.inta			),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,			config.pad_token_id			) ,			tf.inta			),
				    ] ,			axis=-1 ,			)
		if head_mask is None:
				__lowerCAmelCase:	Optional[int]   				=      tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				__lowerCAmelCase:	Dict   				=      tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				__lowerCAmelCase:	int   				=      tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}





@require_tf
class    snake_case     (    lowerCamelCase__,							lowerCamelCase__,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
				SCREAMING_SNAKE_CASE_       :				Dict       				=  (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  (
				    {
				        """conversational""": TFBlenderbotForConditionalGeneration,
				        """feature-extraction""": TFBlenderbotModel,
				        """summarization""": TFBlenderbotForConditionalGeneration,
				        """text2text-generation""": TFBlenderbotForConditionalGeneration,
				        """translation""": TFBlenderbotForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_       :				Any       				=  True
				SCREAMING_SNAKE_CASE_       :				int       				=  False
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  False

				def 							lowercase_					(     self							:    List[str])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      TFBlenderbotModelTester(self)
						__lowerCAmelCase:	List[Any]   				=      ConfigTester(self					,  config_class=snake_case__)

				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						self.config_tester.run_common_tests()




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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*snake_case__)





@require_tokenizers
@require_tf
class    snake_case     (    unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  ["""My friends are cool but they eat too many carbs."""]
				SCREAMING_SNAKE_CASE_       :				str       				=  """facebook/blenderbot-400M-distill"""

				@cached_property
				def 							lowercase_					(     self							:    str)->       int:



						'''simple docstring'''


						return BlenderbotTokenizer.from_pretrained(self.model_name)

				@cached_property
				def 							lowercase_					(     self							:    List[str])->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
						return model




				@slow
				def 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.tokenizer(self.src_text					,  return_tensors="tf")
						__lowerCAmelCase:	Union[str, Any]   				=      self.model.generate(
						    model_inputs.input_ids					,  )
						__lowerCAmelCase:	str   				=      self.tokenizer.batch_decode(generated_ids.numpy()					,  skip_special_tokens=snake_case__)[0]
						assert (
						    generated_words
						    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
						)









"""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)


