

"""simple docstring"""

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    _UpperCamelCase		    =						"""platform"""

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__=None				,				lowercase__=None      ) ->	str:
 if attention_mask is None:
  lowerCAmelCase__      :							str    =							np.where(input_ids != config.pad_token_id				,				1				,				0      )
 return {
     "input_ids": input_ids,
     "attention_mask": attention_mask,
 }






@require_flax
class  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case=13 ,				snake_case=7 ,				snake_case=True ,				snake_case=False ,				snake_case=99 ,				snake_case=16 ,				snake_case=2 ,				snake_case=4 ,				snake_case=4 ,				snake_case="gelu" ,				snake_case=0.1 ,				snake_case=0.1 ,				snake_case=20 ,				snake_case=2 ,				snake_case=1 ,				snake_case=0 ,				snake_case=16 ,				snake_case=16 ,				snake_case=0.02 ,				):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							parent
        lowerCAmelCase__      :							List[Any]    =							batch_size
        lowerCAmelCase__      :							Union[str, Any]    =							seq_length
        lowerCAmelCase__      :							List[Any]    =							is_training
        lowerCAmelCase__      :							Tuple    =							use_labels
        lowerCAmelCase__      :							Dict    =							vocab_size
        lowerCAmelCase__      :							Tuple    =							hidden_size
        lowerCAmelCase__      :							List[str]    =							num_hidden_layers
        lowerCAmelCase__      :							Any    =							num_attention_heads
        lowerCAmelCase__      :							str    =							intermediate_size
        lowerCAmelCase__      :							Union[str, Any]    =							hidden_act
        lowerCAmelCase__      :							Tuple    =							hidden_dropout_prob
        lowerCAmelCase__      :							int    =							attention_probs_dropout_prob
        lowerCAmelCase__      :							List[Any]    =							max_position_embeddings
        lowerCAmelCase__      :							Union[str, Any]    =							eos_token_id
        lowerCAmelCase__      :							List[Any]    =							pad_token_id
        lowerCAmelCase__      :							int    =							bos_token_id
        lowerCAmelCase__      :							Tuple    =							embed_dim
        lowerCAmelCase__      :							Tuple    =							word_embed_proj_dim
        lowerCAmelCase__      :							Tuple    =							initializer_range
        lowerCAmelCase__      :							Optional[int]    =							False






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,				self.vocab_size      ) ,				3 ,				self.vocab_size      )
        lowerCAmelCase__      :							int    =							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,				dtype=np.intaa      )) ,				-1      )

        lowerCAmelCase__      :							Any    =							OPTConfig(
            vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				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_id=self.eos_token_id ,				bos_token_id=self.bos_token_id ,				pad_token_id=self.pad_token_id ,				embed_dim=self.embed_dim ,				is_encoder_decoder=snake_case ,				word_embed_proj_dim=self.word_embed_proj_dim ,				initializer_range=self.initializer_range ,				use_cache=snake_case ,				)
        lowerCAmelCase__      :							List[Any]    =							prepare_opt_inputs_dict(snake_case ,				snake_case      )
        return config, inputs_dict






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Union[str, Any]    =							self.prepare_config_and_inputs()
        return config, inputs_dict






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							20
        lowerCAmelCase__      :							str    =							model_class_name(snake_case      )

        lowerCAmelCase__      :							List[Any]    =							inputs_dict["input_ids"]
        lowerCAmelCase__      :							Union[str, Any]    =							inputs_dict["attention_mask"]

        lowerCAmelCase__      :							Union[str, Any]    =							model.init_cache(input_ids.shape[0] ,				snake_case      )
        lowerCAmelCase__      :							Optional[int]    =							jnp.ones((input_ids.shape[0], max_length) ,				dtype="i4"      )

        lowerCAmelCase__      :							Optional[int]    =							jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1      )[None, :] ,				(input_ids.shape[0], input_ids.shape[-1] - 1) ,				)
        lowerCAmelCase__      :							int    =							model(
            input_ids[:, :-1] ,				attention_mask=snake_case ,				past_key_values=snake_case ,				position_ids=snake_case ,				)

        lowerCAmelCase__      :							List[str]    =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,				dtype="i4"      )
        lowerCAmelCase__      :							Optional[int]    =							model(
            input_ids[:, -1:] ,				attention_mask=snake_case ,				past_key_values=outputs_cache.past_key_values ,				position_ids=snake_case ,				)

        lowerCAmelCase__      :							str    =							model(snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
        self.parent.assertTrue(diff < 1e-3 ,				msg=F"""Max diff is {diff}"""      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							20
        lowerCAmelCase__      :							str    =							model_class_name(snake_case      )

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Tuple    =							(
            inputs_dict["input_ids"],
            inputs_dict["attention_mask"],
        )

        lowerCAmelCase__      :							Union[str, Any]    =							jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])      ),
            ] ,				axis=-1 ,				)

        lowerCAmelCase__      :							Optional[int]    =							model.init_cache(input_ids.shape[0] ,				snake_case      )
        lowerCAmelCase__      :							Optional[Any]    =							jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1      )[None, :] ,				(input_ids.shape[0], input_ids.shape[-1] - 1) ,				)

        lowerCAmelCase__      :							Optional[Any]    =							model(
            input_ids[:, :-1] ,				attention_mask=snake_case ,				past_key_values=snake_case ,				position_ids=snake_case ,				)
        lowerCAmelCase__      :							Tuple    =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,				dtype="i4"      )
        lowerCAmelCase__      :							Optional[int]    =							model(
            input_ids[:, -1:] ,				past_key_values=outputs_cache.past_key_values ,				attention_mask=snake_case ,				position_ids=snake_case ,				)

        lowerCAmelCase__      :							List[Any]    =							model(snake_case ,				attention_mask=snake_case      )

        lowerCAmelCase__      :							Any    =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
        self.parent.assertTrue(diff < 1e-3 ,				msg=F"""Max diff is {diff}"""      )






@require_flax
class  __a				(	__magic_name__					,  unittest.TestCase					,  __magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Tuple          =	(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
       __UpperCamelCase				:			Union[str, Any]          =	() if is_flax_available() else ()






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							FlaxOPTModelTester(self      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Optional[int]    =							self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(snake_case ,				snake_case ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[str]    =							self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward_with_attn_mask(snake_case ,				snake_case ,				snake_case      )






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for model_class_name in self.all_model_classes:
         lowerCAmelCase__      :							Optional[int]    =							model_class_name.from_pretrained("facebook/opt-125m"      )
         lowerCAmelCase__      :							int    =							np.ones((1, 1)      ) * model.config.eos_token_id
         lowerCAmelCase__      :							Any    =							model(snake_case      )
         self.assertIsNotNone(snake_case      )






@require_sentencepiece
@require_flax
class  __a				(	unittest.TestCase					):





       """simple docstring"""


       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							FlaxOPTModel.from_pretrained("facebook/opt-350m"      )
        lowerCAmelCase__      :							Any    =							jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]      )
        lowerCAmelCase__      :							Tuple    =							model(input_ids=snake_case      ).last_hidden_state
        lowerCAmelCase__      :							Dict    =							(1, 11, 512)
        self.assertEqual(output.shape ,				snake_case      )
        lowerCAmelCase__      :							List[str]    =							jnp.array(
            [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]      )
        self.assertTrue(jnp.allclose(output[:, :3, :3] ,				snake_case ,				atol=4e-2      )      )






@require_flax
@slow
class  __a				(	unittest.TestCase					):





       """simple docstring"""


       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        super().setUp()
        lowerCAmelCase__      :							str    =							"facebook/opt-350m"






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							FlaxOPTForCausalLM.from_pretrained(self.path_model      )
        lowerCAmelCase__      :							Any    =							GPTaTokenizer.from_pretrained(self.path_model      )

        lowerCAmelCase__      :							Optional[int]    =							[
            "Today is a beautiful day and I want to",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]
        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
        lowerCAmelCase__      :							List[Any]    =							tokenizer(snake_case ,				return_tensors="jax" ,				padding=snake_case ,				add_special_tokens=snake_case      )
        lowerCAmelCase__      :							Optional[int]    =							model(inputs.input_ids ,				attention_mask=inputs.attention_mask      )[0].mean(axis=-1      )
        lowerCAmelCase__      :							Union[str, Any]    =							jnp.array(
            [
                [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
                [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
                [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
                [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
            ]      )
        self.assertTrue(jnp.allclose(snake_case ,				snake_case ,				atol=4e-2      )      )

        lowerCAmelCase__      :							List[str]    =							jax.jit(snake_case      )
        lowerCAmelCase__      :							Any    =							model(inputs.input_ids ,				attention_mask=inputs.attention_mask      )[0].mean(axis=-1      )
        self.assertTrue(jnp.allclose(snake_case ,				snake_case ,				atol=4e-2      )      )






@require_flax
@slow
class  __a				(	unittest.TestCase					):





       """simple docstring"""


       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return [
            "Today is a beautiful day and I want",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							"facebook/opt-125m"

        lowerCAmelCase__      :							List[Any]    =							[
            "Today is a beautiful day and I want to",
            "In the city of New York, the city",
            "Paris is the capital of France and the capital",
            "Computers and mobile phones have taken over the",
        ]

        lowerCAmelCase__      :							Any    =							[]

        lowerCAmelCase__      :							str    =							FlaxOPTForCausalLM.from_pretrained(snake_case      )
        lowerCAmelCase__      :							List[Any]    =							GPTaTokenizer.from_pretrained(snake_case      )

        for prompt in self.prompts:
         lowerCAmelCase__      :							Optional[Any]    =							tokenizer(snake_case ,				return_tensors="jax"      ).input_ids

         lowerCAmelCase__      :							Optional[int]    =							model.generate(snake_case ,				max_length=10      )
         lowerCAmelCase__      :							Optional[int]    =							generated_ids[0]

         lowerCAmelCase__      :							Tuple    =							tokenizer.batch_decode(snake_case ,				skip_special_tokens=snake_case      )
         predicted_outputs += generated_string

        self.assertListEqual(snake_case ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							"facebook/opt-350m"

        lowerCAmelCase__      :							Union[str, Any]    =							[
            "Today is a beautiful day and I want to",
            "In the city of San Francisco, the city",
            "Paris is the capital of France and the capital",
            "Computers and mobile phones have taken over the",
        ]

        lowerCAmelCase__      :							Dict    =							[]
        lowerCAmelCase__      :							Tuple    =							FlaxOPTForCausalLM.from_pretrained(snake_case      )
        lowerCAmelCase__      :							List[Any]    =							GPTaTokenizer.from_pretrained(snake_case      )

        for prompt in self.prompts:
         lowerCAmelCase__      :							Union[str, Any]    =							tokenizer(snake_case ,				return_tensors="jax"      ).input_ids

         lowerCAmelCase__      :							Dict    =							model.generate(snake_case ,				max_length=10      )
         lowerCAmelCase__      :							Union[str, Any]    =							generated_ids[0]

         lowerCAmelCase__      :							Any    =							tokenizer.batch_decode(snake_case ,				skip_special_tokens=snake_case      )
         predicted_outputs += generated_string

        self.assertListEqual(snake_case ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							"facebook/opt-125m"
        lowerCAmelCase__      :							Tuple    =							[
            "Today is a beautiful day and I want to thank",
            "In the city of Rome Canaver Canaver Canaver Canaver",
        ]
        lowerCAmelCase__      :							Dict    =							FlaxOPTForCausalLM.from_pretrained(snake_case      )
        lowerCAmelCase__      :							Any    =							GPTaTokenizer.from_pretrained(snake_case      )
        lowerCAmelCase__      :							Union[str, Any]    =							tokenizer(
            [
                "Today is a beautiful day and I want to",
                "In the city of",
            ] ,				return_tensors="jax" ,				padding=snake_case ,				)

        lowerCAmelCase__      :							int    =							jax.jit(model.generate      )

        lowerCAmelCase__      :							List[str]    =							jit_generate(inputs["input_ids"] ,				attention_mask=inputs["attention_mask"]      ).sequences

        lowerCAmelCase__      :							str    =							tokenizer.batch_decode(snake_case ,				skip_special_tokens=snake_case      )

        self.assertIsNotNone(snake_case ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							"facebook/opt-350m"

        lowerCAmelCase__      :							List[Any]    =							GPTaTokenizer.from_pretrained(snake_case      )
        lowerCAmelCase__      :							str    =							FlaxOPTForCausalLM.from_pretrained(snake_case      )

        lowerCAmelCase__      :							List[str]    =							"left"

        # use different length sentences to test batching
        lowerCAmelCase__      :							Tuple    =							[
            "Hello, my dog is a little",
            "Today, I",
        ]

        lowerCAmelCase__      :							Union[str, Any]    =							tokenizer(snake_case ,				return_tensors="jax" ,				padding=snake_case      )
        lowerCAmelCase__      :							str    =							inputs["input_ids"]

        lowerCAmelCase__      :							Union[str, Any]    =							model.generate(input_ids=snake_case ,				attention_mask=inputs["attention_mask"] ,				trace=snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							tokenizer(sentences[0] ,				return_tensors="jax"      ).input_ids
        lowerCAmelCase__      :							Union[str, Any]    =							model.generate(input_ids=snake_case      )

        lowerCAmelCase__      :							Dict    =							inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
        lowerCAmelCase__      :							str    =							tokenizer(sentences[1] ,				return_tensors="jax"      ).input_ids
        lowerCAmelCase__      :							Dict    =							model.generate(input_ids=snake_case ,				max_length=model.config.max_length - num_paddings      )

        lowerCAmelCase__      :							int    =							tokenizer.batch_decode(outputs[0] ,				skip_special_tokens=snake_case      )
        lowerCAmelCase__      :							Optional[Any]    =							tokenizer.decode(output_non_padded[0][0] ,				skip_special_tokens=snake_case      )
        lowerCAmelCase__      :							List[str]    =							tokenizer.decode(output_padded[0][0] ,				skip_special_tokens=snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							[
            "Hello, my dog is a little bit of a dork.\nI'm a little bit",
            "Today, I was in the middle of a conversation with a friend about the",
        ]
        self.assertListEqual(snake_case ,				snake_case      )
        self.assertListEqual(snake_case ,				[non_padded_sentence, padded_sentence]      )









"""simple docstring"""

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_UpperCamelCase		    =						logging.get_logger(__name__)






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Tuple          =	['input_values', 'attention_mask']






       def __init__(      self ,				snake_case=1 ,				snake_case=16_000 ,				snake_case=0.0 ,				snake_case=False ,				snake_case=True ,				**snake_case ,				):





        """simple docstring"""

        super().__init__(feature_size=snake_case ,				sampling_rate=snake_case ,				padding_value=snake_case ,				**snake_case      )
        lowerCAmelCase__      :							Dict    =							return_attention_mask
        lowerCAmelCase__      :							List[str]    =							do_normalize






       @staticmethod
       def  SCREAMING_SNAKE_CASE_  (      snake_case ,				snake_case ,				snake_case = 0.0      ):





        """simple docstring"""

        if attention_mask is not None:
         lowerCAmelCase__      :							List[str]    =							np.array(snake_case ,				np.intaa      )
         lowerCAmelCase__      :							str    =							[]

         for vector, length in zip(snake_case ,				attention_mask.sum(-1      )      ):
          lowerCAmelCase__      :							Any    =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7      )
          if length < normed_slice.shape[0]:
           lowerCAmelCase__      :							List[Any]    =							padding_value

          normed_input_values.append(snake_case      )
        else:
         lowerCAmelCase__      :							List[Any]    =							[(x - x.mean()) / np.sqrt(x.var() + 1e-7      ) for x in input_values]

        return normed_input_values






       def __call__(      self ,				snake_case ,				snake_case = False ,				snake_case = None ,				snake_case = False ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
          raise ValueError(
              F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
              F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
              F""" {self.sampling_rate} and not {sampling_rate}."""      )
        else:
         logger.warning(
             "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
             "Failing to do so can result in silent errors that might be hard to debug."      )

        lowerCAmelCase__      :							List[str]    =							isinstance(snake_case ,				np.ndarray      ) and len(raw_speech.shape      ) > 1
        if is_batched_numpy and len(raw_speech.shape      ) > 2:
         raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""      )
        lowerCAmelCase__      :							Tuple    =							is_batched_numpy or (
            isinstance(snake_case ,				(list, tuple)      ) and (isinstance(raw_speech[0] ,				(np.ndarray, tuple, list)      ))
        )

        # always return batch
        if not is_batched:
         lowerCAmelCase__      :							Tuple    =							[raw_speech]

        # convert into correct format for padding
        lowerCAmelCase__      :							Tuple    =							BatchFeature({"input_values": raw_speech}      )

        lowerCAmelCase__      :							Any    =							self.pad(
            snake_case ,				padding=snake_case ,				max_length=snake_case ,				truncation=snake_case ,				pad_to_multiple_of=snake_case ,				return_attention_mask=snake_case ,				)

        # convert input values to correct format
        lowerCAmelCase__      :							List[Any]    =							padded_inputs["input_values"]
        if not isinstance(input_values[0] ,				np.ndarray      ):
         lowerCAmelCase__      :							Any    =							[np.asarray(snake_case ,				dtype=np.floataa      ) for array in input_values]
        elif (
            not isinstance(snake_case ,				np.ndarray      )
            and isinstance(input_values[0] ,				np.ndarray      )
            and input_values[0].dtype is np.dtype(np.floataa      )
        ):
         lowerCAmelCase__      :							str    =							[array.astype(np.floataa      ) for array in input_values]
        elif isinstance(snake_case ,				np.ndarray      ) and input_values.dtype is np.dtype(np.floataa      ):
         lowerCAmelCase__      :							List[Any]    =							input_values.astype(np.floataa      )

        # convert attention_mask to correct format
        lowerCAmelCase__      :							int    =							padded_inputs.get("attention_mask"      )
        if attention_mask is not None:
         lowerCAmelCase__      :							Tuple    =							[np.asarray(snake_case ,				dtype=np.intaa      ) for array in attention_mask]

        # zero-mean and unit-variance normalization
        if self.do_normalize:
         lowerCAmelCase__      :							List[Any]    =							(
             attention_mask
             if self._get_padding_strategies(snake_case ,				max_length=snake_case      ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         lowerCAmelCase__      :							Union[str, Any]    =							self.zero_mean_unit_var_norm(
             padded_inputs["input_values"] ,				attention_mask=snake_case ,				padding_value=self.padding_value      )

        if return_tensors is not None:
         lowerCAmelCase__      :							Optional[Any]    =							padded_inputs.convert_to_tensors(snake_case      )

        return padded_inputs






