

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
     lowercase_									=    "platform"

     import jax
     import jax.numpy as jnp

     from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[Any]       ,     SCREAMING_SNAKE_CASE__     :      List[str]       ,     SCREAMING_SNAKE_CASE__     :      Union[str, Any]=None       ,     SCREAMING_SNAKE_CASE__     :      Tuple=None )				-> Optional[Any]:





    '''simple docstring'''






    if attention_mask is None:
        A__        =  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					:			Dict,lowercase_					:			Optional[Any],lowercase_					:			Dict=1_3,lowercase_					:			str=7,lowercase_					:			str=True,lowercase_					:			List[str]=False,lowercase_					:			List[str]=9_9,lowercase_					:			str=1_6,lowercase_					:			Tuple=2,lowercase_					:			str=4,lowercase_					:			List[str]=4,lowercase_					:			Union[str, Any]="gelu",lowercase_					:			Any=0.1,lowercase_					:			List[str]=0.1,lowercase_					:			Optional[int]=2_0,lowercase_					:			int=2,lowercase_					:			Union[str, Any]=1,lowercase_					:			Tuple=0,lowercase_					:			Any=1_6,lowercase_					:			List[Any]=1_6,lowercase_					:			Optional[Any]=0.02,)->       Tuple:





        '''simple docstring'''







        A__        =  parent
        A__        =  batch_size
        A__        =  seq_length
        A__        =  is_training
        A__        =  use_labels
        A__        =  vocab_size
        A__        =  hidden_size
        A__        =  num_hidden_layers
        A__        =  num_attention_heads
        A__        =  intermediate_size
        A__        =  hidden_act
        A__        =  hidden_dropout_prob
        A__        =  attention_probs_dropout_prob
        A__        =  max_position_embeddings
        A__        =  eos_token_id
        A__        =  pad_token_id
        A__        =  bos_token_id
        A__        =  embed_dim
        A__        =  word_embed_proj_dim
        A__        =  initializer_range
        A__        =  False


    def 		snake_case__	( self					:			List[str]						)->       str:





        '''simple docstring'''







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

        A__        =  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=lowercase_,word_embed_proj_dim=self.word_embed_proj_dim,initializer_range=self.initializer_range,use_cache=lowercase_,)
        A__        =  prepare_opt_inputs_dict(lowercase_,lowercase_						)
        return config, inputs_dict


    def 		snake_case__	( self					:			Tuple						)->       Union[str, Any]:





        '''simple docstring'''







        A__ ,		A__        =  self.prepare_config_and_inputs()
        return config, inputs_dict


    def 		snake_case__	( self					:			List[str],lowercase_					:			str,lowercase_					:			str,lowercase_					:			Optional[Any]						)->       Tuple:





        '''simple docstring'''







        A__        =  2_0
        A__        =  model_class_name(lowercase_						)

        A__        =  inputs_dict['input_ids']
        A__        =  inputs_dict['attention_mask']

        A__        =  model.init_cache(input_ids.shape[0],lowercase_						)
        A__        =  jnp.ones((input_ids.shape[0], max_length),dtype='i4'						)

        A__        =  jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1						)[None, :],(input_ids.shape[0], input_ids.shape[-1] - 1),)
        A__        =  model(
            input_ids[:, :-1],attention_mask=lowercase_,past_key_values=lowercase_,position_ids=lowercase_,)

        A__        =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],dtype='i4'						)
        A__        =  model(
            input_ids[:, -1:],attention_mask=lowercase_,past_key_values=outputs_cache.past_key_values,position_ids=lowercase_,)

        A__        =  model(lowercase_						)

        A__        =  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 		snake_case__	( self					:			int,lowercase_					:			Any,lowercase_					:			Any,lowercase_					:			str						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  2_0
        A__        =  model_class_name(lowercase_						)

        A__ ,		A__        =  (
            inputs_dict['input_ids'],
            inputs_dict['attention_mask'],
        )

        A__        =  jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])						),
            ],axis=-1,)

        A__        =  model.init_cache(input_ids.shape[0],lowercase_						)
        A__        =  jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1						)[None, :],(input_ids.shape[0], input_ids.shape[-1] - 1),)

        A__        =  model(
            input_ids[:, :-1],attention_mask=lowercase_,past_key_values=lowercase_,position_ids=lowercase_,)
        A__        =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],dtype='i4'						)
        A__        =  model(
            input_ids[:, -1:],past_key_values=outputs_cache.past_key_values,attention_mask=lowercase_,position_ids=lowercase_,)

        A__        =  model(lowercase_,attention_mask=lowercase_						)

        A__        =  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		(				_UpperCAmelCase       ,				unittest.TestCase       ,				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
    lowerCamelCase						      =				() if is_flax_available() else ()


    def 		snake_case__	( self					:			List[Any]						)->       Optional[int]:





        '''simple docstring'''







        A__        =  FlaxOPTModelTester(self						)


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





        '''simple docstring'''







        A__ ,		A__        =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(lowercase_,lowercase_,lowercase_						)


    def 		snake_case__	( self					:			List[str]						)->       Optional[Any]:





        '''simple docstring'''







        A__ ,		A__        =  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(lowercase_,lowercase_,lowercase_						)


    @slow
    def 		snake_case__	( self					:			Any						)->       Optional[int]:





        '''simple docstring'''







        for model_class_name in self.all_model_classes:
            A__        =  model_class_name.from_pretrained('facebook/opt-125m'						)
            A__        =  np.ones((1, 1)						) * model.config.eos_token_id
            A__        =  model(lowercase_						)
            self.assertIsNotNone(lowercase_						)




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







    """simple docstring"""





    @slow
    def 		snake_case__	( self					:			Dict						)->       Any:





        '''simple docstring'''







        A__        =  FlaxOPTModel.from_pretrained('facebook/opt-350m'						)
        A__        =  jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]						)
        A__        =  model(input_ids=lowercase_						).last_hidden_state
        A__        =  (1, 1_1, 5_1_2)
        self.assertEqual(output.shape,lowercase_						)
        A__        =  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],lowercase_,atol=4E-2						)						)




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







    """simple docstring"""





    def 		snake_case__	( self					:			List[str]						)->       Optional[Any]:





        '''simple docstring'''







        super().setUp()
        A__        =  'facebook/opt-350m'


    def 		snake_case__	( self					:			str						)->       int:





        '''simple docstring'''







        A__        =  FlaxOPTForCausalLM.from_pretrained(self.path_model						)
        A__        =  GPTaTokenizer.from_pretrained(self.path_model						)

        A__        =  [
            '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
        A__        =  tokenizer(lowercase_,return_tensors='jax',padding=lowercase_,add_special_tokens=lowercase_						)
        A__        =  model(inputs.input_ids,attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
        A__        =  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(lowercase_,lowercase_,atol=4E-2						)						)

        A__        =  jax.jit(lowercase_						)
        A__        =  model(inputs.input_ids,attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
        self.assertTrue(jnp.allclose(lowercase_,lowercase_,atol=4E-2						)						)




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







    """simple docstring"""





    @property
    def 		snake_case__	( self					:			Tuple						)->       Tuple:





        '''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 		snake_case__	( self					:			int						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  'facebook/opt-125m'

        A__        =  [
            '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',
        ]

        A__        =  []

        A__        =  FlaxOPTForCausalLM.from_pretrained(lowercase_						)
        A__        =  GPTaTokenizer.from_pretrained(lowercase_						)

        for prompt in self.prompts:
            A__        =  tokenizer(lowercase_,return_tensors='jax'						).input_ids

            A__        =  model.generate(lowercase_,max_length=1_0						)
            A__        =  generated_ids[0]

            A__        =  tokenizer.batch_decode(lowercase_,skip_special_tokens=lowercase_						)
            predicted_outputs += generated_string

        self.assertListEqual(lowercase_,lowercase_						)


    def 		snake_case__	( self					:			Optional[Any]						)->       int:





        '''simple docstring'''







        A__        =  'facebook/opt-350m'

        A__        =  [
            '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',
        ]

        A__        =  []
        A__        =  FlaxOPTForCausalLM.from_pretrained(lowercase_						)
        A__        =  GPTaTokenizer.from_pretrained(lowercase_						)

        for prompt in self.prompts:
            A__        =  tokenizer(lowercase_,return_tensors='jax'						).input_ids

            A__        =  model.generate(lowercase_,max_length=1_0						)
            A__        =  generated_ids[0]

            A__        =  tokenizer.batch_decode(lowercase_,skip_special_tokens=lowercase_						)
            predicted_outputs += generated_string

        self.assertListEqual(lowercase_,lowercase_						)


    def 		snake_case__	( self					:			Dict						)->       str:





        '''simple docstring'''







        A__        =  'facebook/opt-125m'
        A__        =  [
            'Today is a beautiful day and I want to thank',
            'In the city of Rome Canaver Canaver Canaver Canaver',
        ]
        A__        =  FlaxOPTForCausalLM.from_pretrained(lowercase_						)
        A__        =  GPTaTokenizer.from_pretrained(lowercase_						)
        A__        =  tokenizer(
            [
                'Today is a beautiful day and I want to',
                'In the city of',
            ],return_tensors='jax',padding=lowercase_,)

        A__        =  jax.jit(model.generate						)

        A__        =  jit_generate(inputs['input_ids'],attention_mask=inputs['attention_mask']						).sequences

        A__        =  tokenizer.batch_decode(lowercase_,skip_special_tokens=lowercase_						)

        self.assertIsNotNone(lowercase_,lowercase_						)


    def 		snake_case__	( self					:			Dict						)->       Optional[int]:





        '''simple docstring'''







        A__        =  'facebook/opt-350m'

        A__        =  GPTaTokenizer.from_pretrained(lowercase_						)
        A__        =  FlaxOPTForCausalLM.from_pretrained(lowercase_						)

        A__        =  'left'

        # use different length sentences to test batching
        A__        =  [
            'Hello, my dog is a little',
            'Today, I',
        ]

        A__        =  tokenizer(lowercase_,return_tensors='jax',padding=lowercase_						)
        A__        =  inputs['input_ids']

        A__        =  model.generate(input_ids=lowercase_,attention_mask=inputs['attention_mask'],trace=lowercase_						)

        A__        =  tokenizer(sentences[0],return_tensors='jax'						).input_ids
        A__        =  model.generate(input_ids=lowercase_						)

        A__        =  inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
        A__        =  tokenizer(sentences[1],return_tensors='jax'						).input_ids
        A__        =  model.generate(input_ids=lowercase_,max_length=model.config.max_length - num_paddings						)

        A__        =  tokenizer.batch_decode(outputs[0],skip_special_tokens=lowercase_						)
        A__        =  tokenizer.decode(output_non_padded[0][0],skip_special_tokens=lowercase_						)
        A__        =  tokenizer.decode(output_padded[0][0],skip_special_tokens=lowercase_						)

        A__        =  [
            '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(lowercase_,lowercase_						)
        self.assertListEqual(lowercase_,[non_padded_sentence, padded_sentence]						)




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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


lowercase_									=    logging.get_logger(__name__)


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				['input_values', 'attention_mask']


    def __init__( self					:			Tuple,lowercase_					:			Optional[Any]=1,lowercase_					:			Union[str, Any]=1_6_0_0_0,lowercase_					:			str=1_2_8,lowercase_					:			Optional[Any]=1_0_2_4,lowercase_					:			Union[str, Any]=0.0,lowercase_					:			Any=True,lowercase_					:			List[Any]=-4.2_677_393,lowercase_					:			Union[str, Any]=4.5_689_974,lowercase_					:			Dict=False,**lowercase_					:			Dict,)->       int:





        '''simple docstring'''







        super().__init__(feature_size=lowercase_,sampling_rate=lowercase_,padding_value=lowercase_,**lowercase_						)
        A__        =  num_mel_bins
        A__        =  max_length
        A__        =  do_normalize
        A__        =  mean
        A__        =  std
        A__        =  return_attention_mask


    def 		snake_case__	( self					:			Any,lowercase_					:			np.ndarray,lowercase_					:			int,)->       np.ndarray:





        '''simple docstring'''







        A__        =  torch.from_numpy(lowercase_						).unsqueeze(0						)
        A__        =  ta_kaldi.fbank(
            lowercase_,htk_compat=lowercase_,sample_frequency=self.sampling_rate,use_energy=lowercase_,window_type='hanning',num_mel_bins=self.num_mel_bins,dither=0.0,frame_shift=1_0,)

        A__        =  fbank.shape[0]
        A__        =  max_length - n_frames

        # pad or truncate, depending on difference
        if difference > 0:
            A__        =  torch.nn.ZeroPadad((0, 0, 0, difference)						)
            A__        =  pad_module(lowercase_						)
        elif difference < 0:
            A__        =  fbank[0:max_length, :]

        A__        =  fbank.numpy()

        return fbank


    def 		snake_case__	( self					:			int,lowercase_					:			np.ndarray						)->       np.ndarray:





        '''simple docstring'''







        return (input_values - (self.mean)) / (self.std * 2)


    def __call__( self					:			str,lowercase_					:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],lowercase_					:			Optional[int] = None,lowercase_					:			Optional[Union[str, TensorType]] = None,**lowercase_					:			str,)->       BatchFeature:





        '''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.'						)

        A__        =  isinstance(lowercase_,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}'						)
        A__        =  is_batched_numpy or (
            isinstance(lowercase_,(list, tuple)						) and (isinstance(raw_speech[0],(np.ndarray, tuple, list)						))
        )

        if is_batched:
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase_,np.ndarray						):
            A__        =  np.asarray(lowercase_,dtype=np.floataa						)
        elif isinstance(lowercase_,np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
            A__        =  raw_speech.astype(np.floataa						)

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

        # extract fbank features and pad/truncate to max_length
        A__        =  [self._extract_fbank_features(lowercase_,max_length=self.max_length						) for waveform in raw_speech]

        # convert into BatchFeature
        A__        =  BatchFeature({'input_values': features}						)

        # make sure list is in array format
        A__        =  padded_inputs.get('input_values'						)
        if isinstance(input_values[0],lowercase_						):
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for feature in input_values]

        # normalization
        if self.do_normalize:
            A__        =  [self.normalize(lowercase_						) for feature in input_values]

        if return_tensors is not None:
            A__        =  padded_inputs.convert_to_tensors(lowercase_						)

        return padded_inputs

