
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, 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.models.blenderbot_small.modeling_flax_blenderbot_small import (
        FlaxBlenderbotSmallForConditionalGeneration,
        FlaxBlenderbotSmallModel,
        shift_tokens_right,
    )



def lowerCamelCase						(     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						,    SCREAMING_SNAKE_CASE=None						,    ):
    '''simple docstring'''







    if attention_mask is None:
        __UpperCamelCase  :int    						=	np.where(input_ids != config.pad_token_id						,    1						,    0			)
    if decoder_attention_mask is None:
        __UpperCamelCase  :Tuple    						=	np.where(decoder_input_ids != config.pad_token_id						,    1						,    0			)
    if head_mask is None:
        __UpperCamelCase  :Union[str, Any]    						=	np.ones((config.encoder_layers, config.encoder_attention_heads)			)
    if decoder_head_mask is None:
        __UpperCamelCase  :Optional[int]    						=	np.ones((config.decoder_layers, config.decoder_attention_heads)			)
    if cross_attn_head_mask is None:
        __UpperCamelCase  :int    						=	np.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": attention_mask,
    }


class 			lowerCamelCase_  :






    '''simple docstring'''






    def __init__(			self				,				__lowercase				,				__lowercase=13				,				__lowercase=7				,				__lowercase=True				,				__lowercase=False				,				__lowercase=99				,				__lowercase=16				,				__lowercase=2				,				__lowercase=4				,				__lowercase=4				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=32				,				__lowercase=2				,				__lowercase=1				,				__lowercase=0				,				__lowercase=0.02				,				) ->			List[str]:
        __UpperCamelCase  :Union[str, Any]    						=	parent
        __UpperCamelCase  :List[str]    						=	batch_size
        __UpperCamelCase  :Any    						=	seq_length
        __UpperCamelCase  :Tuple    						=	is_training
        __UpperCamelCase  :Optional[Any]    						=	use_labels
        __UpperCamelCase  :int    						=	vocab_size
        __UpperCamelCase  :int    						=	hidden_size
        __UpperCamelCase  :Optional[Any]    						=	num_hidden_layers
        __UpperCamelCase  :Any    						=	num_attention_heads
        __UpperCamelCase  :Optional[int]    						=	intermediate_size
        __UpperCamelCase  :Tuple    						=	hidden_act
        __UpperCamelCase  :List[str]    						=	hidden_dropout_prob
        __UpperCamelCase  :Optional[Any]    						=	attention_probs_dropout_prob
        __UpperCamelCase  :List[Any]    						=	max_position_embeddings
        __UpperCamelCase  :str    						=	eos_token_id
        __UpperCamelCase  :Any    						=	pad_token_id
        __UpperCamelCase  :str    						=	bos_token_id
        __UpperCamelCase  :Union[str, Any]    						=	initializer_range







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :List[str]    						=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,				self.vocab_size)				,				3				,				self.vocab_size)
        __UpperCamelCase  :Optional[int]    						=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,				dtype=np.intaa))				,				-1)

        __UpperCamelCase  :str    						=	shift_tokens_right(__lowercase				,				1				,				2)

        __UpperCamelCase  :str    						=	BlenderbotSmallConfig(
            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_id=self.eos_token_id				,				bos_token_id=self.bos_token_id				,				pad_token_id=self.pad_token_id				,				initializer_range=self.initializer_range				,				use_cache=__lowercase				,				)
        __UpperCamelCase  :List[Any]    						=	prepare_blenderbot_inputs_dict(__lowercase				,				__lowercase				,				__lowercase)
        return config, inputs_dict







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Optional[Any]    						=	self.prepare_config_and_inputs()
        return config, inputs_dict







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase) ->			List[str]:
        __UpperCamelCase  :Dict    						=	20
        __UpperCamelCase  :str    						=	model_class_name(__lowercase)

        __UpperCamelCase  :Dict    						=	model.encode(inputs_dict['''input_ids'''])

        __UpperCamelCase  :Dict    						=	(
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        __UpperCamelCase  :List[str]    						=	model.init_cache(decoder_input_ids.shape[0]				,				__lowercase				,				__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,				dtype='''i4''')

        __UpperCamelCase  :Union[str, Any]    						=	jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]				,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,				)
        __UpperCamelCase  :Optional[Any]    						=	model.decode(
            decoder_input_ids[:, :-1]				,				__lowercase				,				decoder_attention_mask=__lowercase				,				past_key_values=__lowercase				,				decoder_position_ids=__lowercase				,				)

        __UpperCamelCase  :str    						=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,				dtype='''i4''')
        __UpperCamelCase  :Optional[int]    						=	model.decode(
            decoder_input_ids[:, -1:]				,				__lowercase				,				decoder_attention_mask=__lowercase				,				past_key_values=outputs_cache.past_key_values				,				decoder_position_ids=__lowercase				,				)

        __UpperCamelCase  :List[Any]    						=	model.decode(__lowercase				,				__lowercase)

        __UpperCamelCase  :Dict    						=	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     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase) ->			str:
        __UpperCamelCase  :Optional[Any]    						=	20
        __UpperCamelCase  :Dict    						=	model_class_name(__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	model.encode(inputs_dict['''input_ids'''])

        __UpperCamelCase  :List[Any]    						=	(
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        __UpperCamelCase  :str    						=	jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
            ]				,				axis=-1				,				)

        __UpperCamelCase  :Union[str, Any]    						=	model.init_cache(decoder_input_ids.shape[0]				,				__lowercase				,				__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]				,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,				)

        __UpperCamelCase  :Dict    						=	model.decode(
            decoder_input_ids[:, :-1]				,				__lowercase				,				decoder_attention_mask=__lowercase				,				past_key_values=__lowercase				,				decoder_position_ids=__lowercase				,				)
        __UpperCamelCase  :Union[str, Any]    						=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,				dtype='''i4''')
        __UpperCamelCase  :List[Any]    						=	model.decode(
            decoder_input_ids[:, -1:]				,				__lowercase				,				past_key_values=outputs_cache.past_key_values				,				decoder_attention_mask=__lowercase				,				decoder_position_ids=__lowercase				,				)

        __UpperCamelCase  :int    						=	model.decode(__lowercase				,				__lowercase				,				decoder_attention_mask=__lowercase)

        __UpperCamelCase  :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 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    a__			:		Any      			=   9_9







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[str]    						=	np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]				,				dtype=np.intaa				,				)

        __UpperCamelCase  :Tuple    						=	input_ids.shape[0]
        __UpperCamelCase  :Tuple    						=	BlenderbotSmallConfig(
            vocab_size=self.vocab_size				,				d_model=24				,				encoder_layers=2				,				decoder_layers=2				,				encoder_attention_heads=2				,				decoder_attention_heads=2				,				encoder_ffn_dim=32				,				decoder_ffn_dim=32				,				max_position_embeddings=48				,				eos_token_id=2				,				pad_token_id=1				,				bos_token_id=0				,				)
        return config, input_ids, batch_size







    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :str    						=	self._get_config_and_data()
        __UpperCamelCase  :str    						=	FlaxBlenderbotSmallForConditionalGeneration(__lowercase)
        __UpperCamelCase  :List[Any]    						=	lm_model(input_ids=__lowercase)
        __UpperCamelCase  :List[str]    						=	(batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :int    						=	BlenderbotSmallConfig(
            vocab_size=self.vocab_size				,				d_model=14				,				encoder_layers=2				,				decoder_layers=2				,				encoder_attention_heads=2				,				decoder_attention_heads=2				,				encoder_ffn_dim=8				,				decoder_ffn_dim=8				,				max_position_embeddings=48				,				)
        __UpperCamelCase  :Optional[int]    						=	FlaxBlenderbotSmallForConditionalGeneration(__lowercase)
        __UpperCamelCase  :Optional[int]    						=	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,				dtype=np.intaa)
        __UpperCamelCase  :Union[str, Any]    						=	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,				dtype=np.intaa)
        __UpperCamelCase  :str    						=	lm_model(input_ids=__lowercase				,				decoder_input_ids=__lowercase)
        __UpperCamelCase  :List[Any]    						=	(*summary.shape, config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[Any]    						=	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,				dtype=np.intaa)
        __UpperCamelCase  :str    						=	shift_tokens_right(__lowercase				,				1				,				2)
        __UpperCamelCase  :Dict    						=	np.equal(__lowercase				,				1).astype(np.floataa).sum()
        __UpperCamelCase  :Optional[int]    						=	np.equal(__lowercase				,				1).astype(np.floataa).sum()
        self.assertEqual(shifted.shape				,				input_ids.shape)
        self.assertEqual(__lowercase				,				n_pad_before - 1)
        self.assertTrue(np.equal(shifted[:, 0]				,				2).all())


@require_flax
class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase		,			UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Optional[Any]      			=   True
    a__			:		int      			=   (
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    a__			:		Union[str, Any]      			=   (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :str    						=	FlaxBlenderbotSmallModelTester(self)







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :List[Any]    						=	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     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :Optional[int]    						=	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)







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :List[str]    						=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__):
                __UpperCamelCase  :List[str]    						=	self._prepare_for_class(__lowercase				,				__lowercase)
                __UpperCamelCase  :str    						=	model_class(__lowercase)

                @jax.jit
                def encode_jitted(__lowercase				,				__lowercase=None				,				**__lowercase):
                    return model.encode(input_ids=__lowercase				,				attention_mask=__lowercase)

                with self.subTest('''JIT Enabled'''):
                    __UpperCamelCase  :List[str]    						=	encode_jitted(**__lowercase).to_tuple()

                with self.subTest('''JIT Disabled'''):
                    with jax.disable_jit():
                        __UpperCamelCase  :str    						=	encode_jitted(**__lowercase).to_tuple()

                self.assertEqual(len(__lowercase)				,				len(__lowercase))
                for jitted_output, output in zip(__lowercase				,				__lowercase):
                    self.assertEqual(jitted_output.shape				,				output.shape)







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :Dict    						=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__):
                __UpperCamelCase  :Union[str, Any]    						=	model_class(__lowercase)
                __UpperCamelCase  :Dict    						=	model.encode(inputs_dict['''input_ids''']				,				inputs_dict['''attention_mask'''])

                __UpperCamelCase  :Tuple    						=	{
                    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                    '''encoder_outputs''': encoder_outputs,
                }

                @jax.jit
                def decode_jitted(__lowercase				,				__lowercase				,				__lowercase):
                    return model.decode(
                        decoder_input_ids=__lowercase				,				decoder_attention_mask=__lowercase				,				encoder_outputs=__lowercase				,				)

                with self.subTest('''JIT Enabled'''):
                    __UpperCamelCase  :Union[str, Any]    						=	decode_jitted(**__lowercase).to_tuple()

                with self.subTest('''JIT Disabled'''):
                    with jax.disable_jit():
                        __UpperCamelCase  :Union[str, Any]    						=	decode_jitted(**__lowercase).to_tuple()

                self.assertEqual(len(__lowercase)				,				len(__lowercase))
                for jitted_output, output in zip(__lowercase				,				__lowercase):
                    self.assertEqual(jitted_output.shape				,				output.shape)







    @slow
    def     UpperCamelCase__     (			self) ->			List[Any]:
        for model_class_name in self.all_model_classes:
            __UpperCamelCase  :Tuple    						=	model_class_name.from_pretrained('''facebook/blenderbot_small-90M''')
            # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
            __UpperCamelCase  :Tuple    						=	np.ones((1, 1)) * model.config.eos_token_id
            __UpperCamelCase  :List[Any]    						=	model(__lowercase)
            self.assertIsNotNone(__lowercase)






from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


__lowercase											=      datasets.utils.logging.get_logger(__name__)


class 			lowerCamelCase_  (     folder_based_builder.FolderBasedBuilderConfig				):






    '''simple docstring'''






    a__			:		bool      			=   None
    a__			:		bool      			=   None


class 			lowerCamelCase_  (     folder_based_builder.FolderBasedBuilder				):






    '''simple docstring'''






    a__			:		List[Any]      			=   datasets.Audio()
    a__			:		int      			=   """audio"""
    a__			:		str      			=   AudioFolderConfig
    a__			:		List[str]  # definition at the bottom of the script
    a__			:		int      			=   AudioClassification(audio_column="""audio"""		,			label_column="""label"""				)
__lowercase											=      [
    '''.aiff''',
    '''.au''',
    '''.avr''',
    '''.caf''',
    '''.flac''',
    '''.htk''',
    '''.svx''',
    '''.mat4''',
    '''.mat5''',
    '''.mpc2k''',
    '''.ogg''',
    '''.paf''',
    '''.pvf''',
    '''.raw''',
    '''.rf64''',
    '''.sd2''',
    '''.sds''',
    '''.ircam''',
    '''.voc''',
    '''.w64''',
    '''.wav''',
    '''.nist''',
    '''.wavex''',
    '''.wve''',
    '''.xi''',
    '''.mp3''',
    '''.opus''',
]
__lowercase											=      AUDIO_EXTENSIONS





