



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
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
						_snake_case		=  "platform"
						import jax
						import jax.numpy as jnp
						import numpy as np

						from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class   UpperCAmelCase_    :
							lowerCamelCase__   			=		PegasusConfig
							lowerCamelCase__   			=		{}
							lowerCamelCase__   			=		'gelu'







							def __init__(   self,			__a,			__a=13,			__a=7,			__a=True,			__a=False,			__a=99,			__a=32,			__a=5,			__a=4,			__a=37,			__a=0.1,			__a=0.1,			__a=20,			__a=2,			__a=1,			__a=0,			):

														'''simple docstring'''

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

														_lowerCAmelCase     :							str    =    hidden_dropout_prob
														_lowerCAmelCase     :							List[Any]    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							Any    =    max_position_embeddings
														_lowerCAmelCase     :							List[str]    =    eos_token_id
														_lowerCAmelCase     :							Any    =    pad_token_id
														_lowerCAmelCase     :							Optional[Any]    =    bos_token_id







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    ids_tensor([self.batch_size, self.seq_length - 1],			self.vocab_size).clip(3,			self.vocab_size)
														_lowerCAmelCase     :							str    =    np.expand_dims(np.array([self.eos_token_id] * self.batch_size),			1)
														_lowerCAmelCase     :							Optional[Any]    =    np.concatenate([input_ids, eos_tensor],			axis=1)

														_lowerCAmelCase     :							Dict    =    ids_tensor([self.batch_size, self.seq_length],			self.vocab_size)

														_lowerCAmelCase     :							str    =    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     :							int    =    prepare_pegasus_inputs_dict(__a,			__a,			__a)
														return config, inputs_dict







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    20
														_lowerCAmelCase     :							str    =    model_class_name(__a)

														_lowerCAmelCase     :							int    =    model.encode(inputs_dict["input_ids"])

														_lowerCAmelCase   ,    _lowerCAmelCase     :							List[Any]    =    (
														    inputs_dict["decoder_input_ids"],
														    inputs_dict["decoder_attention_mask"],
														)

														_lowerCAmelCase     :							Union[str, Any]    =    model.init_cache(decoder_input_ids.shape[0],			__a,			__a)
														_lowerCAmelCase     :							List[Any]    =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length),			dtype="i4")

														_lowerCAmelCase     :							Dict    =    jnp.broadcast_to(
														    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),			)
														_lowerCAmelCase     :							Dict    =    model.decode(
														    decoder_input_ids[:, :-1],			__a,			decoder_attention_mask=__a,			past_key_values=__a,			decoder_position_ids=__a,			)

														_lowerCAmelCase     :							str    =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],			dtype="i4")
														_lowerCAmelCase     :							Any    =    model.decode(
														    decoder_input_ids[:, -1:],			__a,			decoder_attention_mask=__a,			past_key_values=outputs_cache.past_key_values,			decoder_position_ids=__a,			)

														_lowerCAmelCase     :							List[str]    =    model.decode(__a,			__a)

														_lowerCAmelCase     :							str    =    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,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    20
														_lowerCAmelCase     :							Any    =    model_class_name(__a)

														_lowerCAmelCase     :							List[str]    =    model.encode(inputs_dict["input_ids"])

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    (
														    inputs_dict["decoder_input_ids"],
														    inputs_dict["decoder_attention_mask"],
														)

														_lowerCAmelCase     :							Optional[int]    =    jnp.concatenate(
														    [
														        decoder_attention_mask,
														        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
														    ],			axis=-1,			)

														_lowerCAmelCase     :							Tuple    =    model.init_cache(decoder_input_ids.shape[0],			__a,			__a)
														_lowerCAmelCase     :							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),			)

														_lowerCAmelCase     :							Optional[int]    =    model.decode(
														    decoder_input_ids[:, :-1],			__a,			decoder_attention_mask=__a,			past_key_values=__a,			decoder_position_ids=__a,			)
														_lowerCAmelCase     :							str    =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],			dtype="i4")
														_lowerCAmelCase     :							Union[str, Any]    =    model.decode(
														    decoder_input_ids[:, -1:],			__a,			past_key_values=outputs_cache.past_key_values,			decoder_attention_mask=__a,			decoder_position_ids=__a,			)

														_lowerCAmelCase     :							Optional[int]    =    model.decode(__a,			__a,			decoder_attention_mask=__a)

														_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 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=None				, _lowerCamelCase=None				, ):





							'''simple docstring'''




							if attention_mask is None:
														_lowerCAmelCase     :							Tuple    =    np.not_equal(_lowerCamelCase				, config.pad_token_id  ).astype(np.inta  )
							if decoder_attention_mask is None:
														_lowerCAmelCase     :							str    =    np.concatenate(
														    [
														        np.ones(decoder_input_ids[:, :1].shape				, dtype=np.inta  ),
														        np.not_equal(decoder_input_ids[:, 1:]				, config.pad_token_id  ).astype(np.inta  ),
														    ]				, axis=-1				, )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							}



@require_flax
class   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		(
							    (
							        FlaxPegasusForConditionalGeneration,
							        FlaxPegasusModel,
							    )
							    if is_flax_available()
							    else ()
							)
							lowerCamelCase__   			=		(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
							lowerCamelCase__   			=		True
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    FlaxPegasusModelTester(self)
														_lowerCAmelCase     :							int    =    ConfigTester(self,			config_class=__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.config_tester.run_common_tests()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    self.model_tester.prepare_config_and_inputs_for_common()
														for model_class in self.all_model_classes:
																					self.model_tester.check_use_cache_forward(__a,			__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs_for_common()
														for model_class in self.all_model_classes:
																					self.model_tester.check_use_cache_forward_with_attn_mask(__a,			__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__):
																												_lowerCAmelCase     :							Dict    =    self._prepare_for_class(__a,			__a)
																												_lowerCAmelCase     :							Optional[int]    =    model_class(__a)

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

																												with self.subTest("JIT Enabled"):
																																			_lowerCAmelCase     :							List[Any]    =    encode_jitted(**__a).to_tuple()

																												with self.subTest("JIT Disabled"):
																																			with jax.disable_jit():
																																										_lowerCAmelCase     :							Optional[int]    =    encode_jitted(**__a).to_tuple()

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__):
																												_lowerCAmelCase     :							List[str]    =    model_class(__a)
																												_lowerCAmelCase     :							Optional[int]    =    model.encode(inputs_dict["input_ids"],			inputs_dict["attention_mask"])

																												_lowerCAmelCase     :							str    =    {
																												    "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(__a,			__a,			__a):
																																			return model.decode(
																																			    decoder_input_ids=__a,			decoder_attention_mask=__a,			encoder_outputs=__a,			)

																												with self.subTest("JIT Enabled"):
																																			_lowerCAmelCase     :							str    =    decode_jitted(**__a).to_tuple()

																												with self.subTest("JIT Disabled"):
																																			with jax.disable_jit():
																																										_lowerCAmelCase     :							str    =    decode_jitted(**__a).to_tuple()

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







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for model_class_name in self.all_model_classes:
																					_lowerCAmelCase     :							Optional[int]    =    model_class_name.from_pretrained("google/pegasus-large",			from_pt=__a)
																					_lowerCAmelCase     :							Dict    =    np.ones((1, 1))
																					_lowerCAmelCase     :							List[str]    =    model(__a)
																					self.assertIsNotNone(__a)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
														_lowerCAmelCase     :							Any    =    PegasusTokenizer.from_pretrained("google/pegasus-xsum")

														_lowerCAmelCase     :							Optional[int]    =    [
														    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
														    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
														]

														_lowerCAmelCase     :							List[Any]    =    [
														    "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
														    "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
														]

														_lowerCAmelCase     :							int    =    tokenizer(__a,			return_tensors="np",			truncation=__a,			max_length=512,			padding=__a)
														_lowerCAmelCase     :							Union[str, Any]    =    model.generate(**__a,			num_beams=2).sequences
														_lowerCAmelCase     :							Union[str, Any]    =    tokenizer.batch_decode(__a,			skip_special_tokens=__a)
														assert tgt_text == decoded





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'wav2vec2'







							def __init__(   self,			__a=32,			__a=768,			__a=12,			__a=12,			__a=3072,			__a="gelu",			__a=0.1,			__a=0.1,			__a=0.1,			__a=0.0,			__a=0.0,			__a=0.1,			__a=0.1,			__a=0.02,			__a=1E-5,			__a="group",			__a="gelu",			__a=(512, 512, 512, 512, 512, 512, 512),			__a=(5, 2, 2, 2, 2, 2, 2),			__a=(10, 3, 3, 3, 3, 2, 2),			__a=False,			__a=128,			__a=16,			__a=False,			__a=True,			__a=0.05,			__a=10,			__a=2,			__a=0.0,			__a=10,			__a=0,			__a=320,			__a=2,			__a=0.1,			__a=100,			__a=256,			__a=256,			__a=0.1,			__a="sum",			__a=False,			__a=False,			__a=256,			__a=(512, 512, 512, 512, 1500),			__a=(5, 3, 3, 1, 1),			__a=(1, 2, 3, 1, 1),			__a=512,			__a=0,			__a=1,			__a=2,			__a=False,			__a=3,			__a=2,			__a=3,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a,			pad_token_id=__a,			bos_token_id=__a,			eos_token_id=__a)
														_lowerCAmelCase     :							str    =    hidden_size
														_lowerCAmelCase     :							Optional[int]    =    feat_extract_norm
														_lowerCAmelCase     :							Union[str, Any]    =    feat_extract_activation
														_lowerCAmelCase     :							Optional[Any]    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    list(__a)
														_lowerCAmelCase     :							str    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    conv_bias
														_lowerCAmelCase     :							str    =    num_conv_pos_embeddings
														_lowerCAmelCase     :							List[Any]    =    num_conv_pos_embedding_groups
														_lowerCAmelCase     :							str    =    len(self.conv_dim)
														_lowerCAmelCase     :							List[str]    =    num_hidden_layers
														_lowerCAmelCase     :							str    =    intermediate_size
														_lowerCAmelCase     :							Any    =    hidden_act
														_lowerCAmelCase     :							int    =    num_attention_heads
														_lowerCAmelCase     :							Optional[Any]    =    hidden_dropout
														_lowerCAmelCase     :							List[str]    =    attention_dropout
														_lowerCAmelCase     :							Tuple    =    activation_dropout
														_lowerCAmelCase     :							int    =    feat_proj_dropout
														_lowerCAmelCase     :							List[str]    =    final_dropout
														_lowerCAmelCase     :							int    =    layerdrop
														_lowerCAmelCase     :							int    =    layer_norm_eps
														_lowerCAmelCase     :							Union[str, Any]    =    initializer_range
														_lowerCAmelCase     :							str    =    vocab_size
														_lowerCAmelCase     :							Optional[Any]    =    do_stable_layer_norm
														_lowerCAmelCase     :							Any    =    use_weighted_layer_sum

														if (
														    (len(self.conv_stride) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel) != self.num_feat_extract_layers)
														    or (len(self.conv_dim) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																					    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																					    f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
																					    f" `len(config.conv_kernel) = {len(self.conv_kernel)}`.")

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														_lowerCAmelCase     :							str    =    apply_spec_augment
														_lowerCAmelCase     :							Optional[Any]    =    mask_time_prob
														_lowerCAmelCase     :							Optional[int]    =    mask_time_length
														_lowerCAmelCase     :							List[str]    =    mask_time_min_masks
														_lowerCAmelCase     :							Optional[int]    =    mask_feature_prob
														_lowerCAmelCase     :							Optional[int]    =    mask_feature_length
														_lowerCAmelCase     :							List[str]    =    mask_feature_min_masks

														# parameters for pretraining with codevector quantized representations
														_lowerCAmelCase     :							Union[str, Any]    =    num_codevectors_per_group
														_lowerCAmelCase     :							str    =    num_codevector_groups
														_lowerCAmelCase     :							Optional[int]    =    contrastive_logits_temperature
														_lowerCAmelCase     :							Optional[int]    =    feat_quantizer_dropout
														_lowerCAmelCase     :							Optional[int]    =    num_negatives
														_lowerCAmelCase     :							Union[str, Any]    =    codevector_dim
														_lowerCAmelCase     :							Any    =    proj_codevector_dim
														_lowerCAmelCase     :							Optional[int]    =    diversity_loss_weight

														# ctc loss
														_lowerCAmelCase     :							Tuple    =    ctc_loss_reduction
														_lowerCAmelCase     :							Tuple    =    ctc_zero_infinity

														# adapter
														_lowerCAmelCase     :							List[Any]    =    add_adapter
														_lowerCAmelCase     :							List[str]    =    adapter_kernel_size
														_lowerCAmelCase     :							str    =    adapter_stride
														_lowerCAmelCase     :							List[str]    =    num_adapter_layers
														_lowerCAmelCase     :							str    =    output_hidden_size or hidden_size
														_lowerCAmelCase     :							Tuple    =    adapter_attn_dim

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														_lowerCAmelCase     :							str    =    classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														_lowerCAmelCase     :							str    =    list(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    list(__a)
														_lowerCAmelCase     :							Tuple    =    xvector_output_dim







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return functools.reduce(operator.mul,			self.conv_stride,			1)
