





"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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
		A			      =	"""platform"""

		import jax
		import jax.numpy as jnp

		from transformers import BlenderbotTokenizer
		from transformers.models.blenderbot.modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    shift_tokens_right,
		)

def      __A   (      a_    :str ,							a_    :Dict ,							a_    :str=None ,							a_    :Optional[int]=None ,							a_    :str=None ,							a_    :Tuple=None ,							a_    :List[Any]=None ,							a_    :Optional[Any]=None ,							)		->		Any:
				if attention_mask is None:
								__a			:    Tuple      							=	np.where(input_ids != config.pad_token_id ,							1 ,							0)
				if decoder_attention_mask is None:
								__a			:    Optional[int]      							=	np.where(decoder_input_ids != config.pad_token_id ,							1 ,							0)
				if head_mask is None:
								__a			:    Optional[Any]      							=	np.ones((config.encoder_layers, config.encoder_attention_heads))
				if decoder_head_mask is None:
								__a			:    Optional[Any]      							=	np.ones((config.decoder_layers, config.decoder_attention_heads))
				if cross_attn_head_mask is None:
								__a			:    Tuple      							=	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 					__lowercase      :







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase=13						,      _UpperCAmelCase=7						,      _UpperCAmelCase=True						,      _UpperCAmelCase=False						,      _UpperCAmelCase=99						,      _UpperCAmelCase=16						,      _UpperCAmelCase=2						,      _UpperCAmelCase=4						,      _UpperCAmelCase=4						,      _UpperCAmelCase="gelu"						,      _UpperCAmelCase=0.1						,      _UpperCAmelCase=0.1						,      _UpperCAmelCase=32						,      _UpperCAmelCase=2						,      _UpperCAmelCase=1						,      _UpperCAmelCase=0						,      _UpperCAmelCase=0.0_2						,      ):
									__a			:    Dict      							=	parent
									__a			:    Tuple      							=	batch_size
									__a			:    List[str]      							=	seq_length
									__a			:    Optional[int]      							=	is_training
									__a			:    Optional[Any]      							=	use_labels
									__a			:    str      							=	vocab_size
									__a			:    str      							=	hidden_size
									__a			:    Any      							=	num_hidden_layers
									__a			:    Dict      							=	num_attention_heads
									__a			:    int      							=	intermediate_size
									__a			:    Optional[int]      							=	hidden_act
									__a			:    int      							=	hidden_dropout_prob
									__a			:    List[Any]      							=	attention_probs_dropout_prob
									__a			:    List[Any]      							=	max_position_embeddings
									__a			:    Union[str, Any]      							=	eos_token_id
									__a			:    Any      							=	pad_token_id
									__a			:    str      							=	bos_token_id
									__a			:    Tuple      							=	initializer_range







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[int]      							=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,      self.vocab_size  )						,      3						,      self.vocab_size  )
									__a			:    Tuple      							=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,      dtype=np.intaa  ))						,      -1  )

									__a			:    int      							=	shift_tokens_right(A_						,      1						,      2  )

									__a			:    List[Any]      							=	BlenderbotConfig(
									    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=A_						,      )
									__a			:    Optional[int]      							=	prepare_blenderbot_inputs_dict(A_						,      A_						,      A_  )
									return config, inputs_dict







					def 			_lowerCamelCase		(    self  ):
									__a							,    __a			:    Optional[Any]      							=	self.prepare_config_and_inputs()
									return config, inputs_dict







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    Any      							=	20
									__a			:    int      							=	model_class_name(A_  )

									__a			:    List[str]      							=	model.encode(inputs_dict['''input_ids''']  )

									__a							,    __a			:    List[str]      							=	(
									    inputs_dict['''decoder_input_ids'''],
									    inputs_dict['''decoder_attention_mask'''],
									)

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

									__a			:    List[Any]      							=	jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]						,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,      )
									__a			:    str      							=	model.decode(
									    decoder_input_ids[:, :-1]						,      A_						,      decoder_attention_mask=A_						,      past_key_values=A_						,      decoder_position_ids=A_						,      )

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

									__a			:    str      							=	model.decode(A_						,      A_  )

									__a			:    int      							=	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 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    Optional[Any]      							=	20
									__a			:    Union[str, Any]      							=	model_class_name(A_  )

									__a			:    Tuple      							=	model.encode(inputs_dict['''input_ids''']  )

									__a							,    __a			:    Union[str, Any]      							=	(
									    inputs_dict['''decoder_input_ids'''],
									    inputs_dict['''decoder_attention_mask'''],
									)

									__a			:    List[Any]      							=	jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
									    ]						,      axis=-1						,      )

									__a			:    Optional[Any]      							=	model.init_cache(decoder_input_ids.shape[0]						,      A_						,      A_  )
									__a			:    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)						,      )

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

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

									__a			:    Optional[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 					__lowercase      (      unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	99







					def 			_lowerCamelCase		(    self  ):
									__a			:    Any      							=	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						,      )

									__a			:    List[Any]      							=	input_ids.shape[0]
									__a			:    Any      							=	BlenderbotConfig(
									    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 			_lowerCamelCase		(    self  ):
									__a							,    __a							,    __a			:    Any      							=	self._get_config_and_data()
									__a			:    Optional[int]      							=	FlaxBlenderbotForConditionalGeneration(A_  )
									__a			:    List[Any]      							=	lm_model(input_ids=A_  )
									__a			:    int      							=	(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape						,      A_  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[int]      							=	BlenderbotConfig(
									    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						,      )
									__a			:    int      							=	FlaxBlenderbotForConditionalGeneration(A_  )
									__a			:    Optional[int]      							=	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,      dtype=np.intaa  )
									__a			:    Union[str, Any]      							=	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,      dtype=np.intaa  )
									__a			:    List[Any]      							=	lm_model(input_ids=A_						,      decoder_input_ids=A_  )
									__a			:    Optional[int]      							=	(*summary.shape, config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape						,      A_  )







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






@require_flax
class 					__lowercase      (      __SCREAMING_SNAKE_CASE ,   unittest.TestCase ,   __SCREAMING_SNAKE_CASE				):







					'''simple docstring'''




					__lowerCAmelCase	=	True
					__lowerCAmelCase	=	(
					    (
					        FlaxBlenderbotModel,
					        FlaxBlenderbotForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					__lowerCAmelCase	=	(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







					def 			_lowerCamelCase		(    self  ):
									__a			:    Union[str, Any]      							=	FlaxBlenderbotModelTester(self  )







					def 			_lowerCamelCase		(    self  ):
									__a							,    __a			:    Tuple      							=	self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(A_						,      A_						,      A_  )







					def 			_lowerCamelCase		(    self  ):
									__a							,    __a			:    Tuple      							=	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(A_						,      A_						,      A_  )







					def 			_lowerCamelCase		(    self  ):
									__a							,    __a			:    Any      							=	self.model_tester.prepare_config_and_inputs_for_common()

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

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

																	with self.subTest('''JIT Enabled'''  ):
																					__a			:    Any      							=	encode_jitted(**A_  ).to_tuple()

																	with self.subTest('''JIT Disabled'''  ):
																					with jax.disable_jit():
																									__a			:    Optional[Any]      							=	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 			_lowerCamelCase		(    self  ):
									__a							,    __a			:    Union[str, Any]      							=	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__  ):
																	__a			:    Dict      							=	model_class(A_  )
																	__a			:    List[Any]      							=	model.encode(inputs_dict['''input_ids''']						,      inputs_dict['''attention_mask''']  )

																	__a			:    List[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(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
																					return model.decode(
																					    decoder_input_ids=A_						,      decoder_attention_mask=A_						,      encoder_outputs=A_						,      )

																	with self.subTest('''JIT Enabled'''  ):
																					__a			:    List[str]      							=	decode_jitted(**A_  ).to_tuple()

																	with self.subTest('''JIT Disabled'''  ):
																					with jax.disable_jit():
																									__a			:    Optional[int]      							=	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 			_lowerCamelCase		(    self  ):

									for model_class_name in self.all_model_classes:
													__a			:    List[str]      							=	model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''  )
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													__a			:    Any      							=	np.ones((1, 1)  ) * model.config.eos_token_id
													__a			:    Any      							=	model(A_  )
													self.assertIsNotNone(A_  )







					@unittest.skipUnless(jax_device != '''cpu'''						,      '''3B test too slow on CPU.'''  )
					@slow
					def 			_lowerCamelCase		(    self  ):
									__a			:    Dict      							=	{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
									__a			:    Union[str, Any]      							=	{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

									__a			:    Any      							=	FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''						,      from_pt=A_  )
									__a			:    List[str]      							=	BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''  )

									__a			:    List[Any]      							=	['''Sam''']
									__a			:    List[str]      							=	tokenizer(A_						,      return_tensors='''jax'''  )

									__a			:    Optional[int]      							=	model.generate(**A_						,      **A_  )
									__a			:    str      							=	'''Sam is a great name. It means "sun" in Gaelic.'''

									__a			:    Optional[int]      							=	tokenizer.batch_decode(A_						,      **A_  )
									assert generated_txt[0].strip() == tgt_text






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:				List[str]				=			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Dict				=			[
      """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """WavLMForAudioFrameClassification""",
      """WavLMForCTC""",
      """WavLMForSequenceClassification""",
      """WavLMForXVector""",
      """WavLMModel""",
      """WavLMPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
