




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
		__UpperCamelCase   :			Any          = "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						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase=None				,     __lowerCamelCase=None				,     __lowerCamelCase=None				,     __lowerCamelCase=None				,     __lowerCamelCase=None				,     __lowerCamelCase=None				,     ) ->			List[Any]:
	if attention_mask is None:
		a									=				np.where(input_ids != config.pad_token_id				,     1				,     0       )
	if decoder_attention_mask is None:
		a									=				np.where(decoder_input_ids != config.pad_token_id				,     1				,     0       )
	if head_mask is None:
		a									=				np.ones((config.encoder_layers, config.encoder_attention_heads)       )
	if decoder_head_mask is None:
		a									=				np.ones((config.decoder_layers, config.decoder_attention_heads)       )
	if cross_attn_head_mask is None:
		a									=				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      :
							def __init__(		self		:Optional[Any]						, __magic_name__		:List[Any]						, __magic_name__		:List[str]=13						, __magic_name__		:Any=7						, __magic_name__		:Optional[Any]=True						, __magic_name__		:List[str]=False						, __magic_name__		:Optional[Any]=99						, __magic_name__		:Union[str, Any]=16						, __magic_name__		:Dict=2						, __magic_name__		:Union[str, Any]=4						, __magic_name__		:Optional[int]=4						, __magic_name__		:Dict="gelu"						, __magic_name__		:List[Any]=0.1						, __magic_name__		:Optional[int]=0.1						, __magic_name__		:List[str]=32						, __magic_name__		:str=2						, __magic_name__		:List[Any]=1						, __magic_name__		:Any=0						, __magic_name__		:Optional[int]=0.02						, ):







								'''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									=				initializer_range
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''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									=				shift_tokens_right(__magic_name__						, 1						, 2							)

								a									=				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=__magic_name__						, )
								a									=				prepare_blenderbot_inputs_dict(__magic_name__						, __magic_name__						, __magic_name__							)
								return config, inputs_dict
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								a							,					a									=				self.prepare_config_and_inputs()
								return config, inputs_dict
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:Optional[int]						, __magic_name__		:Any						, __magic_name__		:Optional[Any]							):







								'''simple docstring'''







								a									=				20
								a									=				model_class_name(__magic_name__							)

								a									=				model.encode(inputs_dict["""input_ids"""]							)

								a							,					a									=				(
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

								a									=				model.init_cache(decoder_input_ids.shape[0]						, __magic_name__						, __magic_name__							)
								a									=				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						, dtype="""i4"""							)

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

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

								a									=				model.decode(__magic_name__						, __magic_name__							)

								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  lowerCamelCase__				(		self		:str						, __magic_name__		:Union[str, Any]						, __magic_name__		:List[Any]						, __magic_name__		:Dict							):







								'''simple docstring'''







								a									=				20
								a									=				model_class_name(__magic_name__							)

								a									=				model.encode(inputs_dict["""input_ids"""]							)

								a							,					a									=				(
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

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

								a									=				model.init_cache(decoder_input_ids.shape[0]						, __magic_name__						, __magic_name__							)
								a									=				jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]						, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						, )

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

								a									=				model.decode(__magic_name__						, __magic_name__						, decoder_attention_mask=__magic_name__							)

								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 						__lowerCAmelCase      (      unittest.TestCase					):
							UpperCamelCase__   	=    99
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				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									=				input_ids.shape[0]
								a									=				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		:Optional[Any]							):







								'''simple docstring'''







								a							,					a							,					a									=				self._get_config_and_data()
								a									=				FlaxBlenderbotForConditionalGeneration(__magic_name__							)
								a									=				lm_model(input_ids=__magic_name__							)
								a									=				(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs["""logits"""].shape						, __magic_name__							)
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								a									=				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									=				FlaxBlenderbotForConditionalGeneration(__magic_name__							)
								a									=				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						, dtype=np.intaa							)
								a									=				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						, dtype=np.intaa							)
								a									=				lm_model(input_ids=__magic_name__						, decoder_input_ids=__magic_name__							)
								a									=				(*summary.shape, config.vocab_size)
								self.assertEqual(outputs["""logits"""].shape						, __magic_name__							)





							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								a									=				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						, dtype=np.intaa							)
								a									=				shift_tokens_right(__magic_name__						, 1						, 2							)
								a									=				np.equal(__magic_name__						, 1							).astype(np.floataa							).sum()
								a									=				np.equal(__magic_name__						, 1							).astype(np.floataa							).sum()
								self.assertEqual(shifted.shape						, input_ids.shape							)
								self.assertEqual(__magic_name__						, n_pad_before - 1							)
								self.assertTrue(np.equal(shifted[:, 0]						, 2							).all()							)







@require_flax
class 						__lowerCAmelCase      (      __magic_name__ ,			unittest.TestCase ,			__magic_name__					):
							UpperCamelCase__   	=    True
							UpperCamelCase__   	=    (
							    (
							        FlaxBlenderbotModel,
							        FlaxBlenderbotForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							UpperCamelCase__   	=    (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				FlaxBlenderbotModelTester(self							)
							def  lowerCamelCase__				(		self		: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(__magic_name__						, __magic_name__						, __magic_name__							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''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(__magic_name__						, __magic_name__						, __magic_name__							)
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a							,					a									=				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
										a									=				self._prepare_for_class(__magic_name__						, __magic_name__							)
										a									=				model_class(__magic_name__							)

										@jax.jit
										def encode_jitted(__magic_name__		:int						, __magic_name__		:Tuple=None						, **__magic_name__		:List[str]							):
											return model.encode(input_ids=__magic_name__						, attention_mask=__magic_name__							)

										with self.subTest("""JIT Enabled"""							):
											a									=				encode_jitted(**__magic_name__							).to_tuple()

										with self.subTest("""JIT Disabled"""							):
											with jax.disable_jit():
												a									=				encode_jitted(**__magic_name__							).to_tuple()

										self.assertEqual(len(__magic_name__							)						, len(__magic_name__							)							)
										for jitted_output, output in zip(__magic_name__						, __magic_name__							):
											self.assertEqual(jitted_output.shape						, output.shape							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a							,					a									=				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
										a									=				model_class(__magic_name__							)
										a									=				model.encode(inputs_dict["""input_ids"""]						, inputs_dict["""attention_mask"""]							)

										a									=				{
										    """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(__magic_name__		:Any						, __magic_name__		:Tuple						, __magic_name__		:str							):
											return model.decode(
											    decoder_input_ids=__magic_name__						, decoder_attention_mask=__magic_name__						, encoder_outputs=__magic_name__						, )

										with self.subTest("""JIT Enabled"""							):
											a									=				decode_jitted(**__magic_name__							).to_tuple()

										with self.subTest("""JIT Disabled"""							):
											with jax.disable_jit():
												a									=				decode_jitted(**__magic_name__							).to_tuple()

										self.assertEqual(len(__magic_name__							)						, len(__magic_name__							)							)
										for jitted_output, output in zip(__magic_name__						, __magic_name__							):
											self.assertEqual(jitted_output.shape						, output.shape							)
							@slow
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								for model_class_name in self.all_model_classes:
									a									=				model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""							)
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									a									=				np.ones((1, 1)							) * model.config.eos_token_id
									a									=				model(__magic_name__							)
									self.assertIsNotNone(__magic_name__							)





							@unittest.skipUnless(jax_device != """cpu"""						, """3B test too slow on CPU."""							)
							@slow
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
								a									=				{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

								a									=				FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""						, from_pt=__magic_name__							)
								a									=				BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""							)

								a									=				["""Sam"""]
								a									=				tokenizer(__magic_name__						, return_tensors="""jax"""							)

								a									=				model.generate(**__magic_name__						, **__magic_name__							)
								a									=				"""Sam is a great name. It means \"sun\" in Gaelic."""

								a									=				tokenizer.batch_decode(__magic_name__						, **__magic_name__							)
								assert generated_txt[0].strip() == tgt_text







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase   :			Optional[int]          = {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase   :			int          = [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

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

