


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
			__snake_case            =   "platform"

			import jax
			import jax.numpy as jnp

			from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
			    FlaxBlenderbotSmallForConditionalGeneration,
			    FlaxBlenderbotSmallModel,
			    shift_tokens_right,
			)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]=None       ,  SCREAMING_SNAKE_CASE_   :		List[str]=None       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]=None       ,  SCREAMING_SNAKE_CASE_   :		Dict=None       ,  SCREAMING_SNAKE_CASE_   :		Any=None       ,  SCREAMING_SNAKE_CASE_   :		str=None       ,  ):





			"""simple docstring"""




			if attention_mask is None:
						UpperCamelCase         =					np.where(input_ids != config.pad_token_id       ,  1       ,  0	)
			if decoder_attention_mask is None:
						UpperCamelCase         =					np.where(decoder_input_ids != config.pad_token_id       ,  1       ,  0	)
			if head_mask is None:
						UpperCamelCase         =					np.ones((config.encoder_layers, config.encoder_attention_heads)	)
			if decoder_head_mask is None:
						UpperCamelCase         =					np.ones((config.decoder_layers, config.decoder_attention_heads)	)
			if cross_attn_head_mask is None:
						UpperCamelCase         =					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    UpperCAmelCase					:

			'''simple docstring'''





			def __init__(						self							:		Any       ,       __magic_name__							:		Any       ,       __magic_name__							:		int=1_3       ,       __magic_name__							:		Any=7       ,       __magic_name__							:		int=True       ,       __magic_name__							:		Optional[int]=False       ,       __magic_name__							:		Tuple=9_9       ,       __magic_name__							:		Optional[int]=1_6       ,       __magic_name__							:		Any=2       ,       __magic_name__							:		Union[str, Any]=4       ,       __magic_name__							:		List[str]=4       ,       __magic_name__							:		int="gelu"       ,       __magic_name__							:		List[Any]=0.1       ,       __magic_name__							:		int=0.1       ,       __magic_name__							:		List[Any]=3_2       ,       __magic_name__							:		Optional[int]=2       ,       __magic_name__							:		Any=1       ,       __magic_name__							:		str=0       ,       __magic_name__							:		List[Any]=0.02       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					parent
						UpperCamelCase         =					batch_size
						UpperCamelCase         =					seq_length
						UpperCamelCase         =					is_training
						UpperCamelCase         =					use_labels
						UpperCamelCase         =					vocab_size
						UpperCamelCase         =					hidden_size
						UpperCamelCase         =					num_hidden_layers
						UpperCamelCase         =					num_attention_heads
						UpperCamelCase         =					intermediate_size
						UpperCamelCase         =					hidden_act
						UpperCamelCase         =					hidden_dropout_prob
						UpperCamelCase         =					attention_probs_dropout_prob
						UpperCamelCase         =					max_position_embeddings
						UpperCamelCase         =					eos_token_id
						UpperCamelCase         =					pad_token_id
						UpperCamelCase         =					bos_token_id
						UpperCamelCase         =					initializer_range




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







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

						UpperCamelCase         =					shift_tokens_right(__magic_name__       ,       1       ,       2						)

						UpperCamelCase         =					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=__magic_name__       ,       )
						UpperCamelCase         =					prepare_blenderbot_inputs_dict(__magic_name__       ,       __magic_name__       ,       __magic_name__						)
						return config, inputs_dict




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		str       ,       __magic_name__							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					2_0
						UpperCamelCase         =					model_class_name(__magic_name__						)

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

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

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

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

						UpperCamelCase         =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,       dtype="""i4"""						)
						UpperCamelCase         =					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__       ,       )

						UpperCamelCase         =					model.decode(__magic_name__       ,       __magic_name__						)

						UpperCamelCase         =					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							:		Dict       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					2_0
						UpperCamelCase         =					model_class_name(__magic_name__						)

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

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

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

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

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

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

						UpperCamelCase         =					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    UpperCAmelCase					(       unittest.TestCase    ):

			'''simple docstring'''





			lowercase						= 9_9




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					np.array(
						    [
						        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
						        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
						        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
						        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
						        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
						        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
						        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
						        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
						        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
						        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
						        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
						        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
						        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
						    ]       ,       dtype=np.intaa       ,       )

						UpperCamelCase         =					input_ids.shape[0]
						UpperCamelCase         =					BlenderbotSmallConfig(
						    vocab_size=self.vocab_size       ,       d_model=2_4       ,       encoder_layers=2       ,       decoder_layers=2       ,       encoder_attention_heads=2       ,       decoder_attention_heads=2       ,       encoder_ffn_dim=3_2       ,       decoder_ffn_dim=3_2       ,       max_position_embeddings=4_8       ,       eos_token_id=2       ,       pad_token_id=1       ,       bos_token_id=0       ,       )
						return config, input_ids, batch_size




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					self._get_config_and_data()
						UpperCamelCase         =					FlaxBlenderbotSmallForConditionalGeneration(__magic_name__						)
						UpperCamelCase         =					lm_model(input_ids=__magic_name__						)
						UpperCamelCase         =					(batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					BlenderbotSmallConfig(
						    vocab_size=self.vocab_size       ,       d_model=1_4       ,       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=4_8       ,       )
						UpperCamelCase         =					FlaxBlenderbotSmallForConditionalGeneration(__magic_name__						)
						UpperCamelCase         =					np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]       ,       dtype=np.intaa						)
						UpperCamelCase         =					np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]       ,       dtype=np.intaa						)
						UpperCamelCase         =					lm_model(input_ids=__magic_name__       ,       decoder_input_ids=__magic_name__						)
						UpperCamelCase         =					(*summary.shape, config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape       ,       __magic_name__						)





			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]       ,       dtype=np.intaa						)
						UpperCamelCase         =					shift_tokens_right(__magic_name__       ,       1       ,       2						)
						UpperCamelCase         =					np.equal(__magic_name__       ,       1						).astype(np.floataa						).sum()
						UpperCamelCase         =					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    UpperCAmelCase					(       __snake_case      , unittest.TestCase      , __snake_case    ):

			'''simple docstring'''





			lowercase						= True
			lowercase						= (
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			lowercase						= (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					FlaxBlenderbotSmallModelTester(self						)




			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase         =					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							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase         =					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							:		Dict						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

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

												@jax.jit
												def encode_jitted(__magic_name__							:		Dict       ,       __magic_name__							:		Union[str, Any]=None       ,       **__magic_name__							:		str						):
															return model.encode(input_ids=__magic_name__       ,       attention_mask=__magic_name__						)

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

												with self.subTest("""JIT Disabled"""						):
															with jax.disable_jit():
																		UpperCamelCase         =					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							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCamelCase         =					{
												    """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__							:		Optional[Any]       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		Any						):
															return model.decode(
															    decoder_input_ids=__magic_name__       ,       decoder_attention_mask=__magic_name__       ,       encoder_outputs=__magic_name__       ,       )

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

												with self.subTest("""JIT Disabled"""						):
															with jax.disable_jit():
																		UpperCamelCase         =					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							:		int						):







						"""simple docstring"""







						for model_class_name in self.all_model_classes:
									UpperCamelCase         =					model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""						)
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									UpperCamelCase         =					np.ones((1, 1)						) * model.config.eos_token_id
									UpperCamelCase         =					model(__magic_name__						)
									self.assertIsNotNone(__magic_name__						)








from ..utils import DummyObject, requires_backends




class    UpperCAmelCase					(       metaclass=__snake_case    ):
			lowercase						= ["""keras_nlp"""]




			def __init__(						self							:		List[str]       ,       *__magic_name__							:		str       ,       **__magic_name__							:		int						):







						"""simple docstring"""







						requires_backends(self       ,       ["""keras_nlp"""]						)




