

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_							:			List[str]											= '''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       (     snake_case__		,				snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=None		,				snake_case__=None		,				snake_case__=None		,				snake_case__=None		,				):




			'''simple docstring'''


			if attention_mask is None:
						SCREAMING_SNAKE_CASE__								=      np.where(input_ids != config.pad_token_id		,				1		,				0    )
			if decoder_attention_mask is None:
						SCREAMING_SNAKE_CASE__								=      np.where(decoder_input_ids != config.pad_token_id		,				1		,				0    )
			if head_mask is None:
						SCREAMING_SNAKE_CASE__								=      np.ones((config.encoder_layers, config.encoder_attention_heads)    )
			if decoder_head_mask is None:
						SCREAMING_SNAKE_CASE__								=      np.ones((config.decoder_layers, config.decoder_attention_heads)    )
			if cross_attn_head_mask is None:
						SCREAMING_SNAKE_CASE__								=      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				:						int    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						List[str]=1_3    ,				__UpperCAmelCase				:						Tuple=7    ,				__UpperCAmelCase				:						Dict=True    ,				__UpperCAmelCase				:						str=False    ,				__UpperCAmelCase				:						Optional[Any]=9_9    ,				__UpperCAmelCase				:						Optional[Any]=1_6    ,				__UpperCAmelCase				:						Optional[int]=2    ,				__UpperCAmelCase				:						List[str]=4    ,				__UpperCAmelCase				:						List[Any]=4    ,				__UpperCAmelCase				:						Any="gelu"    ,				__UpperCAmelCase				:						Dict=0.1    ,				__UpperCAmelCase				:						Optional[Any]=0.1    ,				__UpperCAmelCase				:						List[Any]=3_2    ,				__UpperCAmelCase				:						int=2    ,				__UpperCAmelCase				:						int=1    ,				__UpperCAmelCase				:						Optional[int]=0    ,				__UpperCAmelCase				:						List[str]=0.02    ,				)   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      seq_length
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      intermediate_size
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      max_position_embeddings
						SCREAMING_SNAKE_CASE__								=      eos_token_id
						SCREAMING_SNAKE_CASE__								=      pad_token_id
						SCREAMING_SNAKE_CASE__								=      bos_token_id
						SCREAMING_SNAKE_CASE__								=      initializer_range

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

						SCREAMING_SNAKE_CASE__								=      shift_tokens_right(lowerCAmelCase__    ,				1    ,				2       )

						SCREAMING_SNAKE_CASE__								=      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=lowerCAmelCase__    ,				)
						SCREAMING_SNAKE_CASE__								=      prepare_blenderbot_inputs_dict(lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__       )
						return config, inputs_dict

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						return config, inputs_dict

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						Dict    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						List[str]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      2_0
						SCREAMING_SNAKE_CASE__								=      model_class_name(lowerCAmelCase__       )

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

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

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

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

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

						SCREAMING_SNAKE_CASE__								=      model.decode(lowerCAmelCase__    ,				lowerCAmelCase__       )

						SCREAMING_SNAKE_CASE__								=      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 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						str       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      2_0
						SCREAMING_SNAKE_CASE__								=      model_class_name(lowerCAmelCase__       )

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

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

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

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

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

						SCREAMING_SNAKE_CASE__								=      model.decode(lowerCAmelCase__    ,				lowerCAmelCase__    ,				decoder_attention_mask=lowerCAmelCase__       )

						SCREAMING_SNAKE_CASE__								=      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			):
			lowerCamelCase__   :		int							=							9_9

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      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    ,				)

						SCREAMING_SNAKE_CASE__								=      input_ids.shape[0]
						SCREAMING_SNAKE_CASE__								=      BlenderbotConfig(
						    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 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      self._get_config_and_data()
						SCREAMING_SNAKE_CASE__								=      FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__       )
						SCREAMING_SNAKE_CASE__								=      lm_model(input_ids=lowerCAmelCase__       )
						SCREAMING_SNAKE_CASE__								=      (batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape    ,				lowerCAmelCase__       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      BlenderbotConfig(
						    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    ,				)
						SCREAMING_SNAKE_CASE__								=      FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__       )
						SCREAMING_SNAKE_CASE__								=      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       )
						SCREAMING_SNAKE_CASE__								=      np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]    ,				dtype=np.intaa       )
						SCREAMING_SNAKE_CASE__								=      lm_model(input_ids=lowerCAmelCase__    ,				decoder_input_ids=lowerCAmelCase__       )
						SCREAMING_SNAKE_CASE__								=      (*summary.shape, config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape    ,				lowerCAmelCase__       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      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       )
						SCREAMING_SNAKE_CASE__								=      shift_tokens_right(lowerCAmelCase__    ,				1    ,				2       )
						SCREAMING_SNAKE_CASE__								=      np.equal(lowerCAmelCase__    ,				1       ).astype(np.floataa       ).sum()
						SCREAMING_SNAKE_CASE__								=      np.equal(lowerCAmelCase__    ,				1       ).astype(np.floataa       ).sum()
						self.assertEqual(shifted.shape    ,				input_ids.shape       )
						self.assertEqual(lowerCAmelCase__    ,				n_pad_before - 1       )
						self.assertTrue(np.equal(shifted[:, 0]    ,				2       ).all()       )





@require_flax
class    lowerCamelCase						(a__			,unittest.TestCase			,a__			):
			lowerCamelCase__   :		Optional[int]							=							True
			lowerCamelCase__   :		Dict							=							(
			    (
			        FlaxBlenderbotModel,
			        FlaxBlenderbotForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			lowerCamelCase__   :		Dict							=							(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      FlaxBlenderbotModelTester(self       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      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(lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__       ):
												SCREAMING_SNAKE_CASE__								=      self._prepare_for_class(lowerCAmelCase__    ,				lowerCAmelCase__       )
												SCREAMING_SNAKE_CASE__								=      model_class(lowerCAmelCase__       )

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

												with self.subTest("""JIT Enabled"""       ):
															SCREAMING_SNAKE_CASE__								=      encode_jitted(**lowerCAmelCase__       ).to_tuple()

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

												SCREAMING_SNAKE_CASE__								=      {
												    "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				:						str    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Any       ):
															return model.decode(
															    decoder_input_ids=lowerCAmelCase__    ,				decoder_attention_mask=lowerCAmelCase__    ,				encoder_outputs=lowerCAmelCase__    ,				)

												with self.subTest("""JIT Enabled"""       ):
															SCREAMING_SNAKE_CASE__								=      decode_jitted(**lowerCAmelCase__       ).to_tuple()

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

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

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Tuple:
						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""       )
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									SCREAMING_SNAKE_CASE__								=      np.ones((1, 1)       ) * model.config.eos_token_id
									SCREAMING_SNAKE_CASE__								=      model(lowerCAmelCase__       )
									self.assertIsNotNone(lowerCAmelCase__       )





			@unittest.skipUnless(jax_device != """cpu"""    ,				"""3B test too slow on CPU."""       )
			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      {"num_beams": 1, "early_stopping": True, "min_length": 1_5, "max_length": 2_5}
						SCREAMING_SNAKE_CASE__								=      {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

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

						SCREAMING_SNAKE_CASE__								=      ["Sam"]
						SCREAMING_SNAKE_CASE__								=      tokenizer(lowerCAmelCase__    ,				return_tensors="""jax"""       )

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

						SCREAMING_SNAKE_CASE__								=      tokenizer.batch_decode(lowerCAmelCase__    ,				**lowerCAmelCase__       )
						assert generated_txt[0].strip() == tgt_text





"""simple docstring"""




def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not nums:  # Makes sure that the list is not empty
						raise ValueError("""List is empty"""    )

			SCREAMING_SNAKE_CASE__								=      sum(snake_case__    ) / len(snake_case__    )  # Calculate the average
			return sum(abs(x - average    ) for x in nums    ) / len(snake_case__    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()


