
"""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
					UpperCAmelCase__      :    Union[str, 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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case=None   ,_snake_case=None   ,_snake_case=None   ,_snake_case=None   ,_snake_case=None   ,_snake_case=None   ,):
							if attention_mask is None:
														SCREAMING_SNAKE_CASE__						:       str           =   np.where(input_ids != config.pad_token_id   ,1   ,0						)
							if decoder_attention_mask is None:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   np.where(decoder_input_ids != config.pad_token_id   ,1   ,0						)
							if head_mask is None:
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   np.ones((config.encoder_layers, config.encoder_attention_heads)						)
							if decoder_head_mask is None:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   np.ones((config.decoder_layers, config.decoder_attention_heads)						)
							if cross_attn_head_mask is None:
														SCREAMING_SNAKE_CASE__						:       Any           =   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_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=13   ,       SCREAMING_SNAKE_CASE__=7   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=99   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=32   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=0   ,       SCREAMING_SNAKE_CASE__=0.02   ,       )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   parent
											SCREAMING_SNAKE_CASE__						:       Tuple           =   batch_size
											SCREAMING_SNAKE_CASE__						:       Dict           =   seq_length
											SCREAMING_SNAKE_CASE__						:       List[str]           =   is_training
											SCREAMING_SNAKE_CASE__						:       List[str]           =   use_labels
											SCREAMING_SNAKE_CASE__						:       Any           =   vocab_size
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   hidden_size
											SCREAMING_SNAKE_CASE__						:       Any           =   num_hidden_layers
											SCREAMING_SNAKE_CASE__						:       int           =   num_attention_heads
											SCREAMING_SNAKE_CASE__						:       List[str]           =   intermediate_size
											SCREAMING_SNAKE_CASE__						:       str           =   hidden_act
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   hidden_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Tuple           =   max_position_embeddings
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   eos_token_id
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   pad_token_id
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   bos_token_id
											SCREAMING_SNAKE_CASE__						:       Any           =   initializer_range



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__						:       Dict           =   shift_tokens_right(SCREAMING_SNAKE_CASE__   ,       1   ,       2    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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=SCREAMING_SNAKE_CASE__   ,       )
											SCREAMING_SNAKE_CASE__						:       Dict           =   prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											return config, inputs_dict



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[str]           =   self.prepare_config_and_inputs()
											return config, inputs_dict



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   20
											SCREAMING_SNAKE_CASE__						:       int           =   model_class_name(SCREAMING_SNAKE_CASE__    )

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

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

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

											SCREAMING_SNAKE_CASE__						:       Dict           =   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__						:       Optional[int]           =   model.decode(
											    decoder_input_ids[:, :-1]   ,       SCREAMING_SNAKE_CASE__   ,       decoder_attention_mask=SCREAMING_SNAKE_CASE__   ,       past_key_values=SCREAMING_SNAKE_CASE__   ,       decoder_position_ids=SCREAMING_SNAKE_CASE__   ,       )

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

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   model.decode(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   20
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   model_class_name(SCREAMING_SNAKE_CASE__    )

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

											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   (
											    inputs_dict["""decoder_input_ids"""],
											    inputs_dict["""decoder_attention_mask"""],
											)

											SCREAMING_SNAKE_CASE__						:       Tuple           =   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__						:       Optional[int]           =   model.init_cache(decoder_input_ids.shape[0]   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   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__						:       Optional[Any]           =   model.decode(
											    decoder_input_ids[:, :-1]   ,       SCREAMING_SNAKE_CASE__   ,       decoder_attention_mask=SCREAMING_SNAKE_CASE__   ,       past_key_values=SCREAMING_SNAKE_CASE__   ,       decoder_position_ids=SCREAMING_SNAKE_CASE__   ,       )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,       dtype="""i4"""    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   model.decode(
											    decoder_input_ids[:, -1:]   ,       SCREAMING_SNAKE_CASE__   ,       past_key_values=outputs_cache.past_key_values   ,       decoder_attention_mask=SCREAMING_SNAKE_CASE__   ,       decoder_position_ids=SCREAMING_SNAKE_CASE__   ,       )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   model.decode(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       decoder_attention_mask=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[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}'''    )






@require_flax
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Dict					       =   99



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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   ,       )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   input_ids.shape[0]
											SCREAMING_SNAKE_CASE__						:       List[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 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self._get_config_and_data()
											SCREAMING_SNAKE_CASE__						:       str           =   FlaxBlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   lm_model(input_ids=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   (batch_size, input_ids.shape[1], config.vocab_size)
											self.assertEqual(outputs["""logits"""].shape   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




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






@require_flax
class 	lowerCAmelCase_						(a__ ,					unittest.TestCase ,					a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Any					       =   True
				__UpperCamelCase      :						Union[str, Any]					       =   (
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				__UpperCamelCase      :						Union[str, Any]					       =   (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   FlaxBlenderbotModelTester(self    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   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(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Tuple           =   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__						:       str           =   self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   model_class(SCREAMING_SNAKE_CASE__    )

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

																									with self.subTest("""JIT Enabled"""    ):
																																SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   encode_jitted(**SCREAMING_SNAKE_CASE__    ).to_tuple()

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

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



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       int           =   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__						:       Optional[Any]           =   model_class(SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Dict           =   model.encode(inputs_dict["""input_ids"""]   ,       inputs_dict["""attention_mask"""]    )

																									SCREAMING_SNAKE_CASE__						:       Any           =   {
																									    """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(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																																return model.decode(
																																    decoder_input_ids=SCREAMING_SNAKE_CASE__   ,       decoder_attention_mask=SCREAMING_SNAKE_CASE__   ,       encoder_outputs=SCREAMING_SNAKE_CASE__   ,       )

																									with self.subTest("""JIT Enabled"""    ):
																																SCREAMING_SNAKE_CASE__						:       List[Any]           =   decode_jitted(**SCREAMING_SNAKE_CASE__    ).to_tuple()

																									with self.subTest("""JIT Disabled"""    ):
																																with jax.disable_jit():
																																							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   decode_jitted(**SCREAMING_SNAKE_CASE__    ).to_tuple()

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



				@slow
				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




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



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



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""   ,       from_pt=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   ["""Sam"""]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       return_tensors="""jax"""    )

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

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






"""simple docstring"""




import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase__      :    List[Any]    							= logging.getLogger()

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """\n""".join(_snake_case						)
							Path(_snake_case						).open("""w"""						).writelines(_snake_case						)


UpperCAmelCase__      :    Union[str, Any]    							= 'patrickvonplaten/t5-tiny-random'
UpperCAmelCase__      :    Optional[int]    							= 'sshleifer/bart-tiny-random'
UpperCAmelCase__      :    Dict    							= 'sshleifer/tiny-mbart'

UpperCAmelCase__      :    int    							= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   Path(self.get_auto_remove_tmp_dir()    ) / """utest_input.source"""
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   input_file_name.parent / """utest_output.txt"""
											assert not output_file_name.exists()
											SCREAMING_SNAKE_CASE__						:       str           =   [""" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."""]
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Any           =   str(Path(self.get_auto_remove_tmp_dir()    ) / """scores.json"""    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """translation_en_to_de""" if model == T5_TINY else """summarization"""
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   F'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

											with patch.object(SCREAMING_SNAKE_CASE__   ,       """argv"""   ,       SCREAMING_SNAKE_CASE__    ):
																		run_generate()
																		assert Path(SCREAMING_SNAKE_CASE__    ).exists()
																		# os.remove(Path(output_file_name))



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



				@parameterized.expand([BART_TINY, MBART_TINY]    )
				@slow
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



				@parameterized.expand([T5_TINY, MBART_TINY]    )
				@slow
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   Path(self.get_auto_remove_tmp_dir()    ) / """utest_input.source"""
											SCREAMING_SNAKE_CASE__						:       int           =   input_file_name.parent / """utest_output.txt"""
											assert not output_file_name.exists()

											SCREAMING_SNAKE_CASE__						:       Any           =   {
											    """en""": ["""Machine learning is great, isn't it?""", """I like to eat bananas""", """Tomorrow is another great day!"""],
											    """de""": [
											        """Maschinelles Lernen ist großartig, oder?""",
											        """Ich esse gerne Bananen""",
											        """Morgen ist wieder ein toller Tag!""",
											    ],
											}

											SCREAMING_SNAKE_CASE__						:       List[str]           =   Path(self.get_auto_remove_tmp_dir()    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   str(tmp_dir / """scores.json"""    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   str(tmp_dir / """val.target"""    )
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       text["""en"""]    )
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       text["""de"""]    )
											SCREAMING_SNAKE_CASE__						:       str           =   """translation_en_to_de""" if model == T5_TINY else """summarization"""
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   F'''
            run_eval_search.py
            {model}
            {str(SCREAMING_SNAKE_CASE__    )}
            {str(SCREAMING_SNAKE_CASE__    )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
											testargs.extend(["""--search""", """num_beams=1:2 length_penalty=0.9:1.0"""]    )

											with patch.object(SCREAMING_SNAKE_CASE__   ,       """argv"""   ,       SCREAMING_SNAKE_CASE__    ):
																		with CaptureStdout() as cs:
																									run_search()
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [""" num_beams | length_penalty""", model, """Best score args"""]
																		SCREAMING_SNAKE_CASE__						:       Any           =   ["""Info"""]
																		if "translation" in task:
																									expected_strings.append("""bleu"""    )
																		else:
																									expected_strings.extend(SCREAMING_SNAKE_CASE__    )
																		for w in expected_strings:
																									assert w in cs.out
																		for w in un_expected_strings:
																									assert w not in cs.out
																		assert Path(SCREAMING_SNAKE_CASE__    ).exists()
																		os.remove(Path(SCREAMING_SNAKE_CASE__    )    )




