




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
		__lowerCAmelCase	:       Optional[int]     =	'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     __magic_name__       (				A	:      Optional[int],						A	:      Optional[int],						A	:      Any=None,						A	:      int=None,						A	:      List[str]=None,						A	:      str=None,						A	:      Dict=None,						A	:      Union[str, Any]=None,						):






			'''simple docstring'''







			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 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  List[Any]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Tuple=13      ,   __lowerCamelCase       :  Dict=7      ,   __lowerCamelCase       :  Tuple=True      ,   __lowerCamelCase       :  Optional[int]=False      ,   __lowerCamelCase       :  Any=99      ,   __lowerCamelCase       :  int=16      ,   __lowerCamelCase       :  Any=2      ,   __lowerCamelCase       :  Optional[Any]=4      ,   __lowerCamelCase       :  Optional[int]=4      ,   __lowerCamelCase       :  List[Any]="gelu"      ,   __lowerCamelCase       :  Dict=0.1      ,   __lowerCamelCase       :  Union[str, Any]=0.1      ,   __lowerCamelCase       :  Any=32      ,   __lowerCamelCase       :  Optional[Any]=2      ,   __lowerCamelCase       :  List[Any]=1      ,   __lowerCamelCase       :  Dict=0      ,   __lowerCamelCase       :  Any=0.02      ,   )      ->  List[str]:
							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 							__UpperCAmelCase							(   self       :  int							)      ->  Optional[int]:
							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(lowerCamelCase_      ,   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=lowerCamelCase_      ,   )
							a					=    prepare_blenderbot_inputs_dict(lowerCamelCase_      ,   lowerCamelCase_      ,   lowerCamelCase_							)
							return config, inputs_dict





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  List[Any]:
							a					,	a					=    self.prepare_config_and_inputs()
							return config, inputs_dict





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Tuple							)      ->  str:
							a					=    20
							a					=    model_class_name(lowerCamelCase_							)

							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]      ,   lowerCamelCase_      ,   lowerCamelCase_							)
							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]      ,   lowerCamelCase_      ,   decoder_attention_mask=lowerCamelCase_      ,   past_key_values=lowerCamelCase_      ,   decoder_position_ids=lowerCamelCase_      ,   )

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

							a					=    model.decode(lowerCamelCase_      ,   lowerCamelCase_							)

							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 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  Any							)      ->  Any:
							a					=    20
							a					=    model_class_name(lowerCamelCase_							)

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

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

							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 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							str											=      99





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  List[str]:
							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 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Union[str, Any]:
							a					,	a					,	a					=    self._get_config_and_data()
							a					=    FlaxBlenderbotForConditionalGeneration(lowerCamelCase_							)
							a					=    lm_model(input_ids=lowerCamelCase_							)
							a					=    (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["logits"].shape      ,   lowerCamelCase_							)





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Tuple:
							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(lowerCamelCase_							)
							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=lowerCamelCase_      ,   decoder_input_ids=lowerCamelCase_							)
							a					=    (*summary.shape, config.vocab_size)
							self.assertEqual(outputs["logits"].shape      ,   lowerCamelCase_							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  List[Any]:
							a					=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,   dtype=np.intaa							)
							a					=    shift_tokens_right(lowerCamelCase_      ,   1      ,   2							)
							a					=    np.equal(lowerCamelCase_      ,   1							).astype(np.floataa							).sum()
							a					=    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 					snake_case__							(a__					, unittest.TestCase					, a__     ):
				"""simple docstring"""



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





				def 							__UpperCAmelCase							(   self       :  int							)      ->  Optional[Any]:
							a					=    FlaxBlenderbotModelTester(self							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  int:
							a					,	a					=    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 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Union[str, Any]:
							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(lowerCamelCase_      ,   lowerCamelCase_      ,   lowerCamelCase_							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Dict:
							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(lowerCamelCase_      ,   lowerCamelCase_							)
													a					=    model_class(lowerCamelCase_							)

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

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

													with self.subTest("JIT Disabled"							):
																with jax.disable_jit():
																			a					=    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 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Union[str, Any]:
							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(lowerCamelCase_							)
													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(__lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  str							):
																return model.decode(
																    decoder_input_ids=lowerCamelCase_      ,   decoder_attention_mask=lowerCamelCase_      ,   encoder_outputs=lowerCamelCase_      ,   )

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

													with self.subTest("JIT Disabled"							):
																with jax.disable_jit():
																			a					=    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 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Union[str, Any]:
							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(lowerCamelCase_							)
										self.assertIsNotNone(lowerCamelCase_							)





				@unittest.skipUnless(jax_device != "cpu"      ,   "3B test too slow on CPU."							)
				@slow
				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Any:
							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=lowerCamelCase_							)
							a					=    BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"							)

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

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

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












from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__lowerCAmelCase	:       List[Any]     =	logging.get_logger(__name__)


__lowerCAmelCase	:       List[Any]     =	TypeVar('DatasetType', Dataset, IterableDataset)




def     __magic_name__       (				A	:      List[DatasetType],						A	:      Optional[List[float]] = None,						A	:      Optional[int] = None,						A	:      Optional[DatasetInfo] = None,						A	:      Optional[NamedSplit] = None,						A	:      Literal["first_exhausted", "all_exhausted"] = "first_exhausted",						):






			'''simple docstring'''







			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("Unable to interleave an empty list of datasets."     )
			for i, dataset in enumerate(A     ):
						if not isinstance(A,						(Dataset, IterableDataset)     ):
									if isinstance(A,						(DatasetDict, IterableDatasetDict)     ):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."     )
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(A     )}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(A     )     )}']"""     )
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A     ).__name__}."""     )
						if i == 0:
									a					,	a					=    (
									    (Dataset, IterableDataset) if isinstance(A,						A     ) else (IterableDataset, Dataset)
									)
						elif not isinstance(A,						A     ):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""     )
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    A,						A,						A,						info=A,						split=A,						stopping_strategy=A     )
			else:
						return _interleave_iterable_datasets(
						    A,						A,						A,						info=A,						split=A,						stopping_strategy=A     )






def     __magic_name__       (				A	:      List[DatasetType],						A	:      Optional[DatasetInfo] = None,						A	:      Optional[NamedSplit] = None,						A	:      int = 0,						):






			'''simple docstring'''







			if not dsets:
						raise ValueError("Unable to concatenate an empty list of datasets."     )
			for i, dataset in enumerate(A     ):
						if not isinstance(A,						(Dataset, IterableDataset)     ):
									if isinstance(A,						(DatasetDict, IterableDatasetDict)     ):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."     )
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(A     )}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(A     )     )}']"""     )
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A     ).__name__}."""     )
						if i == 0:
									a					,	a					=    (
									    (Dataset, IterableDataset) if isinstance(A,						A     ) else (IterableDataset, Dataset)
									)
						elif not isinstance(A,						A     ):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(A,						info=A,						split=A,						axis=A     )
			else:
						return _concatenate_iterable_datasets(A,						info=A,						split=A,						axis=A     )






