

"""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
						snake_case_   :   Optional[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_	(    _lowercase       :		str							,      _lowercase       :		Tuple							,      _lowercase       :		Optional[int]=None							,      _lowercase       :		int=None							,      _lowercase       :		Optional[int]=None							,      _lowercase       :		Union[str, Any]=None							,      _lowercase       :		List[str]=None							,      _lowercase       :		Union[str, Any]=None							,      ):




					'''simple docstring'''






					if attention_mask is None:
										UpperCAmelCase			:   Tuple      =				np.where(input_ids != config.pad_token_id							,      1							,      0	)
					if decoder_attention_mask is None:
										UpperCAmelCase			:   Any      =				np.where(decoder_input_ids != config.pad_token_id							,      1							,      0	)
					if head_mask is None:
										UpperCAmelCase			:   List[Any]      =				np.ones((config.encoder_layers, config.encoder_attention_heads)	)
					if decoder_head_mask is None:
										UpperCAmelCase			:   str      =				np.ones((config.decoder_layers, config.decoder_attention_heads)	)
					if cross_attn_head_mask is None:
										UpperCAmelCase			:   List[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       snake_case__  :







			def __init__(			self			: int			,  lowercase			: List[str]			,  lowercase			: Optional[int]=13			,  lowercase			: List[str]=7			,  lowercase			: Tuple=True			,  lowercase			: str=False			,  lowercase			: List[Any]=99			,  lowercase			: Optional[Any]=16			,  lowercase			: Union[str, Any]=2			,  lowercase			: List[str]=4			,  lowercase			: Optional[int]=4			,  lowercase			: List[str]="gelu"			,  lowercase			: int=0.1			,  lowercase			: str=0.1			,  lowercase			: List[Any]=32			,  lowercase			: Any=2			,  lowercase			: Tuple=1			,  lowercase			: List[str]=0			,  lowercase			: Any=0.0_2			,  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



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

								UpperCAmelCase			:   Optional[Any]      =				shift_tokens_right(lowercase			,  1			,  2  )

								UpperCAmelCase			:   Any      =				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=lowercase			,  )
								UpperCAmelCase			:   List[str]      =				prepare_blenderbot_inputs_dict(lowercase			,  lowercase			,  lowercase  )
								return config, inputs_dict







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase      ,							UpperCAmelCase			:   int      =				self.prepare_config_and_inputs()
								return config, inputs_dict







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: Optional[int]			,  lowercase			: Any			,  lowercase			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Any      =				20
								UpperCAmelCase			:   Any      =				model_class_name(lowercase  )

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

								UpperCAmelCase      ,							UpperCAmelCase			:   Optional[Any]      =				(
								    inputs_dict["decoder_input_ids"],
								    inputs_dict["decoder_attention_mask"],
								)

								UpperCAmelCase			:   Union[str, Any]      =				model.init_cache(decoder_input_ids.shape[0]			,  lowercase			,  lowercase  )
								UpperCAmelCase			:   Any      =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,  dtype="i4"  )

								UpperCAmelCase			:   Tuple      =				jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]			,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,  )
								UpperCAmelCase			:   Optional[int]      =				model.decode(
								    decoder_input_ids[:, :-1]			,  lowercase			,  decoder_attention_mask=lowercase			,  past_key_values=lowercase			,  decoder_position_ids=lowercase			,  )

								UpperCAmelCase			:   Union[str, Any]      =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,  dtype="i4"  )
								UpperCAmelCase			:   Optional[int]      =				model.decode(
								    decoder_input_ids[:, -1:]			,  lowercase			,  decoder_attention_mask=lowercase			,  past_key_values=outputs_cache.past_key_values			,  decoder_position_ids=lowercase			,  )

								UpperCAmelCase			:   List[str]      =				model.decode(lowercase			,  lowercase  )

								UpperCAmelCase			:   Optional[Any]      =				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			: Tuple			,  lowercase			: Tuple			,  lowercase			: Dict			,  lowercase			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				20
								UpperCAmelCase			:   Dict      =				model_class_name(lowercase  )

								UpperCAmelCase			:   Tuple      =				model.encode(inputs_dict["input_ids"]  )

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

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

								UpperCAmelCase			:   Optional[Any]      =				model.init_cache(decoder_input_ids.shape[0]			,  lowercase			,  lowercase  )
								UpperCAmelCase			:   str      =				jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]			,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,  )

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

								UpperCAmelCase			:   Tuple      =				model.decode(lowercase			,  lowercase			,  decoder_attention_mask=lowercase  )

								UpperCAmelCase			:   Tuple      =				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     ):
			SCREAMING_SNAKE_CASE__     				=						99







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				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			,  )

								UpperCAmelCase			:   Tuple      =				input_ids.shape[0]
								UpperCAmelCase			:   Tuple      =				BlenderbotConfig(
								    vocab_size=self.vocab_size			,  d_model=24			,  encoder_layers=2			,  decoder_layers=2			,  encoder_attention_heads=2			,  decoder_attention_heads=2			,  encoder_ffn_dim=32			,  decoder_ffn_dim=32			,  max_position_embeddings=48			,  eos_token_id=2			,  pad_token_id=1			,  bos_token_id=0			,  )
								return config, input_ids, batch_size







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   Tuple      =				self._get_config_and_data()
								UpperCAmelCase			:   int      =				FlaxBlenderbotForConditionalGeneration(lowercase  )
								UpperCAmelCase			:   List[str]      =				lm_model(input_ids=lowercase  )
								UpperCAmelCase			:   List[str]      =				(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs["logits"].shape			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



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







@require_flax
class       snake_case__  ( lowercase__					,  unittest.TestCase					,  lowercase__     ):
			SCREAMING_SNAKE_CASE__     				=						True
			SCREAMING_SNAKE_CASE__     				=						(
			    (
			        FlaxBlenderbotModel,
			        FlaxBlenderbotForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			SCREAMING_SNAKE_CASE__     				=						(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				FlaxBlenderbotModelTester(self  )







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase      ,							UpperCAmelCase			:   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(lowercase			,  lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				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(lowercase			,  lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase      ,							UpperCAmelCase			:   Dict      =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__  ):
																		UpperCAmelCase			:   Tuple      =				self._prepare_for_class(lowercase			,  lowercase  )
																		UpperCAmelCase			:   Tuple      =				model_class(lowercase  )

																		@jax.jit
																		def encode_jitted(lowercase			: Tuple			,  lowercase			: Any=None			,  **lowercase			: Optional[Any]  ):
																							return model.encode(input_ids=lowercase			,  attention_mask=lowercase  )

																		with self.subTest("JIT Enabled"  ):
																							UpperCAmelCase			:   Optional[int]      =				encode_jitted(**lowercase  ).to_tuple()

																		with self.subTest("JIT Disabled"  ):
																							with jax.disable_jit():
																												UpperCAmelCase			:   List[Any]      =				encode_jitted(**lowercase  ).to_tuple()

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







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase      ,							UpperCAmelCase			:   Optional[Any]      =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__  ):
																		UpperCAmelCase			:   Any      =				model_class(lowercase  )
																		UpperCAmelCase			:   Union[str, Any]      =				model.encode(inputs_dict["input_ids"]			,  inputs_dict["attention_mask"]  )

																		UpperCAmelCase			:   List[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(lowercase			: Dict			,  lowercase			: Optional[int]			,  lowercase			: List[str]  ):
																							return model.decode(
																							    decoder_input_ids=lowercase			,  decoder_attention_mask=lowercase			,  encoder_outputs=lowercase			,  )

																		with self.subTest("JIT Enabled"  ):
																							UpperCAmelCase			:   Any      =				decode_jitted(**lowercase  ).to_tuple()

																		with self.subTest("JIT Disabled"  ):
																							with jax.disable_jit():
																												UpperCAmelCase			:   Optional[Any]      =				decode_jitted(**lowercase  ).to_tuple()

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







			@slow
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



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







			@unittest.skipUnless(jax_device != "cpu"			,  "3B test too slow on CPU."  )
			@slow
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



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

								UpperCAmelCase			:   Union[str, Any]      =				FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"			,  from_pt=lowercase  )
								UpperCAmelCase			:   str      =				BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"  )

								UpperCAmelCase			:   Optional[Any]      =				["Sam"]
								UpperCAmelCase			:   int      =				tokenizer(lowercase			,  return_tensors="jax"  )

								UpperCAmelCase			:   Dict      =				model.generate(**lowercase			,  **lowercase  )
								UpperCAmelCase			:   Optional[Any]      =				"Sam is a great name. It means \"sun\" in Gaelic."

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





"""simple docstring"""






from __future__ import annotations

from collections import Counter
from random import random







class       snake_case__  :







			def __init__(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{}







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				{}







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: str			,  lowercase			: str			,  lowercase			: float  ):





								'''simple docstring'''



								if nodea not in self.connections:
													self.add_node(lowercase  )
								if nodea not in self.connections:
													self.add_node(lowercase  )
								UpperCAmelCase			:   int      =				probability







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								return list(self.connections  )







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				0
								UpperCAmelCase			:   Union[str, Any]      =				random()

								for dest in self.connections[node]:
													current_probability += self.connections[node][dest]
													if current_probability > random_value:
																		return dest
								return ""



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		list[tuple[str, str, float]]							,      _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				MarkovChainGraphUndirectedUnweighted()

					for nodea, nodea, probability in transitions:
										graph.add_transition_probability(_lowercase							,      _lowercase							,      _lowercase	)

					UpperCAmelCase			:   List[Any]      =				Counter(graph.get_nodes()	)
					UpperCAmelCase			:   List[Any]      =				start

					for _ in range(_lowercase	):
										UpperCAmelCase			:   Optional[Any]      =				graph.transition(_lowercase	)
										visited[node] += 1

					return visited


if __name__ == "__main__":
	import doctest

	doctest.testmod()


