


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				_lowercase							:   Dict						=						"platform"

				import jax
				import jax.numpy as jnp

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





def 	_lowerCAmelCase					( UpperCamelCase__:  int				,       UpperCamelCase__:  Dict				,       UpperCamelCase__:  Union[str, Any]=None				,       UpperCamelCase__:  List[Any]=None				,       UpperCamelCase__:  Dict=None				,       UpperCamelCase__:  Any=None				,       UpperCamelCase__:  Dict=None				,       UpperCamelCase__:  Optional[Any]=None				,       )			-> Union[str, Any]:





	"""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    _UpperCamelCase     :

				"""simple docstring"""







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

					A             =					BlenderbotSmallConfig(
					    vocab_size=self.vocab_size					, d_model=self.hidden_size					, encoder_layers=self.num_hidden_layers					, decoder_layers=self.num_hidden_layers					, encoder_attention_heads=self.num_attention_heads					, decoder_attention_heads=self.num_attention_heads					, encoder_ffn_dim=self.intermediate_size					, decoder_ffn_dim=self.intermediate_size					, dropout=self.hidden_dropout_prob					, attention_dropout=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, eos_token_id=self.eos_token_id					, bos_token_id=self.bos_token_id					, pad_token_id=self.pad_token_id					, initializer_range=self.initializer_range					, use_cache=a__					, )
					A             =					prepare_blenderbot_inputs_dict(a__					, a__					, a__ )
					return config, inputs_dict

				def   _UpperCAmelCase       (    self )						->					Any:
					A       ,      A             =					self.prepare_config_and_inputs()
					return config, inputs_dict

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					Union[str, Any]:
					A             =					20
					A             =					model_class_name(a__ )

					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]					, a__					, a__ )
					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]					, a__					, decoder_attention_mask=a__					, past_key_values=a__					, decoder_position_ids=a__					, )

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

					A             =					model.decode(a__					, a__ )

					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					, a__					, a__					, a__ )						->					int:
					A             =					20
					A             =					model_class_name(a__ )

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

					A             =					model.decode(a__					, a__					, decoder_attention_mask=a__ )

					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    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







				lowerCAmelCase					=			9_9

				def   _UpperCAmelCase       (    self )						->					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             =					BlenderbotSmallConfig(
					    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 )						->					Dict:
					A       ,      A       ,      A             =					self._get_config_and_data()
					A             =					FlaxBlenderbotSmallForConditionalGeneration(a__ )
					A             =					lm_model(input_ids=a__ )
					A             =					(batch_size, input_ids.shape[1], config.vocab_size)
					self.assertEqual(outputs["""logits"""].shape					, a__ )

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					BlenderbotSmallConfig(
					    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             =					FlaxBlenderbotSmallForConditionalGeneration(a__ )
					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=a__					, decoder_input_ids=a__ )
					A             =					(*summary.shape, config.vocab_size)
					self.assertEqual(outputs["""logits"""].shape					, a__ )

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






@require_flax
class    _UpperCamelCase     (       __snake_case     ,		unittest.TestCase     ,		__snake_case					):

				"""simple docstring"""







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

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					FlaxBlenderbotSmallModelTester(self )

				def   _UpperCAmelCase       (    self )						->					Dict:
					A       ,      A             =					self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
						self.model_tester.check_use_cache_forward(a__					, a__					, a__ )

				def   _UpperCAmelCase       (    self )						->					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_with_attn_mask(a__					, a__					, a__ )

				def   _UpperCAmelCase       (    self )						->					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             =					self._prepare_for_class(a__					, a__ )
							A             =					model_class(a__ )

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

							with self.subTest("""JIT Enabled""" ):
								A             =					encode_jitted(**a__ ).to_tuple()

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

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

				def   _UpperCAmelCase       (    self )						->					List[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(a__ )
							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(a__					, a__					, a__ ):
								return model.decode(
								    decoder_input_ids=a__					, decoder_attention_mask=a__					, encoder_outputs=a__					, )

							with self.subTest("""JIT Enabled""" ):
								A             =					decode_jitted(**a__ ).to_tuple()

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

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

				@slow
				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					for model_class_name in self.all_model_classes:
						A             =					model_class_name.from_pretrained("""facebook/blenderbot_small-90M""" )
						# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
						A             =					np.ones((1, 1) ) * model.config.eos_token_id
						A             =					model(a__ )
						self.assertIsNotNone(a__ )







def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  int				,       UpperCamelCase__:  Any=False    )			-> str:





	"""simple docstring"""







	if isinstance(UpperCamelCase__				,       UpperCamelCase__    ) and isinstance(UpperCamelCase__				,       UpperCamelCase__    ):
		A             =					len(set_a.intersection(UpperCamelCase__    )    )

		if alternative_union:
			A             =					len(UpperCamelCase__    ) + len(UpperCamelCase__    )
		else:
			A             =					len(set_a.union(UpperCamelCase__    )    )

		return intersection / union

	if isinstance(UpperCamelCase__				,       (list, tuple)    ) and isinstance(UpperCamelCase__				,       (list, tuple)    ):
		A             =					[element for element in set_a if element in set_b]

		if alternative_union:
			A             =					len(UpperCamelCase__    ) + len(UpperCamelCase__    )
			return len(UpperCamelCase__    ) / union
		else:
			A             =					set_a + [element for element in set_b if element not in set_a]
			return len(UpperCamelCase__    ) / len(UpperCamelCase__    )

		return len(UpperCamelCase__    ) / len(UpperCamelCase__    )
	return None


if __name__ == "__main__":
				_lowercase							:   List[Any]						=						{"a", "b", "c", "d", "e"}
				_lowercase							:   List[Any]						=						{"c", "d", "e", "f", "h", "i"}
				print(jaccard_similarity(set_a, set_b))



