
"""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
			__lowerCAmelCase    :							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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  ):





		"""simple docstring"""




		if attention_mask is None:
				lowerCAmelCase__        =   np.where(input_ids != config.pad_token_id			,  1			,  0     )
		if decoder_attention_mask is None:
				lowerCAmelCase__        =   np.where(decoder_input_ids != config.pad_token_id			,  1			,  0     )
		if head_mask is None:
				lowerCAmelCase__        =   np.ones((config.encoder_layers, config.encoder_attention_heads)     )
		if decoder_head_mask is None:
				lowerCAmelCase__        =   np.ones((config.decoder_layers, config.decoder_attention_heads)     )
		if cross_attn_head_mask is None:
				lowerCAmelCase__        =   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       a_      :




					def __init__(	self						:     Optional[int]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     List[Any]=13  ,						snake_case__						:     List[str]=7  ,						snake_case__						:     Optional[int]=True  ,						snake_case__						:     Optional[int]=False  ,						snake_case__						:     Any=99  ,						snake_case__						:     List[str]=16  ,						snake_case__						:     Dict=2  ,						snake_case__						:     Union[str, Any]=4  ,						snake_case__						:     Dict=4  ,						snake_case__						:     Dict="gelu"  ,						snake_case__						:     Any=0.1  ,						snake_case__						:     List[Any]=0.1  ,						snake_case__						:     Any=32  ,						snake_case__						:     Tuple=2  ,						snake_case__						:     List[str]=1  ,						snake_case__						:     Dict=0  ,						snake_case__						:     Optional[int]=0.02  ,						):
							lowerCAmelCase__        =   parent
							lowerCAmelCase__        =   batch_size
							lowerCAmelCase__        =   seq_length
							lowerCAmelCase__        =   is_training
							lowerCAmelCase__        =   use_labels
							lowerCAmelCase__        =   vocab_size
							lowerCAmelCase__        =   hidden_size
							lowerCAmelCase__        =   num_hidden_layers
							lowerCAmelCase__        =   num_attention_heads
							lowerCAmelCase__        =   intermediate_size
							lowerCAmelCase__        =   hidden_act
							lowerCAmelCase__        =   hidden_dropout_prob
							lowerCAmelCase__        =   attention_probs_dropout_prob
							lowerCAmelCase__        =   max_position_embeddings
							lowerCAmelCase__        =   eos_token_id
							lowerCAmelCase__        =   pad_token_id
							lowerCAmelCase__        =   bos_token_id
							lowerCAmelCase__        =   initializer_range




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,						self.vocab_size     )  ,						3  ,						self.vocab_size     )
							lowerCAmelCase__        =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,						dtype=np.intaa     ))  ,						-1     )

							lowerCAmelCase__        =   shift_tokens_right(snake_case__  ,						1  ,						2     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.prepare_config_and_inputs()
							return config, inputs_dict




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Tuple  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   20
							lowerCAmelCase__        =   model_class_name(snake_case__     )

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

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

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

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

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

							lowerCAmelCase__        =   model.decode(snake_case__  ,						snake_case__     )

							lowerCAmelCase__        =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
							self.parent.assertTrue(diff < 1E-3  ,						msg=F"""Max diff is {diff}"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   20
							lowerCAmelCase__        =   model_class_name(snake_case__     )

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

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

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

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

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

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

							lowerCAmelCase__        =   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       a_      (     unittest.TestCase			):
					UpperCamelCase_ :	int			=    99




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   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  ,						)

							lowerCAmelCase__        =   input_ids.shape[0]
							lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_config_and_data()
							lowerCAmelCase__        =   FlaxBlenderbotForConditionalGeneration(snake_case__     )
							lowerCAmelCase__        =   lm_model(input_ids=snake_case__     )
							lowerCAmelCase__        =   (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape  ,						snake_case__     )




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




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

@require_flax
class       a_      (     __UpperCamelCase ,		unittest.TestCase ,		__UpperCamelCase			):
					UpperCamelCase_ :	Optional[int]			=    True
					UpperCamelCase_ :	Union[str, Any]			=    (
					    (
					        FlaxBlenderbotModel,
					        FlaxBlenderbotForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					UpperCamelCase_ :	Optional[int]			=    (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   FlaxBlenderbotModelTester(self     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(snake_case__  ,						snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   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(snake_case__  ,						snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

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

											lowerCAmelCase__        =   {
											    """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(snake_case__						:     List[Any]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     List[Any]     ):
													return model.decode(
													    decoder_input_ids=snake_case__  ,						decoder_attention_mask=snake_case__  ,						encoder_outputs=snake_case__  ,						)

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

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

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




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							for model_class_name in self.all_model_classes:
									lowerCAmelCase__        =   model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""     )
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									lowerCAmelCase__        =   np.ones((1, 1)     ) * model.config.eos_token_id
									lowerCAmelCase__        =   model(snake_case__     )
									self.assertIsNotNone(snake_case__     )




					@unittest.skipUnless(jax_device != """cpu"""  ,						"""3B test too slow on CPU."""     )
					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
							lowerCAmelCase__        =   {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

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

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

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

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





"""simple docstring"""




import os
from math import logaa







def 							_UpperCAmelCase		(       lowerCamelCase__ = "base_exp.txt"     ):





		"""simple docstring"""




		lowerCAmelCase__        =   0
		lowerCAmelCase__        =   0
		for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase__     )			,  lowerCamelCase__     )     )     ):
				lowerCAmelCase__   ,					lowerCAmelCase__        =   list(map(lowerCamelCase__			,  line.split(""","""     )     )     )
				if x * logaa(lowerCamelCase__     ) > largest:
						lowerCAmelCase__        =   x * logaa(lowerCamelCase__     )
						lowerCAmelCase__        =   i + 1
		return result


if __name__ == "__main__":
			print(solution())




