





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
  _A					:				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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None ,							)				->						Any:
    if attention_mask is None:
        SCREAMING_SNAKE_CASE__											=			np.where(input_ids != config.pad_token_id ,							1 ,							0	)
    if decoder_attention_mask is None:
        SCREAMING_SNAKE_CASE__											=			np.where(decoder_input_ids != config.pad_token_id ,							1 ,							0	)
    if head_mask is None:
        SCREAMING_SNAKE_CASE__											=			np.ones((config.encoder_layers, config.encoder_attention_heads)	)
    if decoder_head_mask is None:
        SCREAMING_SNAKE_CASE__											=			np.ones((config.decoder_layers, config.decoder_attention_heads)	)
    if cross_attn_head_mask is None:
        SCREAMING_SNAKE_CASE__											=			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   ,				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   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			seq_length
        SCREAMING_SNAKE_CASE__											=			is_training
        SCREAMING_SNAKE_CASE__											=			use_labels
        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			hidden_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__											=			max_position_embeddings
        SCREAMING_SNAKE_CASE__											=			eos_token_id
        SCREAMING_SNAKE_CASE__											=			pad_token_id
        SCREAMING_SNAKE_CASE__											=			bos_token_id
        SCREAMING_SNAKE_CASE__											=			initializer_range



    def  lowercase_					(       self     ):





        '''simple docstring'''

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

        SCREAMING_SNAKE_CASE__											=			shift_tokens_right(A_   ,				1   ,				2     )

        SCREAMING_SNAKE_CASE__											=			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=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			prepare_blenderbot_inputs_dict(A_   ,				A_   ,				A_     )
        return config, inputs_dict



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()
        return config, inputs_dict



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			model_class_name(A_     )

        SCREAMING_SNAKE_CASE__											=			model.encode(inputs_dict['''input_ids''']     )

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			(
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        SCREAMING_SNAKE_CASE__											=			model.init_cache(decoder_input_ids.shape[0]   ,				A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,				dtype='''i4'''     )

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

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

        SCREAMING_SNAKE_CASE__											=			model.decode(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			model_class_name(A_     )

        SCREAMING_SNAKE_CASE__											=			model.encode(inputs_dict['''input_ids''']     )

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			(
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        SCREAMING_SNAKE_CASE__											=			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__											=			model.init_cache(decoder_input_ids.shape[0]   ,				A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			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__											=			model.decode(
            decoder_input_ids[:, :-1]   ,				A_   ,				decoder_attention_mask=A_   ,				past_key_values=A_   ,				decoder_position_ids=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,				dtype='''i4'''     )
        SCREAMING_SNAKE_CASE__											=			model.decode(
            decoder_input_ids[:, -1:]   ,				A_   ,				past_key_values=outputs_cache.past_key_values   ,				decoder_attention_mask=A_   ,				decoder_position_ids=A_   ,				)

        SCREAMING_SNAKE_CASE__											=			model.decode(A_   ,				A_   ,				decoder_attention_mask=A_     )

        SCREAMING_SNAKE_CASE__											=			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'''







    lowerCamelCase__    :		Optional[Any]				=	9_9



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			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__											=			input_ids.shape[0]
        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self._get_config_and_data()
        SCREAMING_SNAKE_CASE__											=			FlaxBlenderbotForConditionalGeneration(A_     )
        SCREAMING_SNAKE_CASE__											=			lm_model(input_ids=A_     )
        SCREAMING_SNAKE_CASE__											=			(batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			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__											=			FlaxBlenderbotForConditionalGeneration(A_     )
        SCREAMING_SNAKE_CASE__											=			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,				dtype=np.intaa     )
        SCREAMING_SNAKE_CASE__											=			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,				dtype=np.intaa     )
        SCREAMING_SNAKE_CASE__											=			lm_model(input_ids=A_   ,				decoder_input_ids=A_     )
        SCREAMING_SNAKE_CASE__											=			(*summary.shape, config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape   ,				A_     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,				dtype=np.intaa     )
        SCREAMING_SNAKE_CASE__											=			shift_tokens_right(A_   ,				1   ,				2     )
        SCREAMING_SNAKE_CASE__											=			np.equal(A_   ,				1     ).astype(np.floataa     ).sum()
        SCREAMING_SNAKE_CASE__											=			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 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase			,     __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			FlaxBlenderbotModelTester(self     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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__											=			self._prepare_for_class(A_   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			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'''     ):
                    SCREAMING_SNAKE_CASE__											=			encode_jitted(**A_     ).to_tuple()

                with self.subTest('''JIT Disabled'''     ):
                    with jax.disable_jit():
                        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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__											=			model_class(A_     )
                SCREAMING_SNAKE_CASE__											=			model.encode(inputs_dict['''input_ids''']   ,				inputs_dict['''attention_mask''']     )

                SCREAMING_SNAKE_CASE__											=			{
                    '''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'''     ):
                    SCREAMING_SNAKE_CASE__											=			decode_jitted(**A_     ).to_tuple()

                with self.subTest('''JIT Disabled'''     ):
                    with jax.disable_jit():
                        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self     ):





        '''simple docstring'''

        for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__											=			model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''     )
            # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
            SCREAMING_SNAKE_CASE__											=			np.ones((1, 1)     ) * model.config.eos_token_id
            SCREAMING_SNAKE_CASE__											=			model(A_     )
            self.assertIsNotNone(A_     )




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





        '''simple docstring'''

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

        SCREAMING_SNAKE_CASE__											=			FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''   ,				from_pt=A_     )
        SCREAMING_SNAKE_CASE__											=			BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''     )

        SCREAMING_SNAKE_CASE__											=			['''Sam''']
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_   ,				return_tensors='''jax'''     )

        SCREAMING_SNAKE_CASE__											=			model.generate(**A_   ,				**A_     )
        SCREAMING_SNAKE_CASE__											=			'''Sam is a great name. It means "sun" in Gaelic.'''

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







import operator as op


def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			lambda lowerCAmelCase_ ,							lowerCAmelCase_	: int(x / y	)  # noqa: E731 integer division operation
    SCREAMING_SNAKE_CASE__											=			{
        '''^''': op.pow,
        '''*''': op.mul,
        '''/''': div,
        '''+''': op.add,
        '''-''': op.sub,
    }  # operators & their respective operation

    # print table header
    print('''Symbol'''.center(8	) ,							'''Action'''.center(1_2	) ,							'''Stack''' ,							sep=''' | '''	)
    print('''-''' * (3_0 + len(lowerCAmelCase_	))	)

    for x in post_fix:
        if x.isdigit():  # if x in digit
            stack.append(lowerCAmelCase_	)  # append x to stack
            # output in tabular format
            print(x.rjust(8	) ,							('''push(''' + x + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)
        else:
            SCREAMING_SNAKE_CASE__											=			stack.pop()  # pop stack
            # output in tabular format
            print(''''''.rjust(8	) ,							('''pop(''' + b + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)

            SCREAMING_SNAKE_CASE__											=			stack.pop()  # pop stack
            # output in tabular format
            print(''''''.rjust(8	) ,							('''pop(''' + a + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)

            stack.append(
                str(opr[x](int(lowerCAmelCase_	) ,							int(lowerCAmelCase_	)	)	)	)  # evaluate the 2 values popped from stack & push result to stack
            # output in tabular format
            print(
                x.rjust(8	) ,							('''push(''' + a + x + b + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | ''' ,							)

    return int(stack[0]	)


if __name__ == "__main__":
  _A					:				Dict				=			input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
  print("""\n\tResult = """, solve(Postfix))
