



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
    import os

    import jax.numpy as jnp
    from jax import jit

    from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
    from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

    lowerCAmelCase__					  =  "0.12"  # assumed parallelism: 8


if is_torch_available():
    import torch


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=None						):

    """simple docstring"""


    if rng is None:
        lowercase__					:   Optional[int]													=					random.Random()

    lowercase__					:   str													=					1
    for dim in shape:
        total_dims *= dim

    lowercase__					:   int													=					[]
    for _ in range(lowerCamelCase_						):
        values.append(rng.randint(0					,			vocab_size - 1						)						)

    lowercase__					:   List[Any]													=					np.array(lowerCamelCase_					,			dtype=jnp.intaa						).reshape(lowerCamelCase_						)

    return output


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=None						):

    """simple docstring"""


    lowercase__					:   Tuple													=					ids_tensor(lowerCamelCase_					,			vocab_size=2					,			rng=lowerCamelCase_						)
    # make sure that at least one token is attended to for each batch
    lowercase__					:   int													=					1
    return attn_mask

@require_flax
class 				snake_case__:

     """simple docstring"""






     lowercase_								=		None
     lowercase_								=		()







     def 			snake_case					(							self :     Optional[int]					):
         lowercase__					:   Dict													=					self.model_tester.prepare_config_and_inputs_for_common()

         # cut to half length & take max batch_size 3
         lowercase__					:   Optional[Any]													=					2
         lowercase__					:   Optional[int]													=					inputs['input_ids'].shape[-1] // 2
         lowercase__					:   str													=					inputs['input_ids'][:max_batch_size, :sequence_length]

         lowercase__					:   List[str]													=					jnp.ones_like(SCREAMING_SNAKE_CASE					)
         lowercase__					:   List[Any]													=					attention_mask[:max_batch_size, :sequence_length]

         # generate max 5 tokens
         lowercase__					:   Optional[int]													=					input_ids.shape[-1] + 5
         if config.eos_token_id is not None and config.pad_token_id is None:
             # hack to allow generate for models such as GPT2 as is done in `generate()`
             lowercase__					:   Tuple													=					config.eos_token_id
         return config, input_ids, attention_mask, max_length







     @is_pt_flax_cross_test
     def 			snake_case					(							self :     str					):
         lowercase__					:   str													=					self._get_input_ids_and_config()
         lowercase__					:   List[str]													=					False
         lowercase__					:   Union[str, Any]													=					max_length
         lowercase__					:   List[Any]													=					0

         for model_class in self.all_generative_model_classes:
             lowercase__					:   Any													=					model_class(SCREAMING_SNAKE_CASE					)

             lowercase__					:   List[Any]													=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowercase__					:   Any													=					getattr(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
             lowercase__					:   List[Any]													=					pt_model_class(SCREAMING_SNAKE_CASE					).eval()
             lowercase__					:   List[str]													=					load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE				,       flax_model.params					)

             lowercase__					:   Tuple													=					flax_model.generate(SCREAMING_SNAKE_CASE					).sequences
             lowercase__					:   Optional[int]													=					pt_model.generate(torch.tensor(SCREAMING_SNAKE_CASE				,       dtype=torch.long					)					)

             if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                 lowercase__					:   Optional[int]													=					flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

             self.assertListEqual(pt_generation_outputs.numpy().tolist()				,       flax_generation_outputs.tolist()					)







     def 			snake_case					(							self :     Tuple					):
         lowercase__					:   Optional[Any]													=					self._get_input_ids_and_config()
         lowercase__					:   Optional[int]													=					False
         lowercase__					:   List[Any]													=					max_length

         for model_class in self.all_generative_model_classes:
             lowercase__					:   List[str]													=					model_class(SCREAMING_SNAKE_CASE					)

             lowercase__					:   Any													=					model.generate(SCREAMING_SNAKE_CASE					).sequences
             self.assertEqual(generation_outputs.shape[-1]				,       SCREAMING_SNAKE_CASE					)

             lowercase__					:   Tuple													=					jit(model.generate					)
             lowercase__					:   Any													=					jit_generate(SCREAMING_SNAKE_CASE					).sequences

             self.assertListEqual(generation_outputs.tolist()				,       jit_generation_outputs.tolist()					)







     def 			snake_case					(							self :     Union[str, Any]					):
         lowercase__					:   List[Any]													=					self._get_input_ids_and_config()
         lowercase__					:   Optional[int]													=					True
         lowercase__					:   Tuple													=					max_length

         for model_class in self.all_generative_model_classes:
             lowercase__					:   str													=					model_class(SCREAMING_SNAKE_CASE					)

             lowercase__					:   int													=					model.generate(SCREAMING_SNAKE_CASE					).sequences
             self.assertEqual(generation_outputs.shape[-1]				,       SCREAMING_SNAKE_CASE					)

             lowercase__					:   Optional[int]													=					jit(model.generate					)
             lowercase__					:   Optional[Any]													=					jit_generate(SCREAMING_SNAKE_CASE					).sequences

             self.assertListEqual(generation_outputs.tolist()				,       jit_generation_outputs.tolist()					)







     def 			snake_case					(							self :     Union[str, Any]					):
         lowercase__					:   str													=					self._get_input_ids_and_config()
         lowercase__					:   Optional[Any]													=					False
         lowercase__					:   Tuple													=					max_length
         lowercase__					:   int													=					2

         for model_class in self.all_generative_model_classes:
             lowercase__					:   List[str]													=					model_class(SCREAMING_SNAKE_CASE					)

             lowercase__					:   Dict													=					model.generate(SCREAMING_SNAKE_CASE					).sequences
             self.assertEqual(generation_outputs.shape[-1]				,       SCREAMING_SNAKE_CASE					)

             lowercase__					:   Optional[int]													=					jit(model.generate					)
             lowercase__					:   Union[str, Any]													=					jit_generate(SCREAMING_SNAKE_CASE					).sequences

             self.assertListEqual(generation_outputs.tolist()				,       jit_generation_outputs.tolist()					)







     def 			snake_case					(							self :     Any					):
         lowercase__					:   int													=					self._get_input_ids_and_config()
         lowercase__					:   Optional[int]													=					False
         lowercase__					:   Union[str, Any]													=					max_length
         lowercase__					:   Optional[Any]													=					2
         lowercase__					:   Dict													=					2

         for model_class in self.all_generative_model_classes:
             lowercase__					:   str													=					model_class(SCREAMING_SNAKE_CASE					)

             lowercase__					:   List[Any]													=					model.generate(SCREAMING_SNAKE_CASE					).sequences
             self.assertEqual(generation_outputs.shape[0]				,       input_ids.shape[0] * config.num_return_sequences					)







     def 			snake_case					(							self :     List[Any]					):
         lowercase__					:   List[Any]													=					self._get_input_ids_and_config()
         lowercase__					:   int													=					True
         lowercase__					:   Dict													=					max_length
         lowercase__					:   Optional[int]													=					0.8
         lowercase__					:   Union[str, Any]													=					10
         lowercase__					:   List[str]													=					0.3
         lowercase__					:   Optional[Any]													=					1
         lowercase__					:   str													=					8
         lowercase__					:   Union[str, Any]													=					9

         for model_class in self.all_generative_model_classes:
             lowercase__					:   Union[str, Any]													=					model_class(SCREAMING_SNAKE_CASE					)

             lowercase__					:   List[Any]													=					model.generate(SCREAMING_SNAKE_CASE					).sequences
             self.assertEqual(generation_outputs.shape[-1]				,       SCREAMING_SNAKE_CASE					)

             lowercase__					:   int													=					jit(model.generate					)
             lowercase__					:   Dict													=					jit_generate(SCREAMING_SNAKE_CASE					).sequences

             self.assertListEqual(generation_outputs.tolist()				,       jit_generation_outputs.tolist()					)







     def 			snake_case					(							self :     Union[str, Any]					):
         lowercase__					:   int													=					self._get_input_ids_and_config()
         lowercase__					:   Optional[Any]													=					max_length
         lowercase__					:   Union[str, Any]													=					1
         lowercase__					:   Dict													=					8
         lowercase__					:   Optional[Any]													=					9

         for model_class in self.all_generative_model_classes:
             lowercase__					:   Dict													=					model_class(SCREAMING_SNAKE_CASE					)

             lowercase__					:   Optional[Any]													=					model.generate(SCREAMING_SNAKE_CASE					).sequences
             self.assertEqual(generation_outputs.shape[-1]				,       SCREAMING_SNAKE_CASE					)

             lowercase__					:   Any													=					jit(model.generate					)
             lowercase__					:   Dict													=					jit_generate(SCREAMING_SNAKE_CASE					).sequences

             self.assertListEqual(generation_outputs.tolist()				,       jit_generation_outputs.tolist()					)







     def 			snake_case					(							self :     List[Any]					):
         lowercase__					:   Optional[int]													=					self._get_input_ids_and_config()
         lowercase__					:   List[Any]													=					max_length
         lowercase__					:   Dict													=					2
         lowercase__					:   int													=					1
         lowercase__					:   str													=					8
         lowercase__					:   Optional[Any]													=					9

         for model_class in self.all_generative_model_classes:
             lowercase__					:   Optional[int]													=					model_class(SCREAMING_SNAKE_CASE					)

             lowercase__					:   Any													=					model.generate(SCREAMING_SNAKE_CASE					).sequences
             self.assertEqual(generation_outputs.shape[-1]				,       SCREAMING_SNAKE_CASE					)

             lowercase__					:   Dict													=					jit(model.generate					)
             lowercase__					:   Tuple													=					jit_generate(SCREAMING_SNAKE_CASE					).sequences

             self.assertListEqual(generation_outputs.tolist()				,       jit_generation_outputs.tolist()					)







     def 			snake_case					(							self :     Any					):
         lowercase__					:   Dict													=					self._get_input_ids_and_config()

         # pad attention mask on the left
         lowercase__					:   List[Any]													=					attention_mask.at[(0, 0)].set(0					)

         lowercase__					:   Tuple													=					False
         lowercase__					:   List[str]													=					max_length

         for model_class in self.all_generative_model_classes:
             lowercase__					:   str													=					model_class(SCREAMING_SNAKE_CASE					)

             lowercase__					:   Dict													=					model.generate(SCREAMING_SNAKE_CASE				,       attention_mask=SCREAMING_SNAKE_CASE					).sequences
             self.assertEqual(generation_outputs.shape[-1]				,       SCREAMING_SNAKE_CASE					)

             lowercase__					:   Optional[int]													=					jit(model.generate					)
             lowercase__					:   Union[str, Any]													=					jit_generate(SCREAMING_SNAKE_CASE				,       attention_mask=SCREAMING_SNAKE_CASE					).sequences

             self.assertListEqual(generation_outputs.tolist()				,       jit_generation_outputs.tolist()					)







     def 			snake_case					(							self :     Any					):
         lowercase__					:   Union[str, Any]													=					self._get_input_ids_and_config()

         # pad attention mask on the left
         lowercase__					:   Tuple													=					attention_mask.at[(0, 0)].set(0					)

         lowercase__					:   int													=					True
         lowercase__					:   List[str]													=					max_length

         for model_class in self.all_generative_model_classes:
             lowercase__					:   List[str]													=					model_class(SCREAMING_SNAKE_CASE					)

             lowercase__					:   Any													=					model.generate(SCREAMING_SNAKE_CASE				,       attention_mask=SCREAMING_SNAKE_CASE					).sequences
             self.assertEqual(generation_outputs.shape[-1]				,       SCREAMING_SNAKE_CASE					)

             lowercase__					:   Any													=					jit(model.generate					)
             lowercase__					:   Optional[int]													=					jit_generate(SCREAMING_SNAKE_CASE				,       attention_mask=SCREAMING_SNAKE_CASE					).sequences

             self.assertListEqual(generation_outputs.tolist()				,       jit_generation_outputs.tolist()					)







     def 			snake_case					(							self :     int					):
         lowercase__					:   Optional[int]													=					self._get_input_ids_and_config()

         # pad attention mask on the left
         lowercase__					:   Optional[int]													=					attention_mask.at[(0, 0)].set(0					)

         lowercase__					:   Optional[int]													=					2
         lowercase__					:   Tuple													=					max_length

         for model_class in self.all_generative_model_classes:
             lowercase__					:   Tuple													=					model_class(SCREAMING_SNAKE_CASE					)

             lowercase__					:   Optional[int]													=					model.generate(SCREAMING_SNAKE_CASE				,       attention_mask=SCREAMING_SNAKE_CASE					).sequences
             self.assertEqual(generation_outputs.shape[-1]				,       SCREAMING_SNAKE_CASE					)

             lowercase__					:   Union[str, Any]													=					jit(model.generate					)
             lowercase__					:   List[Any]													=					jit_generate(SCREAMING_SNAKE_CASE				,       attention_mask=SCREAMING_SNAKE_CASE					).sequences

             self.assertListEqual(generation_outputs.tolist()				,       jit_generation_outputs.tolist()					)






@require_flax
class 				snake_case__(unittest.TestCase      ):

     """simple docstring"""






     def 			snake_case					(							self :     List[Any]					):
         lowercase__					:   Any													=					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"					)
         lowercase__					:   int													=					FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"					)

         lowercase__					:   str													=					'Hello world'
         lowercase__					:   List[str]													=					tokenizer(SCREAMING_SNAKE_CASE				,       return_tensors="np"					).input_ids

         # typos are quickly detected (the correct argument is `do_sample`)
         with self.assertRaisesRegex(SCREAMING_SNAKE_CASE				,       "do_samples"					):
             model.generate(SCREAMING_SNAKE_CASE				,       do_samples=SCREAMING_SNAKE_CASE					)

         # arbitrary arguments that will not be used anywhere are also not accepted
         with self.assertRaisesRegex(SCREAMING_SNAKE_CASE				,       "foo"					):
             lowercase__					:   str													=					{'foo': 'bar'}
             model.generate(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)









def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					[]
				lowercase__					:   Tuple													=					[]
				lowercase__					:   Any													=					{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				lowercase__					:   Any													=					len(lowerCamelCase__						) if (len(lowerCamelCase__						) > 7) else 7

				# Print table header for output
				print(
				    "Symbol".center(8						)					,			"Stack".center(lowerCamelCase__						)					,			"Postfix".center(lowerCamelCase__						)					,			sep=" | "					,			)
				print("-" * (print_width * 3 + 7)						)

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase__						)  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase__						)  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase__						) == 0:
																stack.append(lowerCamelCase__						)  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase__						) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()						)  # pop stack & add to Postfix
																stack.append(lowerCamelCase__						)  # push x to stack

								print(
								    x.center(8						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			sep=" | "					,			)  # Output in tabular format

				while len(lowerCamelCase__						) > 0:  # while stack is not empty
								post_fix.append(stack.pop()						)  # pop stack & add to Postfix
								print(
								    " ".center(8						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			sep=" | "					,			)  # Output in tabular format

				return "".join(lowerCamelCase__						)  # return Postfix as str


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[int]													=					list(infix[::-1]						)  # reverse the infix equation

				for i in range(len(lowerCamelCase__						)						):
								if infix[i] == "(":
												lowercase__					:   Tuple													=					")"  # change "(" to ")"
								elif infix[i] == ")":
												lowercase__					:   Optional[Any]													=					"("  # change ")" to "("

				return (infix_2_postfix("".join(lowerCamelCase__						)						))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
				lowerCAmelCase__					  =  input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
				lowerCAmelCase__					  =  ''''''.join(Infix.split())  # Remove spaces from the input
				print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')




