






from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFLEDForConditionalGeneration, TFLEDModel


@require_tf
class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     UpperCamelCase_				:		List[Any]								=						LEDConfig
     UpperCamelCase_				:		Tuple								=						{}
     UpperCamelCase_				:		Dict								=						'''gelu'''


     def __init__(					self							:						Dict       ,			UpperCAmelCase_							:						Optional[Any]       ,			UpperCAmelCase_							:						List[Any]=13       ,			UpperCAmelCase_							:						Optional[int]=7       ,			UpperCAmelCase_							:						Dict=True       ,			UpperCAmelCase_							:						List[Any]=False       ,			UpperCAmelCase_							:						List[str]=99       ,			UpperCAmelCase_							:						str=32       ,			UpperCAmelCase_							:						Optional[int]=2       ,			UpperCAmelCase_							:						str=4       ,			UpperCAmelCase_							:						Union[str, Any]=37       ,			UpperCAmelCase_							:						Dict=0.1       ,			UpperCAmelCase_							:						Union[str, Any]=0.1       ,			UpperCAmelCase_							:						List[str]=20       ,			UpperCAmelCase_							:						Union[str, Any]=2       ,			UpperCAmelCase_							:						Optional[int]=1       ,			UpperCAmelCase_							:						Optional[Any]=0       ,			UpperCAmelCase_							:						Tuple=4       ,			):
       SCREAMING_SNAKE_CASE							:   Any						    =     parent
       SCREAMING_SNAKE_CASE							:   List[str]						    =     batch_size
       SCREAMING_SNAKE_CASE							:   Any						    =     seq_length
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     is_training
       SCREAMING_SNAKE_CASE							:   int						    =     use_labels
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     vocab_size
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     hidden_size
       SCREAMING_SNAKE_CASE							:   int						    =     num_hidden_layers
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     num_attention_heads
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     intermediate_size

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     hidden_dropout_prob
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE							:   Any						    =     max_position_embeddings
       SCREAMING_SNAKE_CASE							:   str						    =     eos_token_id
       SCREAMING_SNAKE_CASE							:   int						    =     pad_token_id
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     bos_token_id
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     attention_window

       # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
       # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
       # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
       # because its local attention only attends to `self.attention_window` and one before and one after
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.attention_window + 2

       # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
       # the `test_attention_outputs` and `test_hidden_states_output` tests
       SCREAMING_SNAKE_CASE							:   Dict						    =     (
           self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
       )


     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     ids_tensor([self.batch_size, self.seq_length - 1]       ,			self.vocab_size   )
       SCREAMING_SNAKE_CASE							:   int						    =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )       ,			1   )
       SCREAMING_SNAKE_CASE							:   int						    =     tf.concat([input_ids, eos_tensor]       ,			axis=1   )

       SCREAMING_SNAKE_CASE							:   Any						    =     ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.config_cls(
           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_ids=[2]       ,			bos_token_id=self.bos_token_id       ,			pad_token_id=self.pad_token_id       ,			decoder_start_token_id=self.pad_token_id       ,			attention_window=self.attention_window       ,			**self.config_updates       ,			)
       SCREAMING_SNAKE_CASE							:   Tuple						    =     prepare_led_inputs_dict(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Any						    =     tf.concat(
           [tf.zeros_like(UpperCAmelCase_   )[:, :-1], tf.ones_like(UpperCAmelCase_   )[:, -1:]]       ,			axis=-1       ,			)
       SCREAMING_SNAKE_CASE							:   int						    =     global_attention_mask
       return config, inputs_dict






     def 					_A							(					self							:						Dict       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   str						    =     TFLEDModel(config=UpperCAmelCase_   ).get_decoder()
       SCREAMING_SNAKE_CASE							:   str						    =     inputs_dict["input_ids"]

       SCREAMING_SNAKE_CASE							:   List[str]						    =     input_ids[:1, :]
       SCREAMING_SNAKE_CASE							:   Any						    =     inputs_dict["attention_mask"][:1, :]
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     1

       # first forward pass
       SCREAMING_SNAKE_CASE							:   Dict						    =     model(UpperCAmelCase_       ,			attention_mask=UpperCAmelCase_       ,			use_cache=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   int						    =     outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE							:   int						    =     ids_tensor((self.batch_size, 3)       ,			config.vocab_size   )
       SCREAMING_SNAKE_CASE							:   str						    =     tf.cast(ids_tensor((self.batch_size, 3)       ,			2   )       ,			tf.inta   )

       # append to next input_ids and
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tf.concat([input_ids, next_tokens]       ,			axis=-1   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tf.concat([attention_mask, next_attn_mask]       ,			axis=-1   )

       SCREAMING_SNAKE_CASE							:   Dict						    =     model(UpperCAmelCase_       ,			attention_mask=UpperCAmelCase_   )[0]
       SCREAMING_SNAKE_CASE							:   str						    =     model(UpperCAmelCase_       ,			attention_mask=UpperCAmelCase_       ,			past_key_values=UpperCAmelCase_   )[0]

       self.parent.assertEqual(next_tokens.shape[1]       ,			output_from_past.shape[1]   )

       # select random slice
       SCREAMING_SNAKE_CASE							:   Dict						    =     int(ids_tensor((1,)       ,			output_from_past.shape[-1]   )   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     output_from_no_past[:, -3:, random_slice_idx]
       SCREAMING_SNAKE_CASE							:   str						    =     output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(UpperCAmelCase_       ,			UpperCAmelCase_       ,			rtol=1E-3   )





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

  """simple docstring"""






  if attention_mask is None:
    SCREAMING_SNAKE_CASE							:   Tuple						    =     tf.cast(tf.math.not_equal(lowercase    ,  config.pad_token_id					)    ,  tf.inta					)
  if decoder_attention_mask is None:
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape    ,  dtype=tf.inta					),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    ,  config.pad_token_id					)    ,  tf.inta					),
        ]    ,  axis=-1    ,  )
  if head_mask is None:
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
  if decoder_head_mask is None:
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
  return {
      "input_ids": input_ids,
      "attention_mask": attention_mask,
      "decoder_input_ids": decoder_input_ids,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
  }


@require_tf
class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		List[str]								=						(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
     UpperCamelCase_				:		List[str]								=						(TFLEDForConditionalGeneration,) if is_tf_available() else ()
     UpperCamelCase_				:		int								=						(
         {
             '''conversational''': TFLEDForConditionalGeneration,
             '''feature-extraction''': TFLEDModel,
             '''summarization''': TFLEDForConditionalGeneration,
             '''text2text-generation''': TFLEDForConditionalGeneration,
             '''translation''': TFLEDForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     UpperCamelCase_				:		Union[str, Any]								=						True
     UpperCamelCase_				:		int								=						False
     UpperCamelCase_				:		int								=						False
     UpperCamelCase_				:		Union[str, Any]								=						False


     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     TFLEDModelTester(self   )
       SCREAMING_SNAKE_CASE							:   str						    =     ConfigTester(self       ,			config_class=UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[int]   ):
       self.config_tester.run_common_tests()


     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_   )


     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tf.zeros_like(inputs_dict["attention_mask"]   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     2
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     tf.where(
           tf.range(self.model_tester.seq_length   )[None, :] < num_global_attn_indices       ,			1       ,			inputs_dict["global_attention_mask"]       ,			)

       SCREAMING_SNAKE_CASE							:   Tuple						    =     True
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.model_tester.seq_length
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.model_tester.encoder_seq_length

       def check_decoder_attentions_output(UpperCAmelCase_							:						Tuple   ):
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     outputs.decoder_attentions
         self.assertEqual(len(UpperCAmelCase_   )       ,			self.model_tester.num_hidden_layers   )
         self.assertListEqual(
             list(decoder_attentions[0].shape[-3:]   )       ,			[self.model_tester.num_attention_heads, seq_length, seq_length]       ,			)

       def check_encoder_attentions_output(UpperCAmelCase_							:						Union[str, Any]   ):
         SCREAMING_SNAKE_CASE							:   Any						    =     [t.numpy() for t in outputs.encoder_attentions]
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     [t.numpy() for t in outputs.encoder_global_attentions]
         self.assertEqual(len(UpperCAmelCase_   )       ,			self.model_tester.num_hidden_layers   )
         self.assertEqual(len(UpperCAmelCase_   )       ,			self.model_tester.num_hidden_layers   )
         self.assertListEqual(
             list(attentions[0].shape[-3:]   )       ,			[self.model_tester.num_attention_heads, seq_length, seq_length]       ,			)
         self.assertListEqual(
             list(global_attentions[0].shape[-3:]   )       ,			[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]       ,			)

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     True
         SCREAMING_SNAKE_CASE							:   str						    =     False
         SCREAMING_SNAKE_CASE							:   str						    =     False
         SCREAMING_SNAKE_CASE							:   Any						    =     model_class(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     model(self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )   )
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     len(UpperCAmelCase_   )
         self.assertEqual(config.output_hidden_states       ,			UpperCAmelCase_   )
         check_encoder_attentions_output(UpperCAmelCase_   )

         if self.is_encoder_decoder:
           SCREAMING_SNAKE_CASE							:   Dict						    =     model_class(UpperCAmelCase_   )
           SCREAMING_SNAKE_CASE							:   Tuple						    =     model(self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )   )
           self.assertEqual(config.output_hidden_states       ,			UpperCAmelCase_   )
           check_decoder_attentions_output(UpperCAmelCase_   )

         # Check that output attentions can also be changed via the config
         del inputs_dict["output_attentions"]
         SCREAMING_SNAKE_CASE							:   int						    =     True
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     model_class(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   str						    =     model(self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )   )
         self.assertEqual(config.output_hidden_states       ,			UpperCAmelCase_   )
         check_encoder_attentions_output(UpperCAmelCase_   )

         # Check attention is always last and order is fine
         SCREAMING_SNAKE_CASE							:   Tuple						    =     True
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     True
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model_class(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     model(self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )   )

         self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)       ,			len(UpperCAmelCase_   )   )
         self.assertEqual(model.config.output_hidden_states       ,			UpperCAmelCase_   )
         check_encoder_attentions_output(UpperCAmelCase_   )


     @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."   )
     def 					_A							(					self							:						List[Any]   ):
       pass






     def 					_A							(					self							:						Optional[Any]   ):
       # TODO: Head-masking not yet implement
       pass





def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  return tf.constant(lowercase    ,  dtype=tf.intaa					)


snake_case     					=		1e-4


@slow
@require_tf
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"   ).led

       # change to intended input here
       SCREAMING_SNAKE_CASE							:   List[str]						    =     _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     prepare_led_inputs_dict(model.config       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   int						    =     model(**UpperCAmelCase_   )[0]
       SCREAMING_SNAKE_CASE							:   int						    =     (1, 1024, 768)
       self.assertEqual(output.shape       ,			UpperCAmelCase_   )
       # change to expected output here
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tf.convert_to_tensor(
           [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]       ,			)
       tf.debugging.assert_near(output[:, :3, :3]       ,			UpperCAmelCase_       ,			atol=1E-3   )






     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"   )

       # change to intended input here
       SCREAMING_SNAKE_CASE							:   Any						    =     _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     prepare_led_inputs_dict(model.config       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Any						    =     model(**UpperCAmelCase_   )[0]
       SCREAMING_SNAKE_CASE							:   Any						    =     (1, 1024, model.config.vocab_size)
       self.assertEqual(output.shape       ,			UpperCAmelCase_   )
       # change to expected output here
       SCREAMING_SNAKE_CASE							:   Tuple						    =     tf.convert_to_tensor(
           [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]       ,			)
       tf.debugging.assert_near(output[:, :3, :3]       ,			UpperCAmelCase_       ,			atol=1E-3       ,			rtol=1E-3   )













def   __lowerCamelCase   (				__a  :Dict       )						->							Optional[int]:






		"""simple docstring"""

		A__     =  []
		A__     =  []
		A__     =  {
		    """^""": 3,
		    """*""": 2,
		    """/""": 2,
		    """%""": 2,
		    """+""": 1,
		    """-""": 1,
		}  # Priority of each operator
		A__     =  len(__a       ) if (len(__a       ) > 7) else 7

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

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

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

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

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







def   __lowerCamelCase   (				__a  :List[str]       )						->							Union[str, Any]:






		"""simple docstring"""

		A__     =  list(infix[::-1]       )  # reverse the infix equation

		for i in range(len(__a       )       ):
				if infix[i] == "(":
						A__     =  """)"""  # change "(" to ")"
				elif infix[i] == ")":
						A__     =  """("""  # change ")" to "("

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


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



