




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel







@require_tf
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       PegasusConfig
			snake_case_   		=       {}
			snake_case_   		=       '''gelu'''


			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__=13			,						lowerCamelCase__=7			,						lowerCamelCase__=True			,						lowerCamelCase__=False			,						lowerCamelCase__=99			,						lowerCamelCase__=32			,						lowerCamelCase__=2			,						lowerCamelCase__=4			,						lowerCamelCase__=37			,						lowerCamelCase__=0.1			,						lowerCamelCase__=0.1			,						lowerCamelCase__=40			,						lowerCamelCase__=2			,						lowerCamelCase__=1			,						lowerCamelCase__=0			,						)    ->      int:



							'''simple docstring'''
							__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_dropout_prob
							__lowerCamelCase		       =    attention_probs_dropout_prob
							__lowerCamelCase		       =    max_position_embeddings
							__lowerCamelCase		       =    eos_token_id
							__lowerCamelCase		       =    pad_token_id
							__lowerCamelCase		       =    bos_token_id


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    ids_tensor([self.batch_size, self.seq_length - 1]			,						self.vocab_size							)
							__lowerCamelCase		       =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)			,						1							)
							__lowerCamelCase		       =    tf.concat([input_ids, eos_tensor]			,						axis=1							)

							__lowerCamelCase		       =    ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size							)

							__lowerCamelCase		       =    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			,						**self.config_updates			,						)
							__lowerCamelCase		       =    prepare_pegasus_inputs_dict(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)
							return config, inputs_dict






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    TFPegasusModel(config=lowerCamelCase__							).get_decoder()
							__lowerCamelCase		       =    inputs_dict['input_ids']

							__lowerCamelCase		       =    input_ids[:1, :]
							__lowerCamelCase		       =    inputs_dict['attention_mask'][:1, :]
							__lowerCamelCase		       =    inputs_dict['head_mask']
							__lowerCamelCase		       =    1

							# first forward pass
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						head_mask=lowerCamelCase__			,						use_cache=lowerCamelCase__							)

							__lowerCamelCase       ,						__lowerCamelCase		       =    outputs.to_tuple()

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

							# append to next input_ids and
							__lowerCamelCase		       =    tf.concat([input_ids, next_tokens]			,						axis=-1							)
							__lowerCamelCase		       =    tf.concat([attention_mask, next_attn_mask]			,						axis=-1							)

							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__							)[0]
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						past_key_values=lowerCamelCase__							)[0]

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

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

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      Tuple=None ,     UpperCamelCase__				:      List[Any]=None ,     UpperCamelCase__				:      Any=None ,     UpperCamelCase__				:      int=None ,     UpperCamelCase__				:      List[str]=None ,     )     ->       int:





				"""simple docstring"""


				if attention_mask is None:
								__lowerCamelCase		       =    tf.cast(tf.math.not_equal(UpperCamelCase__ ,     config.pad_token_id      ) ,     tf.inta      )
				if decoder_attention_mask is None:
								__lowerCamelCase		       =    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:
								__lowerCamelCase		       =    tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
				if decoder_head_mask is None:
								__lowerCamelCase		       =    tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
				if cross_attn_head_mask is None:
								__lowerCamelCase		       =    tf.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": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}







@require_tf
class     __lowerCAmelCase (       __magic_name__			,				__magic_name__			,				unittest.TestCase ):

			"""simple docstring"""







			snake_case_   		=       (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
			snake_case_   		=       (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
			snake_case_   		=       (
			    {
			        '''conversational''': TFPegasusForConditionalGeneration,
			        '''feature-extraction''': TFPegasusModel,
			        '''summarization''': TFPegasusForConditionalGeneration,
			        '''text2text-generation''': TFPegasusForConditionalGeneration,
			        '''translation''': TFPegasusForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			snake_case_   		=       True
			snake_case_   		=       False
			snake_case_   		=       False


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    TFPegasusModelTester(self							)
							__lowerCamelCase		       =    ConfigTester(self			,						config_class=lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							self.config_tester.run_common_tests()






			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase__							)







@require_sentencepiece
@require_tokenizers
@require_tf
class     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			snake_case_   		=       [
			    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
			    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
			]
			snake_case_   		=       [
			    '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
			    ''' reduce the risk of wildfires.''',
			    '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
			]  # differs slightly from pytorch, likely due to numerical differences in linear layers
			snake_case_   		=       '''google/pegasus-xsum'''


			@cached_property
			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							return AutoTokenizer.from_pretrained(self.model_name							)


			@cached_property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
							return model


			def       lowercase_    (      self			,						**lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    self.translate_src_text(**lowerCamelCase__							)
							assert self.expected_text == generated_words


			def       lowercase_    (      self			,						**lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.tokenizer(self.src_text			,						**lowerCamelCase__			,						padding=lowerCamelCase__			,						return_tensors='tf'							)
							__lowerCamelCase		       =    self.model.generate(
							    model_inputs.input_ids			,						attention_mask=model_inputs.attention_mask			,						num_beams=2			,						use_cache=lowerCamelCase__			,						)
							__lowerCamelCase		       =    self.tokenizer.batch_decode(generated_ids.numpy()			,						skip_special_tokens=lowerCamelCase__							)
							return generated_words






			@slow
			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							self._assert_generated_batch_equal_expected()



"""simple docstring"""






import os

import numpy
import onnx



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							a.name
				UpperCAmelCase__             =							b.name

				UpperCAmelCase__             =							''
				UpperCAmelCase__             =							''

				UpperCAmelCase__             =							a == b

				UpperCAmelCase__             =							name_a
				UpperCAmelCase__             =							name_b

				return res



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for i, input_name in enumerate(node_proto.input       ):
								if input_name == name:
												node_proto.input.insert(lowerCamelCase					,			lowerCamelCase       )
												node_proto.input.pop(i + 1       )

				if node_proto.op_type == "If":
								_graph_replace_input_with(node_proto.attribute[0].g					,			lowerCamelCase					,			lowerCamelCase       )
								_graph_replace_input_with(node_proto.attribute[1].g					,			lowerCamelCase					,			lowerCamelCase       )
				if node_proto.op_type == "Loop":
								_graph_replace_input_with(node_proto.attribute[0].g					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							list(model.graph.initializer       )
				UpperCAmelCase__             =							list(model_without_ext.graph.initializer       )
				for i, ref_i in ind_to_replace:
								assert inits_with_data[i].name == inits[i].name
								assert inits_with_data[ref_i].name == inits[ref_i].name
								assert i > ref_i

								UpperCAmelCase__             =							inits[i].name
								UpperCAmelCase__             =							inits[ref_i].name

								model_without_ext.graph.initializer.remove(inits[i]       )

								# for n in model.graph.node:
								_graph_replace_input_with(model_without_ext.graph					,			lowerCamelCase					,			lowerCamelCase       )







def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							os.path.dirname(lowerCamelCase       )
				UpperCAmelCase__             =							os.path.basename(lowerCamelCase       )

				UpperCAmelCase__             =							onnx.load(os.path.join(lowerCamelCase					,			lowerCamelCase       )       )

				UpperCAmelCase__             =							list(model.graph.initializer       )

				UpperCAmelCase__             =							set()
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							[]

				UpperCAmelCase__             =							0

				for i in range(len(lowerCamelCase       )       ):
								if i in dup_set:
												continue

								for j in range(i + 1					,			len(lowerCamelCase       )       ):
												if j in dup_set:
																continue
												if _is_equal_tensor_proto(inits[i]					,			inits[j]       ):
																dup_set.add(lowerCamelCase       )
																dup_set.add(lowerCamelCase       )

																UpperCAmelCase__             =							inits[j].data_type
																UpperCAmelCase__             =							numpy.prod(inits[j].dims       )
																if dtype == 1:
																				mem_size *= 4
																elif dtype == 6:
																				mem_size *= 4
																elif dtype == 7 or dtype == 1_1:
																				mem_size *= 8
																else:
																				print('unexpected data type: '					,			lowerCamelCase       )
																total_reduced_size += mem_size

																UpperCAmelCase__             =							inits[i].name
																UpperCAmelCase__             =							inits[j].name

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCamelCase       )
																else:
																				UpperCAmelCase__             =							[name_j]
																ind_to_replace.append((j, i)       )

				print('total reduced size: '					,			total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4					,			'GB'       )

				UpperCAmelCase__             =							sorted(lowerCamelCase       )
				_remove_dup_initializers_from_model(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

				UpperCAmelCase__             =							'optimized_' + model_file_name
				UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			lowerCamelCase       )
				onnx.save(lowerCamelCase					,			lowerCamelCase       )

				return new_model



