

"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel







@require_tf
class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       BlenderbotConfig
					lowercase__				 =       {}
					lowercase__				 =       '''gelu'''







					def __init__(       self			:				Union[str, Any]   ,lowercase_			:				Optional[int]   ,lowercase_			:				int=1_3   ,lowercase_			:				Tuple=7   ,lowercase_			:				Tuple=True   ,lowercase_			:				Optional[int]=False   ,lowercase_			:				List[Any]=9_9   ,lowercase_			:				List[str]=3_2   ,lowercase_			:				Dict=2   ,lowercase_			:				Optional[Any]=4   ,lowercase_			:				int=3_7   ,lowercase_			:				int=0.1   ,lowercase_			:				Union[str, Any]=0.1   ,lowercase_			:				Optional[Any]=2_0   ,lowercase_			:				Optional[Any]=2   ,lowercase_			:				Tuple=1   ,lowercase_			:				str=0   ,):
						lowerCAmelCase__	:						List[Any]  				=	parent
						lowerCAmelCase__	:						List[Any]  				=	batch_size
						lowerCAmelCase__	:						Optional[Any]  				=	seq_length
						lowerCAmelCase__	:						int  				=	is_training
						lowerCAmelCase__	:						List[str]  				=	use_labels
						lowerCAmelCase__	:						Tuple  				=	vocab_size
						lowerCAmelCase__	:						Dict  				=	hidden_size
						lowerCAmelCase__	:						Union[str, Any]  				=	num_hidden_layers
						lowerCAmelCase__	:						Union[str, Any]  				=	num_attention_heads
						lowerCAmelCase__	:						str  				=	intermediate_size

						lowerCAmelCase__	:						Union[str, Any]  				=	hidden_dropout_prob
						lowerCAmelCase__	:						str  				=	attention_probs_dropout_prob
						lowerCAmelCase__	:						int  				=	max_position_embeddings
						lowerCAmelCase__	:						Tuple  				=	eos_token_id
						lowerCAmelCase__	:						Dict  				=	pad_token_id
						lowerCAmelCase__	:						List[Any]  				=	bos_token_id







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Union[str, Any]  				=	ids_tensor([self.batch_size, self.seq_length - 1]   ,self.vocab_size		)
						lowerCAmelCase__	:						Optional[Any]  				=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)   ,1		)
						lowerCAmelCase__	:						Dict  				=	tf.concat([input_ids, eos_tensor]   ,axis=1		)

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

						lowerCAmelCase__	:						Optional[Any]  				=	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__	:						List[Any]  				=	prepare_blenderbot_inputs_dict(lowercase_   ,lowercase_   ,lowercase_		)
						return config, inputs_dict







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				Optional[Any]   ,lowercase_			:				Dict		):
						lowerCAmelCase__	:						Optional[int]  				=	TFBlenderbotModel(config=lowercase_		).get_decoder()
						lowerCAmelCase__	:						Union[str, Any]  				=	inputs_dict['''input_ids''']

						lowerCAmelCase__	:						Optional[Any]  				=	input_ids[:1, :]
						lowerCAmelCase__	:						int  				=	inputs_dict['''attention_mask'''][:1, :]
						lowerCAmelCase__	:						int  				=	inputs_dict['''head_mask''']
						lowerCAmelCase__	:						int  				=	1

						# first forward pass
						lowerCAmelCase__	:						Tuple  				=	model(lowercase_   ,attention_mask=lowercase_   ,head_mask=lowercase_   ,use_cache=lowercase_		)

						lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						lowerCAmelCase__	:						Any  				=	ids_tensor((self.batch_size, 3)   ,config.vocab_size		)
						lowerCAmelCase__	:						List[Any]  				=	tf.cast(ids_tensor((self.batch_size, 3)   ,2		)   ,tf.inta		)

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

						lowerCAmelCase__	:						Optional[Any]  				=	model(lowercase_   ,attention_mask=lowercase_		)[0]
						lowerCAmelCase__	:						Tuple  				=	model(lowercase_   ,attention_mask=lowercase_   ,past_key_values=lowercase_		)[0]

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

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

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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_=None						,     A_=None						,     A_=None						,     A_=None						,     A_=None						,     ):







	if attention_mask is None:
		lowerCAmelCase__	:						Any  				=	tf.cast(tf.math.not_equal(lowercase__						,     config.pad_token_id )						,     tf.inta )
	if decoder_attention_mask is None:
		lowerCAmelCase__	:						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:
		lowerCAmelCase__	:						Optional[int]  				=	tf.ones((config.encoder_layers, config.encoder_attention_heads) )
	if decoder_head_mask is None:
		lowerCAmelCase__	:						List[str]  				=	tf.ones((config.decoder_layers, config.decoder_attention_heads) )
	if cross_attn_head_mask is None:
		lowerCAmelCase__	:						Any  				=	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 				SCREAMING_SNAKE_CASE					(		A_    ,							A_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
					lowercase__				 =       (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
					lowercase__				 =       (
					    {
					        '''conversational''': TFBlenderbotForConditionalGeneration,
					        '''feature-extraction''': TFBlenderbotModel,
					        '''summarization''': TFBlenderbotForConditionalGeneration,
					        '''text2text-generation''': TFBlenderbotForConditionalGeneration,
					        '''translation''': TFBlenderbotForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					lowercase__				 =       True
					lowercase__				 =       False
					lowercase__				 =       False







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						List[str]  				=	TFBlenderbotModelTester(self		)
						lowerCAmelCase__	:						Optional[Any]  				=	ConfigTester(self   ,config_class=lowercase_		)







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







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Any  				=	self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*lowercase_		)







@require_tokenizers
@require_tf
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       ['''My friends are cool but they eat too many carbs.''']
					lowercase__				 =       '''facebook/blenderbot-400M-distill'''







					@cached_property
					def        __lowerCAmelCase (       self			:				str		):
						return BlenderbotTokenizer.from_pretrained(self.model_name		)







					@cached_property
					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						List[Any]  				=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
						return model







					@slow
					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.tokenizer(self.src_text   ,return_tensors='''tf'''		)
						lowerCAmelCase__	:						Any  				=	self.model.generate(
						    model_inputs.input_ids   ,)
						lowerCAmelCase__	:						Optional[Any]  				=	self.tokenizer.batch_decode(generated_ids.numpy()   ,skip_special_tokens=lowercase_		)[0]
						assert (
						    generated_words
						    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
						)








import os

import numpy
import onnx







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			a.name
		__lowercase=			b.name

		__lowercase=			''
		__lowercase=			''

		__lowercase=			a == b

		__lowercase=			name_a
		__lowercase=			name_b

		return res







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







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

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







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	str:

		'''simple docstring'''







		for n in graph_proto.node:
				_node_replace_input_with(lowercase__   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		__lowercase=			list(model.graph.initializer	)
		__lowercase=			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

				__lowercase=			inits[i].name
				__lowercase=			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   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__	)						->	Dict:

		'''simple docstring'''







		__lowercase=			os.path.dirname(lowercase__	)
		__lowercase=			os.path.basename(lowercase__	)

		__lowercase=			onnx.load(os.path.join(lowercase__   , lowercase__	)	)

		__lowercase=			list(model.graph.initializer	)

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

		__lowercase=			0

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

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

								__lowercase=			inits[j].data_type
								__lowercase=			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: '   , lowercase__	)
								total_reduced_size += mem_size

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

								if name_i in dup_map:
										dup_map[name_i].append(lowercase__	)
								else:
										__lowercase=			[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'	)

		__lowercase=			sorted(lowercase__	)
		_remove_dup_initializers_from_model(lowercase__   , lowercase__   , lowercase__	)

		__lowercase=			'optimized_' + model_file_name
		__lowercase=			os.path.join(lowercase__   , lowercase__	)
		onnx.save(lowercase__   , lowercase__	)

		return new_model






