


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       A    :
							UpperCamelCase_   :							List[str]       =BlenderbotConfig
							UpperCamelCase_   :							Optional[int]       ={}
							UpperCamelCase_   :							str       ='''gelu'''


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase=1_3      ,				lowerCAmelCase=7      ,				lowerCAmelCase=True      ,				lowerCAmelCase=False      ,				lowerCAmelCase=9_9      ,				lowerCAmelCase=3_2      ,				lowerCAmelCase=2      ,				lowerCAmelCase=4      ,				lowerCAmelCase=3_7      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=2_0      ,				lowerCAmelCase=2      ,				lowerCAmelCase=1      ,				lowerCAmelCase=0      ,				):
									__lowercase=			parent
									__lowercase=			batch_size
									__lowercase=			seq_length
									__lowercase=			is_training
									__lowercase=			use_labels
									__lowercase=			vocab_size
									__lowercase=			hidden_size
									__lowercase=			num_hidden_layers
									__lowercase=			num_attention_heads
									__lowercase=			intermediate_size

									__lowercase=			hidden_dropout_prob
									__lowercase=			attention_probs_dropout_prob
									__lowercase=			max_position_embeddings
									__lowercase=			eos_token_id
									__lowercase=			pad_token_id
									__lowercase=			bos_token_id


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

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

									__lowercase=			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      ,				)
									__lowercase=			prepare_blenderbot_inputs_dict(lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					)
									return config, inputs_dict







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			TFBlenderbotModel(config=lowerCAmelCase					).get_decoder()
									__lowercase=			inputs_dict['input_ids']

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

									# first forward pass
									__lowercase=			model(lowerCAmelCase      ,				attention_mask=lowerCAmelCase      ,				head_mask=lowerCAmelCase      ,				use_cache=lowerCAmelCase					)

									__lowercase,							__lowercase=			outputs.to_tuple()

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

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

									__lowercase=			model(lowerCAmelCase      ,				attention_mask=lowerCAmelCase					)[0]
									__lowercase=			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
									__lowercase=			int(ids_tensor((1,)      ,				output_from_past.shape[-1]					)					)
									__lowercase=			output_from_no_past[:, -3:, random_slice_idx]
									__lowercase=			output_from_past[:, :, random_slice_idx]

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







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__   , lowercase__=None   , lowercase__=None   , lowercase__=None   , lowercase__=None   , lowercase__=None   , )						->	int:

		'''simple docstring'''







		if attention_mask is None:
				__lowercase=			tf.cast(tf.math.not_equal(lowercase__   , config.pad_token_id	)   , tf.inta	)
		if decoder_attention_mask is None:
				__lowercase=			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:
				__lowercase=			tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
		if decoder_head_mask is None:
				__lowercase=			tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
		if cross_attn_head_mask is None:
				__lowercase=			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       A    (   A_					,				A_					,				unittest.TestCase ):
							UpperCamelCase_   :							str       =(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
							UpperCamelCase_   :							Any       =(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
							UpperCamelCase_   :							Dict       =(
							    {
							        '''conversational''': TFBlenderbotForConditionalGeneration,
							        '''feature-extraction''': TFBlenderbotModel,
							        '''summarization''': TFBlenderbotForConditionalGeneration,
							        '''text2text-generation''': TFBlenderbotForConditionalGeneration,
							        '''translation''': TFBlenderbotForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							UpperCamelCase_   :							List[Any]       =True
							UpperCamelCase_   :							Union[str, Any]       =False
							UpperCamelCase_   :							List[Any]       =False


							def 			_A       (self					):
									__lowercase=			TFBlenderbotModelTester(self					)
									__lowercase=			ConfigTester(self      ,				config_class=lowerCAmelCase					)


							def 			_A       (self					):
									self.config_tester.run_common_tests()







							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase					)





@require_tokenizers
@require_tf
class       A    (   unittest.TestCase ):
							UpperCamelCase_   :							List[Any]       =['''My friends are cool but they eat too many carbs.''']
							UpperCamelCase_   :							Dict       ='''facebook/blenderbot-400M-distill'''


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


							@cached_property
							def 			_A       (self					):
									__lowercase=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
									return model







							@slow
							def 			_A       (self					):
									__lowercase=			self.tokenizer(self.src_text      ,				return_tensors='tf'					)
									__lowercase=			self.model.generate(
									    model_inputs.input_ids      ,				)
									__lowercase=			self.tokenizer.batch_decode(generated_ids.numpy()      ,				skip_special_tokens=lowerCAmelCase					)[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






