


"""simple docstring"""


from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel




def   snake_case		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						str=None			,				UpperCamelCase__       :						Union[str, Any]=None    )   ->      Any:
      if attention_mask is None:
            lowerCamelCase					:					Any        =	tf.cast(tf.math.not_equal(UpperCamelCase__			,				config.pad_token_id    )			,				tf.inta    )
      return {"input_ids": input_ids, "attention_mask": attention_mask}


@require_tf
class 			A__	:


     """simple docstring"""
     snake_case__	:					Optional[Any]													=OPTConfig
     snake_case__	:					int													={}
     snake_case__	:					Dict													='''gelu'''

     def __init__(	self:						List[str]     ,		__a:						Optional[Any]     ,		__a:						List[str]=13     ,		__a:						Optional[Any]=7     ,		__a:						List[str]=True     ,		__a:						Tuple=False     ,		__a:						Tuple=99     ,		__a:						Optional[Any]=16     ,		__a:						List[str]=2     ,		__a:						Dict=4     ,		__a:						Any=4     ,		__a:						int="gelu"     ,		__a:						Optional[int]=0.1     ,		__a:						List[str]=0.1     ,		__a:						List[Any]=20     ,		__a:						Optional[Any]=2     ,		__a:						str=1     ,		__a:						Optional[Any]=0     ,		__a:						Optional[Any]=16     ,		__a:						Optional[Any]=16     ,		)->    Tuple:
           lowerCamelCase					:					Optional[int]        =	parent
           lowerCamelCase					:					List[str]        =	batch_size
           lowerCamelCase					:					int        =	seq_length
           lowerCamelCase					:					List[Any]        =	is_training
           lowerCamelCase					:					int        =	use_labels
           lowerCamelCase					:					Tuple        =	vocab_size
           lowerCamelCase					:					Any        =	hidden_size
           lowerCamelCase					:					List[str]        =	num_hidden_layers
           lowerCamelCase					:					List[str]        =	num_attention_heads
           lowerCamelCase					:					Dict        =	intermediate_size
           lowerCamelCase					:					str        =	hidden_act
           lowerCamelCase					:					int        =	hidden_dropout_prob
           lowerCamelCase					:					Optional[int]        =	attention_probs_dropout_prob
           lowerCamelCase					:					Optional[Any]        =	max_position_embeddings
           lowerCamelCase					:					Tuple        =	eos_token_id
           lowerCamelCase					:					List[str]        =	pad_token_id
           lowerCamelCase					:					List[str]        =	bos_token_id
           lowerCamelCase					:					List[Any]        =	embed_dim
           lowerCamelCase					:					Union[str, Any]        =	word_embed_proj_dim
           lowerCamelCase					:					str        =	False

     def 		a__						(	self:						Optional[Any]	)->    List[str]:
           lowerCamelCase					:					int        =	ids_tensor([self.batch_size, self.seq_length - 1]     ,		self.vocab_size	)
           lowerCamelCase					:					Union[str, Any]        =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)     ,		1	)
           lowerCamelCase					:					List[Any]        =	tf.concat([input_ids, eos_tensor]     ,		axis=1	)

           lowerCamelCase					:					List[Any]        =	self.config_cls(
               vocab_size=self.vocab_size     ,		hidden_size=self.hidden_size     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		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_id=self.eos_token_id     ,		bos_token_id=self.bos_token_id     ,		pad_token_id=self.pad_token_id     ,		embed_dim=self.embed_dim     ,		word_embed_proj_dim=self.word_embed_proj_dim     ,		is_encoder_decoder=__a     ,		**self.config_updates     ,		)
           lowerCamelCase					:					int        =	prepare_opt_inputs_dict(__a     ,		__a	)
           return config, inputs_dict







     def 		a__						(	self:						Optional[Any]     ,		__a:						Tuple     ,		__a:						Tuple	)->    List[str]:
           lowerCamelCase					:					str        =	TFOPTModel(config=__a	)
           lowerCamelCase					:					Any        =	inputs_dict["""input_ids"""]

           lowerCamelCase					:					Any        =	input_ids[:1, :]
           lowerCamelCase					:					str        =	inputs_dict["""attention_mask"""][:1, :]
           lowerCamelCase					:					Dict        =	1

           # first forward pass
           lowerCamelCase					:					List[str]        =	model(__a     ,		attention_mask=__a     ,		use_cache=__a	)

           lowerCamelCase					:					Tuple        =	outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           lowerCamelCase					:					Optional[int]        =	ids_tensor((self.batch_size, 3)     ,		config.vocab_size	)
           lowerCamelCase					:					Dict        =	tf.cast(ids_tensor((self.batch_size, 3)     ,		2	)     ,		tf.inta	)

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

           lowerCamelCase					:					Any        =	model(__a     ,		attention_mask=__a	)[0]
           lowerCamelCase					:					Optional[Any]        =	model(__a     ,		attention_mask=__a     ,		past_key_values=__a	)[0]

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

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

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


@require_tf
class 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					int													=(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
     snake_case__	:					Union[str, Any]													=(TFOPTForCausalLM,) if is_tf_available() else ()
     snake_case__	:					List[str]													=(
         {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
     )
     snake_case__	:					Dict													=False
     snake_case__	:					List[str]													=False
     snake_case__	:					List[Any]													=False
     snake_case__	:					Tuple													=10

     def 		a__						(	self:						Dict	)->    List[str]:
           lowerCamelCase					:					Tuple        =	TFOPTModelTester(self	)
           lowerCamelCase					:					List[Any]        =	ConfigTester(self     ,		config_class=__a	)

     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           self.config_tester.run_common_tests()

     def 		a__						(	self:						Any	)->    Dict:
           lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*__a	)







     def 		a__						(	self:						Dict	)->    Union[str, Any]:
           lowerCamelCase					:					Optional[int]        =	self.model_tester.prepare_config_and_inputs_for_common()

           def _get_word_embedding_weight(__a:						List[Any]     ,		__a:						Tuple	):
                 if hasattr(__a     ,		"""weight"""	):
                       return embedding_layer.weight
                 else:
                       # Here we build the word embeddings weights if not exists.
                       # And then we retry to get the attribute once built.
                       model.build()
                       if hasattr(__a     ,		"""weight"""	):
                             return embedding_layer.weight
                       else:
                             return None

           for model_class in self.all_model_classes:
                 for size in [config.vocab_size - 10, config.vocab_size + 10]:
                       # build the embeddings
                       lowerCamelCase					:					List[str]        =	model_class(config=__a	)
                       lowerCamelCase					:					Optional[Any]        =	_get_word_embedding_weight(__a     ,		model.get_input_embeddings()	)
                       lowerCamelCase					:					int        =	_get_word_embedding_weight(__a     ,		model.get_output_embeddings()	)

                       # reshape the embeddings
                       model.resize_token_embeddings(__a	)
                       lowerCamelCase					:					List[Any]        =	_get_word_embedding_weight(__a     ,		model.get_input_embeddings()	)
                       lowerCamelCase					:					Optional[int]        =	_get_word_embedding_weight(__a     ,		model.get_output_embeddings()	)

                       # check that the resized embeddings size matches the desired size.
                       lowerCamelCase					:					Tuple        =	size if size is not None else config.vocab_size

                       self.assertEqual(new_input_embeddings.shape[0]     ,		__a	)

                       # check that weights remain the same after resizing
                       lowerCamelCase					:					List[Any]        =	True
                       for pa, pa in zip(old_input_embeddings.value()     ,		new_input_embeddings.value()	):
                             if tf.math.reduce_sum(tf.math.abs(pa - pa	)	) > 0:
                                   lowerCamelCase					:					Dict        =	False
                       self.assertTrue(__a	)

                       if old_output_embeddings is not None and new_output_embeddings is not None:
                             self.assertEqual(new_output_embeddings.shape[0]     ,		__a	)

                             lowerCamelCase					:					Any        =	True
                             for pa, pa in zip(old_output_embeddings.value()     ,		new_output_embeddings.value()	):
                                   if tf.math.reduce_sum(tf.math.abs(pa - pa	)	) > 0:
                                         lowerCamelCase					:					Optional[int]        =	False
                             self.assertTrue(__a	)




def   snake_case		(     UpperCamelCase__       :						str    )   ->      Tuple:
      return tf.constant(UpperCamelCase__			,				dtype=tf.intaa    )


@require_tf
class 			A__	(						unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Tuple													=99

     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           lowerCamelCase					:					List[Any]        =	tf.ones((4, 1)     ,		dtype=tf.intaa	) * 2
           lowerCamelCase					:					Any        =	tf.concat([ids_tensor((4, 6)     ,		self.vocab_size - 3	) + 3, eos_column_vector]     ,		axis=1	)
           lowerCamelCase					:					Union[str, Any]        =	input_ids.shape[0]
           lowerCamelCase					:					List[str]        =	OPTConfig(
               vocab_size=self.vocab_size     ,		hidden_size=24     ,		num_hidden_layers=2     ,		num_attention_heads=2     ,		ffn_dim=32     ,		max_position_embeddings=48     ,		eos_token_id=2     ,		pad_token_id=1     ,		bos_token_id=0     ,		)
           return config, input_ids, batch_size


@require_sentencepiece
@require_tf
class 			A__	(						unittest.TestCase):


     """simple docstring"""

     @slow
     def 		a__						(	self:						List[str]	)->    int:
           lowerCamelCase					:					int        =	TFOPTModel.from_pretrained("""facebook/opt-350m"""	)
           lowerCamelCase					:					Tuple        =	_long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	)
           lowerCamelCase					:					str        =	tf.not_equal(__a     ,		model.config.pad_token_id	)
           with tf.GradientTape():
                 lowerCamelCase					:					Tuple        =	model(input_ids=__a     ,		attention_mask=__a	).last_hidden_state
           lowerCamelCase					:					List[Any]        =	(1, 11, 512)
           self.assertEqual(output.shape     ,		__a	)
           lowerCamelCase					:					Union[str, Any]        =	tf.constant(
               [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]]	)
           self.assertTrue(np.allclose(output[:, :3, :3]     ,		__a     ,		atol=4e-3	)	)

           lowerCamelCase					:					List[Any]        =	tf.function(__a     ,		jit_compile=__a	)
           lowerCamelCase					:					Optional[Any]        =	xla_generate(__a     ,		__a	)[0]
           self.assertTrue(np.allclose(output[:, :3, :3]     ,		__a     ,		atol=4e-2	)	)


@require_tf
@slow
class 			A__	(						unittest.TestCase):


     """simple docstring"""

     def 		a__						(	self:						str	)->    str:
           super().setUp()
           lowerCamelCase					:					List[str]        =	"""facebook/opt-350m"""







     def 		a__						(	self:						Optional[int]	)->    Tuple:
           lowerCamelCase					:					Optional[int]        =	TFOPTForCausalLM.from_pretrained(self.path_model	)
           lowerCamelCase					:					Optional[int]        =	GPTaTokenizer.from_pretrained(self.path_model	)

           lowerCamelCase					:					Dict        =	[
               """Today is a beautiful day and I want to""",
               """In the city of""",
               """Paris is the capital of France and""",
               """Computers and mobile phones have taken""",
           ]
           # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
           lowerCamelCase					:					Any        =	tokenizer(__a     ,		return_tensors="""tf"""     ,		padding=__a     ,		add_special_tokens=__a	)
           lowerCamelCase					:					int        =	tf.math.reduce_mean(model(inputs.input_ids     ,		attention_mask=inputs.attention_mask	)[0]     ,		axis=-1	)
           lowerCamelCase					:					Union[str, Any]        =	tf.constant(
               [
                   [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                   [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                   [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                   [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
               ]	)
           self.assertTrue(np.allclose(__a     ,		__a     ,		atol=1e-4	)	)

           lowerCamelCase					:					Dict        =	tf.function(__a     ,		jit_compile=__a	)
           lowerCamelCase					:					str        =	tf.math.reduce_mean(xla_generate(inputs.input_ids     ,		attention_mask=inputs.attention_mask	)[0]     ,		axis=-1	)
           self.assertTrue(np.allclose(__a     ,		__a     ,		atol=1e-4	)	)


@require_tf
@slow
class 			A__	(						unittest.TestCase):


     """simple docstring"""

     @property
     def 		a__						(	self:						str	)->    int:
           return [
               "Today is a beautiful day and I want",
               "In the city of",
               "Paris is the capital of France and",
               "Computers and mobile phones have taken",
           ]

     def 		a__						(	self:						Union[str, Any]	)->    Union[str, Any]:
           lowerCamelCase					:					int        =	"""facebook/opt-125m"""

           lowerCamelCase					:					Optional[int]        =	[
               """Today is a beautiful day and I want to""",
               """In the city of New York, the city""",
               """Paris is the capital of France and the capital""",
               """Computers and mobile phones have taken over the""",
           ]

           lowerCamelCase					:					List[str]        =	[]
           lowerCamelCase					:					Union[str, Any]        =	GPTaTokenizer.from_pretrained(__a	)
           lowerCamelCase					:					str        =	TFOPTForCausalLM.from_pretrained(__a	)

           for prompt in self.prompts:
                 lowerCamelCase					:					int        =	tokenizer(__a     ,		return_tensors="""tf"""	).input_ids

                 lowerCamelCase					:					Any        =	model.generate(__a     ,		max_length=10	)

                 lowerCamelCase					:					Tuple        =	tokenizer.batch_decode(__a     ,		skip_special_tokens=__a	)
                 predicted_outputs += generated_string

           self.assertListEqual(__a     ,		__a	)

     def 		a__						(	self:						List[Any]	)->    List[Any]:
           lowerCamelCase					:					Optional[int]        =	"""facebook/opt-350m"""

           lowerCamelCase					:					str        =	GPTaTokenizer.from_pretrained(__a	)
           lowerCamelCase					:					Tuple        =	TFOPTForCausalLM.from_pretrained(__a	)

           lowerCamelCase					:					int        =	"""left"""

           # use different length sentences to test batching
           lowerCamelCase					:					Any        =	[
               """Hello, my dog is a little""",
               """Today, I""",
           ]

           lowerCamelCase					:					Union[str, Any]        =	tokenizer(__a     ,		return_tensors="""tf"""     ,		padding=__a	)
           lowerCamelCase					:					Dict        =	inputs["""input_ids"""]

           lowerCamelCase					:					List[str]        =	model.generate(input_ids=__a     ,		attention_mask=inputs["""attention_mask"""]	)

           lowerCamelCase					:					Tuple        =	tokenizer(sentences[0]     ,		return_tensors="""tf"""	).input_ids
           lowerCamelCase					:					Dict        =	model.generate(input_ids=__a	)

           lowerCamelCase					:					List[str]        =	inputs_non_padded.shape[-1] - tf.math.reduce_sum(
               tf.cast(inputs["""attention_mask"""][-1]     ,		tf.intaa	)	)
           lowerCamelCase					:					str        =	tokenizer(sentences[1]     ,		return_tensors="""tf"""	).input_ids
           lowerCamelCase					:					Optional[int]        =	model.generate(input_ids=__a     ,		max_length=model.config.max_length - num_paddings	)

           lowerCamelCase					:					str        =	tokenizer.batch_decode(__a     ,		skip_special_tokens=__a	)
           lowerCamelCase					:					Optional[int]        =	tokenizer.decode(output_non_padded[0]     ,		skip_special_tokens=__a	)
           lowerCamelCase					:					List[str]        =	tokenizer.decode(output_padded[0]     ,		skip_special_tokens=__a	)

           lowerCamelCase					:					List[str]        =	[
               """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
               """Today, I was in the middle of a conversation with a friend about the""",
           ]
           self.assertListEqual(__a     ,		__a	)
           self.assertListEqual(__a     ,		[non_padded_sentence, padded_sentence]	)







     def 		a__						(	self:						Optional[int]	)->    str:
           lowerCamelCase					:					Optional[int]        =	"""facebook/opt-350m"""

           lowerCamelCase					:					Dict        =	[
               """Today is a beautiful day and I want to""",
               """In the city of San Francisco, the city""",
               """Paris is the capital of France and the capital""",
               """Computers and mobile phones have taken over the""",
           ]

           lowerCamelCase					:					Tuple        =	[]
           lowerCamelCase					:					Optional[int]        =	GPTaTokenizer.from_pretrained(__a	)
           lowerCamelCase					:					List[Any]        =	TFOPTForCausalLM.from_pretrained(__a	)

           for prompt in self.prompts:
                 lowerCamelCase					:					int        =	tokenizer(__a     ,		return_tensors="""tf"""	).input_ids

                 lowerCamelCase					:					Dict        =	model.generate(__a     ,		max_length=10	)

                 lowerCamelCase					:					Any        =	tokenizer.batch_decode(__a     ,		skip_special_tokens=__a	)
                 predicted_outputs += generated_string

           self.assertListEqual(__a     ,		__a	)








"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp


class 			A__	(						unittest.TestCase):


     """simple docstring"""

     def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					Tuple        =	[10, 20, 30, 40, 50, 60]
           lowerCamelCase					:					Union[str, Any]        =	[2, 4, 6, 8, 10, 12]
           lowerCamelCase					:					Union[str, Any]        =	100
           self.assertEqual(kp.calc_profit(__a     ,		__a     ,		__a	)     ,		210	)

     def 		a__						(	self:						str	)->    str:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)

     def 		a__						(	self:						str	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""Weight can not be negative."""	)

     def 		a__						(	self:						Any	)->    Dict:
           self.assertRaisesRegex(__a     ,		"""Profit can not be negative."""	)

     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)







     def 		a__						(	self:						Optional[Any]	)->    Tuple:
           self.assertRaisesRegex(
               __a     ,		"""The length of profit and weight must be same."""	)


if __name__ == "__main__":
    unittest.main()




