


"""simple docstring"""


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=13					,     __a=7					,     __a=True					,     __a=True					,     __a=False					,     __a=True					,     __a=99					,     __a=32					,     __a=5					,     __a=4					,     __a=37					,     __a="gelu"					,     __a=0.1					,     __a=0.1					,     __a=5_12					,     __a=16					,     __a=2					,     __a=0.0_2					,     __a=3					,     __a=4					,     __a=None					,     ):
										__lowerCAmelCase       					=    parent
										__lowerCAmelCase       					=    batch_size
										__lowerCAmelCase       					=    seq_length
										__lowerCAmelCase       					=    is_training
										__lowerCAmelCase       					=    use_input_mask
										__lowerCAmelCase       					=    use_token_type_ids
										__lowerCAmelCase       					=    use_labels
										__lowerCAmelCase       					=    vocab_size
										__lowerCAmelCase       					=    hidden_size
										__lowerCAmelCase       					=    num_hidden_layers
										__lowerCAmelCase       					=    num_attention_heads
										__lowerCAmelCase       					=    intermediate_size
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    hidden_dropout_prob
										__lowerCAmelCase       					=    attention_probs_dropout_prob
										__lowerCAmelCase       					=    max_position_embeddings
										__lowerCAmelCase       					=    type_vocab_size
										__lowerCAmelCase       					=    type_sequence_label_size
										__lowerCAmelCase       					=    initializer_range
										__lowerCAmelCase       					=    num_labels
										__lowerCAmelCase       					=    num_choices
										__lowerCAmelCase       					=    scope

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ids_tensor([self.batch_size, self.seq_length]					,     self.vocab_size			)

										__lowerCAmelCase       					=    None
										if self.use_input_mask:
													__lowerCAmelCase       					=    random_attention_mask([self.batch_size, self.seq_length]			)

										__lowerCAmelCase       					=    None
										if self.use_token_type_ids:
													__lowerCAmelCase       					=    ids_tensor([self.batch_size, self.seq_length]					,     self.type_vocab_size			)

										__lowerCAmelCase       					=    None
										__lowerCAmelCase       					=    None
										__lowerCAmelCase       					=    None
										if self.use_labels:
													__lowerCAmelCase       					=    ids_tensor([self.batch_size]					,     self.type_sequence_label_size			)
													__lowerCAmelCase       					=    ids_tensor([self.batch_size, self.seq_length]					,     self.num_labels			)
													__lowerCAmelCase       					=    ids_tensor([self.batch_size]					,     self.num_choices			)

										__lowerCAmelCase       					=    self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

							def     snake_case       (  self			):
										return OpenLlamaConfig(
										    vocab_size=self.vocab_size					,     hidden_size=self.hidden_size					,     num_hidden_layers=self.num_hidden_layers					,     num_attention_heads=self.num_attention_heads					,     intermediate_size=self.intermediate_size					,     hidden_act=self.hidden_act					,     hidden_dropout_prob=self.hidden_dropout_prob					,     attention_probs_dropout_prob=self.attention_probs_dropout_prob					,     max_position_embeddings=self.max_position_embeddings					,     type_vocab_size=self.type_vocab_size					,     is_decoder=__a					,     initializer_range=self.initializer_range					,     use_stable_embedding=__a					,     )

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    OpenLlamaModel(config=__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a					,     attention_mask=__a			)
										__lowerCAmelCase       					=    model(__a			)
										self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     ):
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    OpenLlamaModel(__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(
										    __a					,     attention_mask=__a					,     encoder_hidden_states=__a					,     encoder_attention_mask=__a					,     )
										__lowerCAmelCase       					=    model(
										    __a					,     attention_mask=__a					,     encoder_hidden_states=__a					,     )
										__lowerCAmelCase       					=    model(__a					,     attention_mask=__a			)
										self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     ):
										__lowerCAmelCase       					=    OpenLlamaForCausalLM(config=__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a					,     attention_mask=__a					,     labels=__a			)
										self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     ):
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    OpenLlamaForCausalLM(config=__a			)
										model.to(__a			)
										model.eval()

										# first forward pass
										__lowerCAmelCase       					=    model(
										    __a					,     attention_mask=__a					,     encoder_hidden_states=__a					,     encoder_attention_mask=__a					,     use_cache=__a					,     )
										__lowerCAmelCase       					=    outputs.past_key_values

										# create hypothetical multiple next token and extent to next_input_ids
										__lowerCAmelCase       					=    ids_tensor((self.batch_size, 3)					,     config.vocab_size			)
										__lowerCAmelCase       					=    ids_tensor((self.batch_size, 3)					,     vocab_size=2			)

										# append to next input_ids and
										__lowerCAmelCase       					=    torch.cat([input_ids, next_tokens]					,     dim=-1			)
										__lowerCAmelCase       					=    torch.cat([input_mask, next_mask]					,     dim=-1			)

										__lowerCAmelCase       					=    model(
										    __a					,     attention_mask=__a					,     encoder_hidden_states=__a					,     encoder_attention_mask=__a					,     output_hidden_states=__a					,     )["hidden_states"][0]
										__lowerCAmelCase       					=    model(
										    __a					,     attention_mask=__a					,     encoder_hidden_states=__a					,     encoder_attention_mask=__a					,     past_key_values=__a					,     output_hidden_states=__a					,     )["hidden_states"][0]

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

										self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]			)

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(__a					,     __a					,     atol=1e-3			)			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										(
										    (
										    __lowerCAmelCase
										)					,			(
										    __lowerCAmelCase
										)					,			(
										    __lowerCAmelCase
										)					,			(
										    __lowerCAmelCase
										)					,			(
										    __lowerCAmelCase
										)					,			(
										    __lowerCAmelCase
										)					,			(
										    __lowerCAmelCase
										)					,			
										)       					=    config_and_inputs
										__lowerCAmelCase       					=    {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict
@require_torch
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Optional[Any]          =(
							    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
							)
							__UpperCAmelCase		:					Optional[Any]          =(OpenLlamaForCausalLM,) if is_torch_available() else ()
							__UpperCAmelCase		:					Optional[int]          =(
							    {
							        """feature-extraction""": OpenLlamaModel,
							        """text-classification""": OpenLlamaForSequenceClassification,
							        """text-generation""": OpenLlamaForCausalLM,
							        """zero-shot""": OpenLlamaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__UpperCAmelCase		:					List[str]          =False
							__UpperCAmelCase		:					Any          =False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    OpenLlamaModelTester(self			)
										__lowerCAmelCase       					=    ConfigTester(self					,     config_class=__a					,     hidden_size=37			)

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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
													__lowerCAmelCase       					=    type
													self.model_tester.create_and_check_model(*__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase       					=    3
										__lowerCAmelCase       					=    input_dict["input_ids"]
										__lowerCAmelCase       					=    input_ids.ne(1			).to(__a			)
										__lowerCAmelCase       					=    ids_tensor([self.model_tester.batch_size]					,     self.model_tester.type_sequence_label_size			)
										__lowerCAmelCase       					=    OpenLlamaForSequenceClassification(__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a					,     attention_mask=__a					,     labels=__a			)
										self.assertEqual(result.logits.shape					,     (self.model_tester.batch_size, self.model_tester.num_labels)			)

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase       					=    3
										__lowerCAmelCase       					=    "single_label_classification"
										__lowerCAmelCase       					=    input_dict["input_ids"]
										__lowerCAmelCase       					=    input_ids.ne(1			).to(__a			)
										__lowerCAmelCase       					=    ids_tensor([self.model_tester.batch_size]					,     self.model_tester.type_sequence_label_size			)
										__lowerCAmelCase       					=    OpenLlamaForSequenceClassification(__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a					,     attention_mask=__a					,     labels=__a			)
										self.assertEqual(result.logits.shape					,     (self.model_tester.batch_size, self.model_tester.num_labels)			)

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase       					=    3
										__lowerCAmelCase       					=    "multi_label_classification"
										__lowerCAmelCase       					=    input_dict["input_ids"]
										__lowerCAmelCase       					=    input_ids.ne(1			).to(__a			)
										__lowerCAmelCase       					=    ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]					,     self.model_tester.type_sequence_label_size			).to(torch.float			)
										__lowerCAmelCase       					=    OpenLlamaForSequenceClassification(__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a					,     attention_mask=__a					,     labels=__a			)
										self.assertEqual(result.logits.shape					,     (self.model_tester.batch_size, self.model_tester.num_labels)			)

							@unittest.skip("Open-Llama buffers include complex numbers, which breaks this test"			)
							def     snake_case       (  self			):
										pass



							@parameterized.expand([("linear",), ("dynamic",)]			)
							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase       					=    ids_tensor([1, 10]					,     config.vocab_size			)
										__lowerCAmelCase       					=    ids_tensor([1, int(config.max_position_embeddings * 1.5			)]					,     config.vocab_size			)

										set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
										__lowerCAmelCase       					=    OpenLlamaModel(__a			)
										original_model.to(__a			)
										original_model.eval()
										__lowerCAmelCase       					=    original_model(__a			).last_hidden_state
										__lowerCAmelCase       					=    original_model(__a			).last_hidden_state

										set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
										__lowerCAmelCase       					=    {"type": scaling_type, "factor": 1_0.0}
										__lowerCAmelCase       					=    OpenLlamaModel(__a			)
										scaled_model.to(__a			)
										scaled_model.eval()
										__lowerCAmelCase       					=    scaled_model(__a			).last_hidden_state
										__lowerCAmelCase       					=    scaled_model(__a			).last_hidden_state

										# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
										# maximum sequence length, so the outputs for the short input should match.
										if scaling_type == "dynamic":
													self.assertTrue(torch.allclose(__a					,     __a					,     atol=1e-5			)			)
										else:
													self.assertFalse(torch.allclose(__a					,     __a					,     atol=1e-5			)			)

										# The output should be different for long inputs
										self.assertFalse(torch.allclose(__a					,     __a					,     atol=1e-5			)			)








"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Tuple          =[
							    # (model_name, model_kwargs)
							    ("""bert-base-cased""", {}),
							    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
							]

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "tf"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "pt"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
										with NamedTemporaryFile(mode="w+t"			) as vocab_file:
													vocab_file.write("\n".join(__a			)			)
													vocab_file.flush()
													__lowerCAmelCase       					=    BertTokenizerFast(vocab_file.name			)

										with TemporaryDirectory() as bert_save_dir:
													__lowerCAmelCase       					=    BertModel(BertConfig(vocab_size=len(__a			)			)			)
													model.save_pretrained(__a			)
													self._test_export(__a					,     "pt"					,     12					,     __a			)

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "tf"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(Path(__a			)			)

													# Ensure the actual quantized model is not bigger than the original one
													if quantized_path.stat().st_size >= Path(__a			).stat().st_size:
																self.fail("Quantized model is bigger than initial ONNX model"			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "pt"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(__a			)

													# Ensure the actual quantized model is not bigger than the original one
													if quantized_path.stat().st_size >= Path(__a			).stat().st_size:
																self.fail("Quantized model is bigger than initial ONNX model"			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a=None					,     **__a			):
										try:
													# Compute path
													with TemporaryDirectory() as tempdir:
																__lowerCAmelCase       					=    Path(__a			).joinpath("model.onnx"			)

													# Remove folder if exists
													if path.parent.exists():
																path.parent.rmdir()

													# Export
													convert(__a					,     __a					,     __a					,     __a					,     __a					,     **__a			)

													return path
										except Exception as e:
													self.fail(__a			)

							@require_torch
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "pt"			)

							@require_tf
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import TFBertModel

										__lowerCAmelCase       					=    TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "tf"			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    FeatureExtractionPipeline(__a					,     __a			)

										__lowerCAmelCase       					=    ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    infer_shapes(__a					,     __a			)

										# Assert all variables are present
										self.assertEqual(len(__a			)					,     len(__a			)			)
										self.assertTrue(all(var_name in shapes for var_name in variable_names			)			)
										self.assertSequenceEqual(variable_names[:3]					,     __a			)
										self.assertSequenceEqual(variable_names[3:]					,     __a			)

										# Assert inputs are {0: batch, 1: sequence}
										for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
													self.assertDictEqual(shapes[var_name]					,     {0: "batch", 1: "sequence"}			)

										# Assert outputs are {0: batch, 1: sequence} and {0: batch}
										self.assertDictEqual(shapes["output_0"]					,     {0: "batch", 1: "sequence"}			)
										self.assertDictEqual(shapes["output_1"]					,     {0: "batch"}			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ["input_ids", "attention_mask", "token_type_ids"]
										__lowerCAmelCase       					=    {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
										__lowerCAmelCase					,			__lowerCAmelCase       					=    ensure_valid_input(FuncContiguousArgs()					,     __a					,     __a			)

										# Should have exactly the same number of args (all are valid)
										self.assertEqual(len(__a			)					,     3			)

										# Should have exactly the same input names
										self.assertEqual(set(__a			)					,     set(__a			)			)

										# Parameter should be reordered according to their respective place in the function:
										# (input_ids, token_type_ids, attention_mask)
										self.assertEqual(__a					,     (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])			)

										# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
										__lowerCAmelCase					,			__lowerCAmelCase       					=    ensure_valid_input(FuncNonContiguousArgs()					,     __a					,     __a			)

										# Should have exactly the one arg (all before the one not provided "some_other_args")
										self.assertEqual(len(__a			)					,     1			)
										self.assertEqual(len(__a			)					,     1			)

										# Should have only "input_ids"
										self.assertEqual(inputs_args[0]					,     tokens["input_ids"]			)
										self.assertEqual(ordered_input_names[0]					,     "input_ids"			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    generate_identified_filename(Path("/home/something/my_fake_model.onnx"			)					,     "-test"			)
										self.assertEqual("/home/something/my_fake_model-test.onnx"					,     generated.as_posix()			)




