


"""simple docstring"""


import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, 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 LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer

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 LlamaConfig(
										    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=lowercase_					,     initializer_range=self.initializer_range					,     )

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    LlamaModel(config=lowercase_			)
										model.to(lowercase_			)
										model.eval()
										__lowerCAmelCase       					=    model(lowercase_					,     attention_mask=lowercase_			)
										__lowerCAmelCase       					=    model(lowercase_			)
										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       					=    LlamaModel(lowercase_			)
										model.to(lowercase_			)
										model.eval()
										__lowerCAmelCase       					=    model(
										    lowercase_					,     attention_mask=lowercase_					,     encoder_hidden_states=lowercase_					,     encoder_attention_mask=lowercase_					,     )
										__lowerCAmelCase       					=    model(
										    lowercase_					,     attention_mask=lowercase_					,     encoder_hidden_states=lowercase_					,     )
										__lowerCAmelCase       					=    model(lowercase_					,     attention_mask=lowercase_			)
										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       					=    LlamaForCausalLM(config=lowercase_			)
										model.to(lowercase_			)
										model.eval()
										__lowerCAmelCase       					=    model(lowercase_					,     attention_mask=lowercase_					,     labels=lowercase_			)
										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       					=    LlamaForCausalLM(config=lowercase_			)
										model.to(lowercase_			)
										model.eval()

										# first forward pass
										__lowerCAmelCase       					=    model(
										    lowercase_					,     attention_mask=lowercase_					,     encoder_hidden_states=lowercase_					,     encoder_attention_mask=lowercase_					,     use_cache=lowercase_					,     )
										__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(
										    lowercase_					,     attention_mask=lowercase_					,     encoder_hidden_states=lowercase_					,     encoder_attention_mask=lowercase_					,     output_hidden_states=lowercase_					,     )["""hidden_states"""][0]
										__lowerCAmelCase       					=    model(
										    lowercase_					,     attention_mask=lowercase_					,     encoder_hidden_states=lowercase_					,     encoder_attention_mask=lowercase_					,     past_key_values=lowercase_					,     output_hidden_states=lowercase_					,     )["""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(lowercase_					,     lowercase_					,     atol=1e-3			)			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										(
										    __lowerCAmelCase		
										)       					=    config_and_inputs
										__lowerCAmelCase       					=    {"""input_ids""": input_ids, """attention_mask""": input_mask}
										return config, inputs_dict

@require_torch
class 						_UpperCamelCase						(     _UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Union[str, Any]          =(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
							__UpperCAmelCase		:					str          =(LlamaForCausalLM,) if is_torch_available() else ()
							__UpperCAmelCase		:					List[Any]          =(
							    {
							        """feature-extraction""": LlamaModel,
							        """text-classification""": LlamaForSequenceClassification,
							        """text-generation""": LlamaForCausalLM,
							        """zero-shot""": LlamaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__UpperCAmelCase		:					int          =False
							__UpperCAmelCase		:					Any          =False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    LlamaModelTester(self			)
										__lowerCAmelCase       					=    ConfigTester(self					,     config_class=lowercase_					,     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(*lowercase_			)

							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(*lowercase_			)

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

							def     snake_case       (  self			):
										__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(lowercase_			)
										__lowerCAmelCase       					=    ids_tensor([self.model_tester.batch_size]					,     self.model_tester.type_sequence_label_size			)
										__lowerCAmelCase       					=    LlamaForSequenceClassification(lowercase_			)
										model.to(lowercase_			)
										model.eval()
										__lowerCAmelCase       					=    model(lowercase_					,     attention_mask=lowercase_					,     labels=lowercase_			)
										self.assertEqual(result.logits.shape					,     (self.model_tester.batch_size, self.model_tester.num_labels)			)

							def     snake_case       (  self			):
										__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(lowercase_			)
										__lowerCAmelCase       					=    ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]					,     self.model_tester.type_sequence_label_size			).to(torch.float			)
										__lowerCAmelCase       					=    LlamaForSequenceClassification(lowercase_			)
										model.to(lowercase_			)
										model.eval()
										__lowerCAmelCase       					=    model(lowercase_					,     attention_mask=lowercase_					,     labels=lowercase_			)
										self.assertEqual(result.logits.shape					,     (self.model_tester.batch_size, self.model_tester.num_labels)			)

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



							@parameterized.expand([("linear",), ("dynamic",)]			)
							def     snake_case       (  self					,     __a			):
										__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       					=    LlamaModel(lowercase_			)
										original_model.to(lowercase_			)
										original_model.eval()
										__lowerCAmelCase       					=    original_model(lowercase_			).last_hidden_state
										__lowerCAmelCase       					=    original_model(lowercase_			).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       					=    LlamaModel(lowercase_			)
										scaled_model.to(lowercase_			)
										scaled_model.eval()
										__lowerCAmelCase       					=    scaled_model(lowercase_			).last_hidden_state
										__lowerCAmelCase       					=    scaled_model(lowercase_			).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(lowercase_					,     lowercase_					,     atol=1e-5			)			)
										else:
													self.assertFalse(torch.allclose(lowercase_					,     lowercase_					,     atol=1e-5			)			)

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

@require_torch
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"			)
							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
										__lowerCAmelCase       					=    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"					,     device_map="auto"			)
										__lowerCAmelCase       					=    model(torch.tensor([input_ids]			)			)
										# Expected mean on dim = -1
										__lowerCAmelCase       					=    torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]			)
										torch.testing.assert_close(out.mean(-1			)					,     lowercase_					,     atol=1e-2					,     rtol=1e-2			)
										# slicing logits[0, 0, 0:30]
										# fmt: off
										__lowerCAmelCase       					=    torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]			)
										# fmt: on
										torch.testing.assert_close(out[0, 0, :30]					,     lowercase_					,     atol=1e-5					,     rtol=1e-5			)

							@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"			)
							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
										__lowerCAmelCase       					=    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"					,     device_map="auto"			)
										__lowerCAmelCase       					=    model(torch.tensor(lowercase_			)			)
										# Expected mean on dim = -1
										__lowerCAmelCase       					=    torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]			)
										torch.testing.assert_close(out.mean(-1			)					,     lowercase_					,     atol=1e-2					,     rtol=1e-2			)
										# slicing logits[0, 0, 0:30]
										# fmt: off
										__lowerCAmelCase       					=    torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]			)
										# fmt: on
										torch.testing.assert_close(out[0, 0, :30]					,     lowercase_					,     atol=1e-5					,     rtol=1e-5			)

							@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"			)
							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
										__lowerCAmelCase       					=    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"					,     device_map="auto"			)
										__lowerCAmelCase       					=    model(torch.tensor(lowercase_			)			)
										# Expected mean on dim = -1
										__lowerCAmelCase       					=    torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]			)
										torch.testing.assert_close(out.mean(-1			)					,     lowercase_					,     atol=1e-2					,     rtol=1e-2			)
										# slicing logits[0, 0, 0:30]
										# fmt: off
										__lowerCAmelCase       					=    torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]			)
										# fmt: on
										torch.testing.assert_close(out.mean(-1			)					,     lowercase_					,     atol=1e-2					,     rtol=1e-2			)

							@unittest.skip(
							    "Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"			)
							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
										__lowerCAmelCase       					=    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"					,     device_map="auto"			)
										__lowerCAmelCase       					=    model(torch.tensor(lowercase_			)			)

										__lowerCAmelCase       					=    torch.tensor(
										    [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]					,     dtype=torch.floataa			)
										torch.testing.assert_close(out.mean(-1			)					,     lowercase_					,     atol=1e-2					,     rtol=1e-2			)
										# fmt: off
										__lowerCAmelCase       					=    torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]			)
										# fmt: on
										torch.testing.assert_close(out[0, 0, :30]					,     lowercase_					,     atol=1e-5					,     rtol=1e-5			)



							@unittest.skip("Model is curently gated"			)
							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
										__lowerCAmelCase       					=    """Simply put, the theory of relativity states that """
										__lowerCAmelCase       					=    LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf"			)
										__lowerCAmelCase       					=    tokenizer.encode(lowercase_					,     return_tensors="pt"			)
										__lowerCAmelCase       					=    LlamaForCausalLM.from_pretrained(
										    "meta-llama/Llama-2-13b-chat-hf"					,     device_map="sequential"					,     use_safetensors=lowercase_			)

										# greedy generation outputs
										__lowerCAmelCase       					=    model.generate(lowercase_					,     max_new_tokens=64					,     top_p=lowercase_					,     temperature=1					,     do_sample=lowercase_			)
										__lowerCAmelCase       					=    tokenizer.decode(generated_ids[0]					,     skip_special_tokens=lowercase_			)
										self.assertEqual(lowercase_					,     lowercase_			)








"""simple docstring"""


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self					,     __a			):
										for model_result in results.values():
													for batch_size, sequence_length in zip(model_result["bs"]					,     model_result["ss"]			):
																__lowerCAmelCase       					=    model_result["result"][batch_size][sequence_length]
																self.assertIsNotNone(__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sgugger/tiny-distilbert-classification"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     only_pretrain_model=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result			)
										self.check_results_dict_not_empty(results.memory_train_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result			)
										self.check_results_dict_not_empty(results.memory_train_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "patrickvonplaten/t5-tiny-random"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     configs=[config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"			)			) == 0					,     "Cannot do xla on CPU."			)
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     use_xla=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]					,     inference=__a					,     save_to_csv=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     inference_time_csv_file=os.path.join(__a					,     "inf_time.csv"			)					,     inference_memory_csv_file=os.path.join(__a					,     "inf_mem.csv"			)					,     env_info_csv_file=os.path.join(__a					,     "env.csv"			)					,     multi_process=__a					,     )
													__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
													benchmark.run()
													self.assertTrue(Path(os.path.join(__a					,     "inf_time.csv"			)			).exists()			)
													self.assertTrue(Path(os.path.join(__a					,     "inf_mem.csv"			)			).exists()			)
													self.assertTrue(Path(os.path.join(__a					,     "env.csv"			)			).exists()			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"

										def _check_summary_is_not_empty(__a			):
													self.assertTrue(hasattr(__a					,     "sequential"			)			)
													self.assertTrue(hasattr(__a					,     "cumulative"			)			)
													self.assertTrue(hasattr(__a					,     "current"			)			)
													self.assertTrue(hasattr(__a					,     "total"			)			)

										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     log_filename=os.path.join(__a					,     "log.txt"			)					,     log_print=__a					,     trace_memory_line_by_line=__a					,     eager_mode=__a					,     multi_process=__a					,     )
													__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
													__lowerCAmelCase       					=    benchmark.run()
													_check_summary_is_not_empty(result.inference_summary			)
													self.assertTrue(Path(os.path.join(__a					,     "log.txt"			)			).exists()			)




