


"""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": 10.0}
										__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"""


from manim import *
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    Rectangle(height=0.5					,     width=0.5			)
										__lowerCAmelCase       					=    Rectangle(height=0.4_6					,     width=0.4_6			).set_stroke(width=0			)

										__lowerCAmelCase       					=    Rectangle(height=0.2_5					,     width=0.2_5			)

										__lowerCAmelCase       					=    [mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    [mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(__a					,     __a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    Text("CPU"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										cpu.move_to([-2.5, -0.5, 0]			)
										self.add(__a			)

										__lowerCAmelCase       					=    [mem.copy() for i in range(4			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    Text("GPU"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										gpu.move_to([-1, -1, 0]			)
										self.add(__a			)

										__lowerCAmelCase       					=    [mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)

										__lowerCAmelCase       					=    Text("Model"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										model.move_to([3, -1.0, 0]			)
										self.add(__a			)

										__lowerCAmelCase       					=    []
										__lowerCAmelCase       					=    []

										for i, rect in enumerate(__a			):
													__lowerCAmelCase       					=    fill.copy().set_fill(__a					,     opacity=0.8			)
													target.move_to(__a			)
													model_arr.append(__a			)

													__lowerCAmelCase       					=    Rectangle(height=0.4_6					,     width=0.4_6			).set_stroke(width=0.0			).set_fill(__a					,     opacity=0.8			)
													cpu_target.move_to(cpu_left_col_base[i]			)
													model_cpu_arr.append(__a			)

										self.add(*__a					,     *__a			)

										__lowerCAmelCase       					=    [meta_mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    [meta_mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(__a					,     __a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    Text("Disk"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										disk.move_to([-4, -1.2_5, 0]			)
										self.add(__a					,     __a			)

										__lowerCAmelCase       					=    Square(side_length=2.2			)
										key.move_to([-5, 2, 0]			)

										__lowerCAmelCase       					=    MarkupText(
										    f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"					,     font_size=18					,     )

										key_text.move_to([-5, 2.4, 0]			)

										self.add(__a					,     __a			)

										__lowerCAmelCase       					=    MarkupText(
										    f"<span fgcolor='{BLUE}'>●</span> Checkpoint"					,     font_size=18					,     )

										blue_text.next_to(__a					,     DOWN * 2.4					,     aligned_edge=key_text.get_left()			)
										self.add(__a			)

										__lowerCAmelCase       					=    MarkupText(
										    f"Now watch as an input is passed through the model\nand how the memory is utilized and handled."					,     font_size=24					,     )
										step_a.move_to([2, 2, 0]			)

										self.play(Write(__a			)			)

										__lowerCAmelCase       					=    Square(0.3			)
										input.set_fill(__a					,     opacity=1.0			)
										input.set_stroke(width=0.0			)
										input.next_to(model_base[0]					,     __a					,     buff=0.5			)

										self.play(Write(__a			)			)

										input.generate_target()
										input.target.next_to(model_arr[0]					,     direction=__a					,     buff=0.0_2			)
										self.play(MoveToTarget(__a			)			)

										self.play(FadeOut(__a			)			)

										__lowerCAmelCase       					=    Arrow(start=__a					,     end=__a					,     color=__a					,     buff=0.5			)
										a.next_to(model_arr[0].get_left()					,     __a					,     buff=0.2			)

										model_cpu_arr[0].generate_target()
										model_cpu_arr[0].target.move_to(gpu_rect[0]			)

										__lowerCAmelCase       					=    MarkupText(
										    f"As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."					,     font_size=24					,     )
										step_a.move_to([2, 2, 0]			)

										self.play(Write(__a					,     run_time=3			)			)

										__lowerCAmelCase       					=    {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.0_2}

										self.play(
										    Write(__a			)					,     Circumscribe(model_arr[0]					,     color=__a					,     **__a			)					,     Circumscribe(model_cpu_arr[0]					,     color=__a					,     **__a			)					,     Circumscribe(gpu_rect[0]					,     color=__a					,     **__a			)					,     )
										self.play(MoveToTarget(model_cpu_arr[0]			)			)

										__lowerCAmelCase       					=    a.copy()
										for i in range(6			):
													a_c.next_to(model_arr[i].get_right() + 0.0_2					,     __a					,     buff=0.2			)

													input.generate_target()
													input.target.move_to(model_arr[i].get_right() + 0.0_2			)

													__lowerCAmelCase       					=    AnimationGroup(
													    FadeOut(__a					,     run_time=0.5			)					,     MoveToTarget(__a					,     run_time=0.5			)					,     FadeIn(__a					,     run_time=0.5			)					,     lag_ratio=0.2			)

													self.play(__a			)

													model_cpu_arr[i].generate_target()
													model_cpu_arr[i].target.move_to(cpu_left_col_base[i]			)

													if i < 5:
																model_cpu_arr[i + 1].generate_target()
																model_cpu_arr[i + 1].target.move_to(gpu_rect[0]			)
																if i >= 1:
																			__lowerCAmelCase       					=    0.7

																self.play(
																    Circumscribe(model_arr[i]					,     **__a			)					,     Circumscribe(cpu_left_col_base[i]					,     **__a			)					,     Circumscribe(cpu_left_col_base[i + 1]					,     color=__a					,     **__a			)					,     Circumscribe(gpu_rect[0]					,     color=__a					,     **__a			)					,     Circumscribe(model_arr[i + 1]					,     color=__a					,     **__a			)					,     )
																if i < 1:
																			self.play(
																			    MoveToTarget(model_cpu_arr[i]			)					,     MoveToTarget(model_cpu_arr[i + 1]			)					,     )
																else:
																			self.play(
																			    MoveToTarget(model_cpu_arr[i]					,     run_time=0.7			)					,     MoveToTarget(model_cpu_arr[i + 1]					,     run_time=0.7			)					,     )
													else:
																model_cpu_arr[i].generate_target()
																model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]			)
																input.generate_target()
																input.target.next_to(model_arr[-1].get_right()					,     RIGHT + 0.0_2					,     buff=0.2			)

																self.play(
																    Circumscribe(model_arr[-1]					,     color=__a					,     **__a			)					,     Circumscribe(cpu_left_col_base[-1]					,     color=__a					,     **__a			)					,     Circumscribe(gpu_rect[0]					,     color=__a					,     **__a			)					,     )

																self.play(MoveToTarget(model_cpu_arr[i]			)			)

													__lowerCAmelCase       					=    a_c
													__lowerCAmelCase       					=    a_c.copy()

										input.generate_target()
										input.target.next_to(model_base[-1]					,     RIGHT + 0.0_2					,     buff=0.5			)
										self.play(
										    FadeOut(__a			)					,     FadeOut(__a					,     run_time=0.5			)					,     )

										__lowerCAmelCase       					=    MarkupText(f"Inference on a model too large for GPU memory\nis successfully completed."					,     font_size=24			)
										step_a.move_to([2, 2, 0]			)

										self.play(Write(__a					,     run_time=3			)					,     MoveToTarget(__a			)			)

										self.wait()




