

"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							__lowerCAmelCase				      =   '''platform'''

							import jax
							import jax.numpy as jnp

							from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def 		A_	(   __UpperCamelCase     :				Tuple       ,	__UpperCamelCase     :				Optional[Any]       ,	__UpperCamelCase     :				Union[str, Any]=None       ,	__UpperCamelCase     :				List[str]=None				):
		if attention_mask is None:
				lowercase										=							np.where(input_ids != config.pad_token_id       ,	1       ,	0				)
		return {
		    "input_ids": input_ids,
		    "attention_mask": attention_mask,
		}
@require_flax
class 	_lowerCAmelCase    :

							def __init__( self       :    Any ,		a       :    int ,		a       :    Union[str, Any]=13 ,		a       :    Optional[Any]=7 ,		a       :    Union[str, Any]=True ,		a       :    List[Any]=False ,		a       :    Optional[Any]=99 ,		a       :    Dict=16 ,		a       :    Optional[Any]=2 ,		a       :    Any=4 ,		a       :    Any=4 ,		a       :    str="gelu" ,		a       :    int=0.1 ,		a       :    Tuple=0.1 ,		a       :    Tuple=20 ,		a       :    Dict=2 ,		a       :    List[str]=1 ,		a       :    Union[str, Any]=0 ,		a       :    List[Any]=16 ,		a       :    Optional[Any]=16 ,		a       :    Optional[int]=0.02 ,		)   ->    str:

									"""simple docstring"""
									lowercase										=							parent
									lowercase										=							batch_size
									lowercase										=							seq_length
									lowercase										=							is_training
									lowercase										=							use_labels
									lowercase										=							vocab_size
									lowercase										=							hidden_size
									lowercase										=							num_hidden_layers
									lowercase										=							num_attention_heads
									lowercase										=							intermediate_size
									lowercase										=							hidden_act
									lowercase										=							hidden_dropout_prob
									lowercase										=							attention_probs_dropout_prob
									lowercase										=							max_position_embeddings
									lowercase										=							eos_token_id
									lowercase										=							pad_token_id
									lowercase										=							bos_token_id
									lowercase										=							embed_dim
									lowercase										=							word_embed_proj_dim
									lowercase										=							initializer_range
									lowercase										=							False

							def 					_lowerCAmelCase  ( self       :    Tuple    )   ->    Dict:

									"""simple docstring"""
									lowercase										=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,		self.vocab_size    ) ,		3 ,		self.vocab_size    )
									lowercase										=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,		dtype=np.intaa    )) ,		-1    )

									lowercase										=							OPTConfig(
									    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 ,		is_encoder_decoder=a ,		word_embed_proj_dim=self.word_embed_proj_dim ,		initializer_range=self.initializer_range ,		use_cache=a ,		)
									lowercase										=							prepare_opt_inputs_dict(a ,		a    )
									return config, inputs_dict

							def 					_lowerCAmelCase  ( self       :    List[Any]    )   ->    int:

									"""simple docstring"""
									lowercase					,  lowercase										=							self.prepare_config_and_inputs()
									return config, inputs_dict

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		a       :    List[Any] ,		a       :    List[str] ,		a       :    Tuple    )   ->    Any:

									"""simple docstring"""
									lowercase										=							20
									lowercase										=							model_class_name(a    )

									lowercase										=							inputs_dict['''input_ids''']
									lowercase										=							inputs_dict['''attention_mask''']

									lowercase										=							model.init_cache(input_ids.shape[0] ,		a    )
									lowercase										=							jnp.ones((input_ids.shape[0], max_length) ,		dtype='''i4'''    )

									lowercase										=							jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1    )[None, :] ,		(input_ids.shape[0], input_ids.shape[-1] - 1) ,		)
									lowercase										=							model(
									    input_ids[:, :-1] ,		attention_mask=a ,		past_key_values=a ,		position_ids=a ,		)

									lowercase										=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,		dtype='''i4'''    )
									lowercase										=							model(
									    input_ids[:, -1:] ,		attention_mask=a ,		past_key_values=outputs_cache.past_key_values ,		position_ids=a ,		)

									lowercase										=							model(a    )

									lowercase										=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
									self.parent.assertTrue(diff < 1E-3 ,		msg=f"""Max diff is {diff}"""    )

							def 					_lowerCAmelCase  ( self       :    Dict ,		a       :    Tuple ,		a       :    Optional[int] ,		a       :    Dict    )   ->    int:

									"""simple docstring"""
									lowercase										=							20
									lowercase										=							model_class_name(a    )

									lowercase					,  lowercase										=							(
									    inputs_dict['''input_ids'''],
									    inputs_dict['''attention_mask'''],
									)

									lowercase										=							jnp.concatenate(
									    [
									        attention_mask,
									        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])    ),
									    ] ,		axis=-1 ,		)

									lowercase										=							model.init_cache(input_ids.shape[0] ,		a    )
									lowercase										=							jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1    )[None, :] ,		(input_ids.shape[0], input_ids.shape[-1] - 1) ,		)

									lowercase										=							model(
									    input_ids[:, :-1] ,		attention_mask=a ,		past_key_values=a ,		position_ids=a ,		)
									lowercase										=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,		dtype='''i4'''    )
									lowercase										=							model(
									    input_ids[:, -1:] ,		past_key_values=outputs_cache.past_key_values ,		attention_mask=a ,		position_ids=a ,		)

									lowercase										=							model(a ,		attention_mask=a    )

									lowercase										=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
									self.parent.assertTrue(diff < 1E-3 ,		msg=f"""Max diff is {diff}"""    )




@require_flax
class 	_lowerCAmelCase    (      __snake_case       ,       unittest.TestCase       ,       __snake_case       ):
							__lowerCAmelCase					:    int     =  (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
							__lowerCAmelCase					:    Optional[Any]     =  () if is_flax_available() else ()

							def 					_lowerCAmelCase  ( self       :    Union[str, Any]    )   ->    Tuple:

									"""simple docstring"""
									lowercase										=							FlaxOPTModelTester(self    )

							def 					_lowerCAmelCase  ( self       :    Any    )   ->    List[str]:

									"""simple docstring"""
									lowercase					,  lowercase										=							self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(a ,		a ,		a    )

							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    Tuple:

									"""simple docstring"""
									lowercase					,  lowercase										=							self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward_with_attn_mask(a ,		a ,		a    )

							@slow
							def 					_lowerCAmelCase  ( self       :    List[Any]    )   ->    Optional[int]:

									"""simple docstring"""
									for model_class_name in self.all_model_classes:
											lowercase										=							model_class_name.from_pretrained('''facebook/opt-125m'''    )
											lowercase										=							np.ones((1, 1)    ) * model.config.eos_token_id
											lowercase										=							model(a    )
											self.assertIsNotNone(a    )




@require_sentencepiece
@require_flax
class 	_lowerCAmelCase    (      unittest.TestCase       ):

							@slow
							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    int:

									"""simple docstring"""
									lowercase										=							FlaxOPTModel.from_pretrained('''facebook/opt-350m'''    )
									lowercase										=							jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]    )
									lowercase										=							model(input_ids=a    ).last_hidden_state
									lowercase										=							(1, 11, 512)
									self.assertEqual(output.shape ,		a    )
									lowercase										=							jnp.array(
									    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]    )
									self.assertTrue(jnp.allclose(output[:, :3, :3] ,		a ,		atol=4E-2    )    )




@require_flax
@slow
class 	_lowerCAmelCase    (      unittest.TestCase       ):

							def 					_lowerCAmelCase  ( self       :    Tuple    )   ->    Any:

									"""simple docstring"""
									super().setUp()
									lowercase										=							'''facebook/opt-350m'''

							def 					_lowerCAmelCase  ( self       :    List[str]    )   ->    int:

									"""simple docstring"""
									lowercase										=							FlaxOPTForCausalLM.from_pretrained(self.path_model    )
									lowercase										=							GPTaTokenizer.from_pretrained(self.path_model    )

									lowercase										=							[
									    '''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
									lowercase										=							tokenizer(a ,		return_tensors='''jax''' ,		padding=a ,		add_special_tokens=a    )
									lowercase										=							model(inputs.input_ids ,		attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
									lowercase										=							jnp.array(
									    [
									        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
									        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
									        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
									        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
									    ]    )
									self.assertTrue(jnp.allclose(a ,		a ,		atol=4E-2    )    )

									lowercase										=							jax.jit(a    )
									lowercase										=							model(inputs.input_ids ,		attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
									self.assertTrue(jnp.allclose(a ,		a ,		atol=4E-2    )    )




@require_flax
@slow
class 	_lowerCAmelCase    (      unittest.TestCase       ):

							@property
							def 					_lowerCAmelCase  ( self       :    Optional[int]    )   ->    Dict:

									"""simple docstring"""
									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 					_lowerCAmelCase  ( self       :    Tuple    )   ->    Union[str, Any]:

									"""simple docstring"""
									lowercase										=							'''facebook/opt-125m'''

									lowercase										=							[
									    '''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''',
									]

									lowercase										=							[]

									lowercase										=							FlaxOPTForCausalLM.from_pretrained(a    )
									lowercase										=							GPTaTokenizer.from_pretrained(a    )

									for prompt in self.prompts:
											lowercase										=							tokenizer(a ,		return_tensors='''jax'''    ).input_ids

											lowercase										=							model.generate(a ,		max_length=10    )
											lowercase										=							generated_ids[0]

											lowercase										=							tokenizer.batch_decode(a ,		skip_special_tokens=a    )
											predicted_outputs += generated_string

									self.assertListEqual(a ,		a    )

							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    Dict:

									"""simple docstring"""
									lowercase										=							'''facebook/opt-350m'''

									lowercase										=							[
									    '''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''',
									]

									lowercase										=							[]
									lowercase										=							FlaxOPTForCausalLM.from_pretrained(a    )
									lowercase										=							GPTaTokenizer.from_pretrained(a    )

									for prompt in self.prompts:
											lowercase										=							tokenizer(a ,		return_tensors='''jax'''    ).input_ids

											lowercase										=							model.generate(a ,		max_length=10    )
											lowercase										=							generated_ids[0]

											lowercase										=							tokenizer.batch_decode(a ,		skip_special_tokens=a    )
											predicted_outputs += generated_string

									self.assertListEqual(a ,		a    )

							def 					_lowerCAmelCase  ( self       :    int    )   ->    Optional[int]:

									"""simple docstring"""
									lowercase										=							'''facebook/opt-125m'''
									lowercase										=							[
									    '''Today is a beautiful day and I want to thank''',
									    '''In the city of Rome Canaver Canaver Canaver Canaver''',
									]
									lowercase										=							FlaxOPTForCausalLM.from_pretrained(a    )
									lowercase										=							GPTaTokenizer.from_pretrained(a    )
									lowercase										=							tokenizer(
									    [
									        '''Today is a beautiful day and I want to''',
									        '''In the city of''',
									    ] ,		return_tensors='''jax''' ,		padding=a ,		)

									lowercase										=							jax.jit(model.generate    )

									lowercase										=							jit_generate(inputs['''input_ids'''] ,		attention_mask=inputs['''attention_mask''']    ).sequences

									lowercase										=							tokenizer.batch_decode(a ,		skip_special_tokens=a    )

									self.assertIsNotNone(a ,		a    )

							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    Optional[int]:

									"""simple docstring"""
									lowercase										=							'''facebook/opt-350m'''

									lowercase										=							GPTaTokenizer.from_pretrained(a    )
									lowercase										=							FlaxOPTForCausalLM.from_pretrained(a    )

									lowercase										=							'''left'''

									# use different length sentences to test batching
									lowercase										=							[
									    '''Hello, my dog is a little''',
									    '''Today, I''',
									]

									lowercase										=							tokenizer(a ,		return_tensors='''jax''' ,		padding=a    )
									lowercase										=							inputs['''input_ids''']

									lowercase										=							model.generate(input_ids=a ,		attention_mask=inputs['''attention_mask'''] ,		trace=a    )

									lowercase										=							tokenizer(sentences[0] ,		return_tensors='''jax'''    ).input_ids
									lowercase										=							model.generate(input_ids=a    )

									lowercase										=							inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
									lowercase										=							tokenizer(sentences[1] ,		return_tensors='''jax'''    ).input_ids
									lowercase										=							model.generate(input_ids=a ,		max_length=model.config.max_length - num_paddings    )

									lowercase										=							tokenizer.batch_decode(outputs[0] ,		skip_special_tokens=a    )
									lowercase										=							tokenizer.decode(output_non_padded[0][0] ,		skip_special_tokens=a    )
									lowercase										=							tokenizer.decode(output_padded[0][0] ,		skip_special_tokens=a    )

									lowercase										=							[
									    '''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]    )


"""simple docstring"""






from manim import *
class 	_lowerCAmelCase    (      __snake_case       ):

							def 					_lowerCAmelCase  ( self       :    Dict    )   ->    Tuple:

									"""simple docstring"""
									lowercase										=							Rectangle(height=0.5 ,		width=0.5    )
									lowercase										=							Rectangle(height=0.46 ,		width=0.46    ).set_stroke(width=0    )
									lowercase										=							Rectangle(height=0.25 ,		width=0.25    )

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

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

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

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

									lowercase										=							[]
									lowercase										=							[]

									for i, rect in enumerate(a    ):
											rect.set_stroke(a    )

											lowercase										=							Rectangle(height=0.46 / 4 ,		width=0.46 / 3    ).set_stroke(width=0.0    ).set_fill(a ,		opacity=0.7    )

											if i == 0:
													cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT    ) ,		buff=0.02 ,		direction=a    )
													cpu_target.set_x(cpu_target.get_x() + 0.1    )
											elif i == 3:
													cpu_target.next_to(model_cpu_arr[0] ,		direction=a ,		buff=0.0    )
											else:
													cpu_target.next_to(model_cpu_arr[i - 1] ,		direction=a ,		buff=0.0    )
											self.add(a    )
											model_cpu_arr.append(a    )

									self.add(*a ,		*a    )

									lowercase										=							[meta_mem.copy() for i in range(6    )]
									lowercase										=							[meta_mem.copy() for i in range(6    )]
									lowercase										=							VGroup(*a    ).arrange(a ,		buff=0    )
									lowercase										=							VGroup(*a    ).arrange(a ,		buff=0    )
									lowercase										=							VGroup(a ,		a    ).arrange(a ,		buff=0    )
									lowercase										=							Text('''Disk''' ,		font_size=24    )
									lowercase										=							Group(a ,		a    ).arrange(a ,		buff=0.5 ,		aligned_edge=a    )
									disk.move_to([-4.0, -1.25, 0]    )
									self.add(a ,		a    )

									lowercase										=							[]

									for i in range(6    ):
											lowercase										=							fill.copy().set_fill(a ,		opacity=0.8    )
											target.move_to(disk_left_col_base[i]    ).scale(0.5    )
											cpu_disk_arr.append(a    )

									self.add(*a    )

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

									lowercase										=							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    )

									lowercase										=							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    )

									lowercase										=							MarkupText(f"""The offloaded weights are all sent to the CPU.""" ,		font_size=24    )
									step_a.move_to([2, 2, 0]    )

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

									for i in range(6    ):
											lowercase										=							cpu_disk_arr[i]
											lowercase										=							rect.copy().set_fill(a ,		opacity=0.8    ).scale(2.0    )
											cpa.generate_target()
											cpa.target.move_to(model_base[i]    )

											if i == 0:
													rect.set_fill(a ,		opacity=0.8    )
													rect.generate_target()
													rect.target.move_to(cpu_left_col_base[0]    ).scale(2.0    )

													self.remove(
													    *a ,		*a ,		)

											else:
													rect.generate_target()
													rect.target.move_to(cpu_left_col_base[i]    ).scale(2.0    )
											self.play(MoveToTarget(a    ) ,		MoveToTarget(a    ) ,		model_base[i].animate.set_stroke(a    )    )
									self.play(FadeOut(a    )    )

									lowercase										=							MarkupText(
									    f"""Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed.""" ,		font_size=24 ,		)
									step_a.move_to([2, 2, 0]    )

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

									lowercase										=							[]
									lowercase										=							[]
									for i in range(6    ):
											lowercase										=							Arrow(start=a ,		end=a ,		color=a ,		buff=0.5    )
											a.next_to(model_base[i].get_left() ,		a ,		buff=0.2    )
											arrows.append(a    )
											animations.append(Write(a    )    )
									self.play(*a    )
									self.wait()