
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser



def      _a     (   __UpperCamelCase       :  List[Any]=None	,__UpperCamelCase       :  List[Any]=None    ):
			return field(default_factory=lambda: default	,metadata=lowerCAmelCase__    )





@dataclass
class 						lowercase       :
					__a					=    field(
					    metadata={"""help""": """The csv file to plot."""}	,		)
					__a					=    field(
					    default=snake_case__	,		metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}	,		)
					__a					=    field(
					    default=snake_case__	,		metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}	,		)
					__a					=    field(
					    default=snake_case__	,		metadata={"""help""": """Disable logarithmic scale when plotting"""}	,		)
					__a					=    field(
					    default=snake_case__	,		metadata={
					        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
					    }	,		)
					__a					=    field(
					    default=snake_case__	,		metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}	,		)
					__a					=    list_field(
					    default=snake_case__	,		metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}					)





def      _a     (   __UpperCamelCase       :  Any    ):
			try:
						int(lowerCAmelCase__    )
						return True
			except ValueError:
						return False



def      _a     (   __UpperCamelCase       :  Union[str, Any]    ):
			try:
						float(lowerCAmelCase__    )
						return True
			except ValueError:
						return False





class 						lowercase       :
					def __init__(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Dict				      =			args
								lowerCAmelCase__ :    List[Any]				      =			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

								with open(self.args.csv_file ,	newline=''''''    ) as csv_file:
											lowerCAmelCase__ :    Optional[int]				      =			csv.DictReader(SCREAMING_SNAKE_CASE__    )
											for row in reader:
														lowerCAmelCase__ :    List[str]				      =			row['model']
														self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']    )    )
														self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']    )    )
														if can_convert_to_int(row['''result''']    ):
																	# value is not None
																	lowerCAmelCase__ :    List[str]				      =			int(row['''result''']    )
														elif can_convert_to_float(row['''result''']    ):
																	# value is not None
																	lowerCAmelCase__ :    Any				      =			float(row['''result''']    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			plt.subplots()
								lowerCAmelCase__ :    Any				      =			'Time usage' if self.args.is_time else 'Memory usage'
								lowerCAmelCase__ :    Any				      =			title_str + ' for training' if self.args.is_train else title_str + ' for inference'

								if not self.args.no_log_scale:
											# set logarithm scales
											ax.set_xscale('''log'''    )
											ax.set_yscale('''log'''    )

								for axis in [ax.xaxis, ax.yaxis]:
											axis.set_major_formatter(ScalarFormatter()    )

								for model_name_idx, model_name in enumerate(self.result_dict.keys()    ):
											lowerCAmelCase__ :    Union[str, Any]				      =			sorted(set(self.result_dict[model_name]['''bsz''']    )    )
											lowerCAmelCase__ :    Optional[Any]				      =			sorted(set(self.result_dict[model_name]['''seq_len''']    )    )
											lowerCAmelCase__ :    Optional[Any]				      =			self.result_dict[model_name]['result']

											(lowerCAmelCase__) :    Dict				      =			(
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											lowerCAmelCase__ :    Union[str, Any]				      =			(
											    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
											)

											for inner_loop_value in inner_loop_array:
														if self.args.plot_along_batch:
																	lowerCAmelCase__ :    Tuple				      =			np.asarray(
																	    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,	dtype=SCREAMING_SNAKE_CASE__ ,	)
														else:
																	lowerCAmelCase__ :    List[Any]				      =			np.asarray(
																	    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,	dtype=np.floataa ,	)

														(lowerCAmelCase__) :    Optional[Any]				      =			(
														    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
														)

														lowerCAmelCase__ :    List[Any]				      =			np.asarray(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )[: len(SCREAMING_SNAKE_CASE__    )]
														plt.scatter(
														    SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''    )
														plt.plot(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	'''--'''    )

											title_str += f''' {label_model_name} vs.'''

								lowerCAmelCase__ :    Optional[int]				      =			title_str[:-4]
								lowerCAmelCase__ :    List[str]				      =			'Time in s' if self.args.is_time else 'Memory in MB'

								# plot
								plt.title(SCREAMING_SNAKE_CASE__    )
								plt.xlabel(SCREAMING_SNAKE_CASE__    )
								plt.ylabel(SCREAMING_SNAKE_CASE__    )
								plt.legend()

								if self.args.figure_png_file is not None:
											plt.savefig(self.args.figure_png_file    )
								else:
											plt.show()





def      _a     (   ):
			lowerCAmelCase__ :    str				      =			HfArgumentParser(lowerCAmelCase__    )
			lowerCAmelCase__ :    List[str]				      =			parser.parse_args_into_dataclasses()[0]
			lowerCAmelCase__ :    List[Any]				      =			Plot(args=lowerCAmelCase__    )
			plot.plot()


if __name__ == "__main__":
							main()





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
							A__       :   Tuple   						=					"""platform"""

							import jax
							import jax.numpy as jnp

							from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def      _a     (   __UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  Union[str, Any]=None	,__UpperCamelCase       :  Optional[int]=None    ):
			if attention_mask is None:
						lowerCAmelCase__ :    Any				      =			np.where(input_ids != config.pad_token_id	,1	,0    )
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			}





@require_flax
class 						lowercase       :
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=13 ,	SCREAMING_SNAKE_CASE__=7 ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=99 ,	SCREAMING_SNAKE_CASE__=16 ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=4 ,	SCREAMING_SNAKE_CASE__=4 ,	SCREAMING_SNAKE_CASE__="gelu" ,	SCREAMING_SNAKE_CASE__=0.1 ,	SCREAMING_SNAKE_CASE__=0.1 ,	SCREAMING_SNAKE_CASE__=20 ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=1 ,	SCREAMING_SNAKE_CASE__=0 ,	SCREAMING_SNAKE_CASE__=16 ,	SCREAMING_SNAKE_CASE__=16 ,	SCREAMING_SNAKE_CASE__=0.02 ,	):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			parent
								lowerCAmelCase__ :    Optional[Any]				      =			batch_size
								lowerCAmelCase__ :    Any				      =			seq_length
								lowerCAmelCase__ :    str				      =			is_training
								lowerCAmelCase__ :    List[Any]				      =			use_labels
								lowerCAmelCase__ :    List[Any]				      =			vocab_size
								lowerCAmelCase__ :    List[str]				      =			hidden_size
								lowerCAmelCase__ :    str				      =			num_hidden_layers
								lowerCAmelCase__ :    List[Any]				      =			num_attention_heads
								lowerCAmelCase__ :    Optional[Any]				      =			intermediate_size
								lowerCAmelCase__ :    int				      =			hidden_act
								lowerCAmelCase__ :    Optional[int]				      =			hidden_dropout_prob
								lowerCAmelCase__ :    Any				      =			attention_probs_dropout_prob
								lowerCAmelCase__ :    str				      =			max_position_embeddings
								lowerCAmelCase__ :    List[str]				      =			eos_token_id
								lowerCAmelCase__ :    List[Any]				      =			pad_token_id
								lowerCAmelCase__ :    Tuple				      =			bos_token_id
								lowerCAmelCase__ :    str				      =			embed_dim
								lowerCAmelCase__ :    Optional[int]				      =			word_embed_proj_dim
								lowerCAmelCase__ :    List[Any]				      =			initializer_range
								lowerCAmelCase__ :    Tuple				      =			False
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,	self.vocab_size    ) ,	3 ,	self.vocab_size    )
								lowerCAmelCase__ :    Union[str, Any]				      =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,	dtype=np.intaa    )) ,	-1    )

								lowerCAmelCase__ :    Union[str, Any]				      =			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=SCREAMING_SNAKE_CASE__ ,	word_embed_proj_dim=self.word_embed_proj_dim ,	initializer_range=self.initializer_range ,	use_cache=SCREAMING_SNAKE_CASE__ ,	)
								lowerCAmelCase__ :    Optional[int]				      =			prepare_opt_inputs_dict(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
								return config, inputs_dict
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    List[str]				      =			self.prepare_config_and_inputs()
								return config, inputs_dict
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			20
								lowerCAmelCase__ :    Optional[int]				      =			model_class_name(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Optional[Any]				      =			inputs_dict['''input_ids''']
								lowerCAmelCase__ :    Optional[Any]				      =			inputs_dict['''attention_mask''']

								lowerCAmelCase__ :    Tuple				      =			model.init_cache(input_ids.shape[0] ,	SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Tuple				      =			jnp.ones((input_ids.shape[0], max_length) ,	dtype='''i4'''    )

								lowerCAmelCase__ :    Any				      =			jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1    )[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1) ,	)
								lowerCAmelCase__ :    str				      =			model(
								    input_ids[:, :-1] ,	attention_mask=SCREAMING_SNAKE_CASE__ ,	past_key_values=SCREAMING_SNAKE_CASE__ ,	position_ids=SCREAMING_SNAKE_CASE__ ,	)

								lowerCAmelCase__ :    int				      =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='''i4'''    )
								lowerCAmelCase__ :    List[str]				      =			model(
								    input_ids[:, -1:] ,	attention_mask=SCREAMING_SNAKE_CASE__ ,	past_key_values=outputs_cache.past_key_values ,	position_ids=SCREAMING_SNAKE_CASE__ ,	)

								lowerCAmelCase__ :    List[str]				      =			model(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    List[Any]				      =			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 lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    int				      =			20
								lowerCAmelCase__ :    Optional[Any]				      =			model_class_name(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__      ,				lowerCAmelCase__ :    Optional[Any]				      =			(
								    inputs_dict['''input_ids'''],
								    inputs_dict['''attention_mask'''],
								)

								lowerCAmelCase__ :    Optional[Any]				      =			jnp.concatenate(
								    [
								        attention_mask,
								        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])    ),
								    ] ,	axis=-1 ,	)

								lowerCAmelCase__ :    Any				      =			model.init_cache(input_ids.shape[0] ,	SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Union[str, Any]				      =			jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1    )[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1) ,	)

								lowerCAmelCase__ :    Tuple				      =			model(
								    input_ids[:, :-1] ,	attention_mask=SCREAMING_SNAKE_CASE__ ,	past_key_values=SCREAMING_SNAKE_CASE__ ,	position_ids=SCREAMING_SNAKE_CASE__ ,	)
								lowerCAmelCase__ :    int				      =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='''i4'''    )
								lowerCAmelCase__ :    List[Any]				      =			model(
								    input_ids[:, -1:] ,	past_key_values=outputs_cache.past_key_values ,	attention_mask=SCREAMING_SNAKE_CASE__ ,	position_ids=SCREAMING_SNAKE_CASE__ ,	)

								lowerCAmelCase__ :    Any				      =			model(SCREAMING_SNAKE_CASE__ ,	attention_mask=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    int				      =			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 						lowercase       (  __UpperCamelCase	,		unittest.TestCase	,		__UpperCamelCase					):
					__a					=    (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
					__a					=    () if is_flax_available() else ()
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Union[str, Any]				      =			FlaxOPTModelTester(self    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    List[Any]				      =			self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			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(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					@slow
					def lowercase_			(		self    ):
								"""simple docstring"""







								for model_class_name in self.all_model_classes:
											lowerCAmelCase__ :    Tuple				      =			model_class_name.from_pretrained('''facebook/opt-125m'''    )
											lowerCAmelCase__ :    Dict				      =			np.ones((1, 1)    ) * model.config.eos_token_id
											lowerCAmelCase__ :    List[str]				      =			model(SCREAMING_SNAKE_CASE__    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE__    )





@require_sentencepiece
@require_flax
class 						lowercase       (  unittest.TestCase					):
					@slow
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			FlaxOPTModel.from_pretrained('''facebook/opt-350m'''    )
								lowerCAmelCase__ :    List[Any]				      =			jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]    )
								lowerCAmelCase__ :    Union[str, Any]				      =			model(input_ids=SCREAMING_SNAKE_CASE__    ).last_hidden_state
								lowerCAmelCase__ :    Any				      =			(1, 11, 512)
								self.assertEqual(output.shape ,	SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    int				      =			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] ,	SCREAMING_SNAKE_CASE__ ,	atol=4E-2    )    )





@require_flax
@slow
class 						lowercase       (  unittest.TestCase					):
					def lowercase_			(		self    ):
								"""simple docstring"""







								super().setUp()
								lowerCAmelCase__ :    Union[str, Any]				      =			'''facebook/opt-350m'''
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			FlaxOPTForCausalLM.from_pretrained(self.path_model    )
								lowerCAmelCase__ :    Any				      =			GPTaTokenizer.from_pretrained(self.path_model    )

								lowerCAmelCase__ :    Tuple				      =			[
								    '''Today is a beautiful day and I want to''',
								    '''In the city of''',
								    '''Paris is the capital of France and''',
								    '''Computers and mobile phones have taken''',
								]
								# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
								lowerCAmelCase__ :    Dict				      =			tokenizer(SCREAMING_SNAKE_CASE__ ,	return_tensors='''jax''' ,	padding=SCREAMING_SNAKE_CASE__ ,	add_special_tokens=SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Optional[Any]				      =			model(inputs.input_ids ,	attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
								lowerCAmelCase__ :    Optional[Any]				      =			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(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	atol=4E-2    )    )

								lowerCAmelCase__ :    List[Any]				      =			jax.jit(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    List[Any]				      =			model(inputs.input_ids ,	attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
								self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	atol=4E-2    )    )





@require_flax
@slow
class 						lowercase       (  unittest.TestCase					):
					@property
					def lowercase_			(		self    ):
								"""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 lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Dict				      =			'''facebook/opt-125m'''

								lowerCAmelCase__ :    List[Any]				      =			[
								    '''Today is a beautiful day and I want to''',
								    '''In the city of New York, the city''',
								    '''Paris is the capital of France and the capital''',
								    '''Computers and mobile phones have taken over the''',
								]

								lowerCAmelCase__ :    Any				      =			[]

								lowerCAmelCase__ :    Optional[int]				      =			FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    List[Any]				      =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )

								for prompt in self.prompts:
											lowerCAmelCase__ :    Union[str, Any]				      =			tokenizer(SCREAMING_SNAKE_CASE__ ,	return_tensors='''jax'''    ).input_ids

											lowerCAmelCase__ :    Dict				      =			model.generate(SCREAMING_SNAKE_CASE__ ,	max_length=10    )
											lowerCAmelCase__ :    Tuple				      =			generated_ids[0]

											lowerCAmelCase__ :    List[str]				      =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )
											predicted_outputs += generated_string

								self.assertListEqual(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			'''facebook/opt-350m'''

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

								lowerCAmelCase__ :    Any				      =			[]
								lowerCAmelCase__ :    List[str]				      =			FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    int				      =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )

								for prompt in self.prompts:
											lowerCAmelCase__ :    str				      =			tokenizer(SCREAMING_SNAKE_CASE__ ,	return_tensors='''jax'''    ).input_ids

											lowerCAmelCase__ :    Optional[Any]				      =			model.generate(SCREAMING_SNAKE_CASE__ ,	max_length=10    )
											lowerCAmelCase__ :    List[Any]				      =			generated_ids[0]

											lowerCAmelCase__ :    Optional[Any]				      =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )
											predicted_outputs += generated_string

								self.assertListEqual(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			'''facebook/opt-125m'''
								lowerCAmelCase__ :    Dict				      =			[
								    '''Today is a beautiful day and I want to thank''',
								    '''In the city of Rome Canaver Canaver Canaver Canaver''',
								]
								lowerCAmelCase__ :    Tuple				      =			FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Optional[Any]				      =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    int				      =			tokenizer(
								    [
								        '''Today is a beautiful day and I want to''',
								        '''In the city of''',
								    ] ,	return_tensors='''jax''' ,	padding=SCREAMING_SNAKE_CASE__ ,	)

								lowerCAmelCase__ :    Union[str, Any]				      =			jax.jit(model.generate    )

								lowerCAmelCase__ :    Optional[Any]				      =			jit_generate(inputs['''input_ids'''] ,	attention_mask=inputs['''attention_mask''']    ).sequences

								lowerCAmelCase__ :    str				      =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )

								self.assertIsNotNone(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			'''facebook/opt-350m'''

								lowerCAmelCase__ :    Tuple				      =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    int				      =			FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Union[str, Any]				      =			'''left'''

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

								lowerCAmelCase__ :    Optional[int]				      =			tokenizer(SCREAMING_SNAKE_CASE__ ,	return_tensors='''jax''' ,	padding=SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Any				      =			inputs['''input_ids''']

								lowerCAmelCase__ :    Optional[Any]				      =			model.generate(input_ids=SCREAMING_SNAKE_CASE__ ,	attention_mask=inputs['''attention_mask'''] ,	trace=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    List[Any]				      =			tokenizer(sentences[0] ,	return_tensors='''jax'''    ).input_ids
								lowerCAmelCase__ :    List[Any]				      =			model.generate(input_ids=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Dict				      =			inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
								lowerCAmelCase__ :    Union[str, Any]				      =			tokenizer(sentences[1] ,	return_tensors='''jax'''    ).input_ids
								lowerCAmelCase__ :    Dict				      =			model.generate(input_ids=SCREAMING_SNAKE_CASE__ ,	max_length=model.config.max_length - num_paddings    )

								lowerCAmelCase__ :    Dict				      =			tokenizer.batch_decode(outputs[0] ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Dict				      =			tokenizer.decode(output_non_padded[0][0] ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Tuple				      =			tokenizer.decode(output_padded[0][0] ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    int				      =			[
								    '''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(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
								self.assertListEqual(SCREAMING_SNAKE_CASE__ ,	[non_padded_sentence, padded_sentence]    )



