

'''simple docstring'''

from argparse import ArgumentParser

from . import BaseTransformersCLICommand




def 							_UpperCamelCase				(   lowerCAmelCase__:					Union[str, Any]   )      -> str:
							return DownloadCommand(args.model		,args.cache_dir		,args.force		,args.trust_remote_code   )






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@staticmethod
				def   a__					(    _lowercase       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				parser.add_parser('download'       )
											download_parser.add_argument(
											    '--cache-dir',     type=_lowercase,     default=_lowercase,     help='Path to location to store the models'       )
											download_parser.add_argument(
											    '--force',     action='store_true',     help='Force the model to be download even if already in cache-dir'       )
											download_parser.add_argument(
											    '--trust-remote-code',     action='store_true',     help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine',     )
											download_parser.add_argument('model',     type=_lowercase,     help='Name of the model to download'       )
											download_parser.set_defaults(func=_lowercase       )



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				model
											SCREAMING_SNAKE_CASE_				=				cache
											SCREAMING_SNAKE_CASE_				=				force
											SCREAMING_SNAKE_CASE_				=				trust_remote_code





				def   a__					(    self       )     ->							Optional[int]:
											from ..models.auto import AutoModel, AutoTokenizer

											AutoModel.from_pretrained(
											    self._model,     cache_dir=self._cache,     force_download=self._force,     trust_remote_code=self._trust_remote_code       )
											AutoTokenizer.from_pretrained(
											    self._model,     cache_dir=self._cache,     force_download=self._force,     trust_remote_code=self._trust_remote_code       )








'''simple docstring'''

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
							import os

							import jax.numpy as jnp
							from jax import jit

							from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
							from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

							SCREAMING_SNAKE_CASE							:				List[Any]     	=			"0.12"  # assumed parallelism: 8


if is_torch_available():
							import torch




def 							_UpperCamelCase				(   lowerCAmelCase__:					Dict		,lowerCAmelCase__:					int		,lowerCAmelCase__:					Tuple=None   )      -> Union[str, Any]:
							if rng is None:
														SCREAMING_SNAKE_CASE_				=				random.Random()

							SCREAMING_SNAKE_CASE_				=				1
							for dim in shape:
														total_dims *= dim

							SCREAMING_SNAKE_CASE_				=				[]
							for _ in range(lowerCAmelCase__   ):
														values.append(rng.randint(0		,vocab_size - 1   )   )

							SCREAMING_SNAKE_CASE_				=				np.array(lowerCAmelCase__		,dtype=jnp.intaa   ).reshape(lowerCAmelCase__   )

							return output




def 							_UpperCamelCase				(   lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					str=None   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				ids_tensor(lowerCAmelCase__		,vocab_size=2		,rng=lowerCAmelCase__   )
							# make sure that at least one token is attended to for each batch
							SCREAMING_SNAKE_CASE_				=				1
							return attn_mask






@require_flax
class    snake_case     :






				"""simple docstring"""
				_a    	=     None
				_a    	=     ()



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs_for_common()

											# cut to half length & take max batch_size 3
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				inputs['input_ids'].shape[-1] // 2
											SCREAMING_SNAKE_CASE_				=				inputs['input_ids'][:max_batch_size, :sequence_length]

											SCREAMING_SNAKE_CASE_				=				jnp.ones_like(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				attention_mask[:max_batch_size, :sequence_length]

											# generate max 5 tokens
											SCREAMING_SNAKE_CASE_				=				input_ids.shape[-1] + 5
											if config.eos_token_id is not None and config.pad_token_id is None:
																		# hack to allow generate for models such as GPT2 as is done in `generate()`
																		SCREAMING_SNAKE_CASE_				=				config.eos_token_id
											return config, input_ids, attention_mask, max_length



				@is_pt_flax_cross_test
				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				0

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		SCREAMING_SNAKE_CASE_				=				getattr(_lowercase,     _lowercase       )
																		SCREAMING_SNAKE_CASE_				=				pt_model_class(_lowercase       ).eval()
																		SCREAMING_SNAKE_CASE_				=				load_flax_weights_in_pytorch_model(_lowercase,     flax_model.params       )

																		SCREAMING_SNAKE_CASE_				=				flax_model.generate(_lowercase       ).sequences
																		SCREAMING_SNAKE_CASE_				=				pt_model.generate(torch.tensor(_lowercase,     dtype=torch.long       )       )

																		if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																									SCREAMING_SNAKE_CASE_				=				flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																		self.assertListEqual(pt_generation_outputs.numpy().tolist(),     flax_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				2

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[0],     input_ids.shape[0] * config.num_return_sequences       )



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				0.8
											SCREAMING_SNAKE_CASE_				=				10
											SCREAMING_SNAKE_CASE_				=				0.3
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

											# pad attention mask on the left
											SCREAMING_SNAKE_CASE_				=				attention_mask.at[(0, 0)].set(0       )

											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase,     attention_mask=_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

											# pad attention mask on the left
											SCREAMING_SNAKE_CASE_				=				attention_mask.at[(0, 0)].set(0       )

											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase,     attention_mask=_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )





				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

											# pad attention mask on the left
											SCREAMING_SNAKE_CASE_				=				attention_mask.at[(0, 0)].set(0       )

											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase,     attention_mask=_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )






@require_flax
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'       )
											SCREAMING_SNAKE_CASE_				=				FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'       )

											SCREAMING_SNAKE_CASE_				=				'Hello world'
											SCREAMING_SNAKE_CASE_				=				tokenizer(_lowercase,     return_tensors='np'       ).input_ids

											# typos are quickly detected (the correct argument is `do_sample`)
											with self.assertRaisesRegex(_lowercase,     'do_samples'       ):
																		model.generate(_lowercase,     do_samples=_lowercase       )

											# arbitrary arguments that will not be used anywhere are also not accepted
											with self.assertRaisesRegex(_lowercase,     'foo'       ):
																		SCREAMING_SNAKE_CASE_				=				{'foo': 'bar'}
																		model.generate(_lowercase,     **_lowercase       )





