


'''simple docstring'''



import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


_lowercase      :      List[Any]            =					logging.getLogger(__name__)

_lowercase      :      List[Any]            =					50  # max width of layer names
_lowercase      :      Tuple            =					70  # max width of quantizer names


def 				lowerCamelCase__				(	A       :      List[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     parser.add_argument_group('''quant_trainer arguments'''					)
						group.add_argument('''--wprec'''				,       type=A				,       default=8				,       help='''weight precision'''					)
						group.add_argument('''--aprec'''				,       type=A				,       default=8				,       help='''activation precision'''					)
						group.add_argument('''--quant-per-tensor'''				,       action='''store_true'''				,       help='''per tensor weight scaling'''					)
						group.add_argument('''--quant-disable'''				,       action='''store_true'''				,       help='''disable all quantizers'''					)
						group.add_argument('''--quant-disable-embeddings'''				,       action='''store_true'''				,       help='''disable all embeddings quantizers'''					)
						group.add_argument('''--quant-disable-keyword'''				,       type=A				,       nargs='''+'''				,       help='''disable quantizers by keyword'''					)
						group.add_argument('''--quant-disable-layer-module'''				,       type=A				,       help='''disable quantizers by keyword under layer.'''					)
						group.add_argument('''--quant-enable-layer-module'''				,       type=A				,       help='''enable quantizers by keyword under layer'''					)
						group.add_argument('''--calibrator'''				,       default='''max'''				,       help='''which quantization range calibrator to use'''					)
						group.add_argument('''--percentile'''				,       default=A				,       type=A				,       help='''percentile for PercentileCalibrator'''					)
						group.add_argument('''--fuse-qkv'''				,       action='''store_true'''				,       help='''use the same scale factor for qkv'''					)
						group.add_argument('''--clip-gelu'''				,       metavar='''N'''				,       type=A				,       help='''clip gelu output maximum value to N'''					)
						group.add_argument(
						    '''--recalibrate-weights'''				,       action='''store_true'''				,       help=(
						        '''recalibrate weight amaxes by taking the max of the weights.'''
						        ''' amaxes will be computed with the current quantization granularity (axis).'''
						    )				,       )


def 				lowerCamelCase__				(	A       :      int					):




						'''simple docstring'''






						if args.calibrator == "max":
												UpperCAmelCase					    =     '''max'''
						elif args.calibrator == "percentile":
												if args.percentile is None:
																		raise ValueError('''Specify --percentile when using percentile calibrator'''					)
												UpperCAmelCase					    =     '''histogram'''
						elif args.calibrator == "mse":
												UpperCAmelCase					    =     '''histogram'''
						else:
												raise ValueError(f"""Invalid calibrator {args.calibrator}"""					)

						UpperCAmelCase					    =     QuantDescriptor(num_bits=args.aprec				,       calib_method=A					)
						UpperCAmelCase					    =     QuantDescriptor(num_bits=args.wprec				,       axis=(None if args.quant_per_tensor else (0,))					)
						quant_nn.QuantLinear.set_default_quant_desc_input(A					)
						quant_nn.QuantLinear.set_default_quant_desc_weight(A					)


def 				lowerCamelCase__				(	A       :      List[Any]				,       A       :      Any				,       A       :      Union[str, Any]=False				,       A       :      int=False					):




						'''simple docstring'''






						logger.info('''Configuring Model for Quantization'''					)
						logger.info(f"""using quantization package {pytorch_quantization.__file__}"""					)

						if not calib:
												if args.quant_disable_embeddings:
																		set_quantizer_by_name(A				,       ['''embeddings''']				,       which='''weight'''				,       _disabled=A					)

												if args.quant_disable:
																		set_quantizer_by_name(A				,       ['''''']				,       _disabled=A					)

												if args.quant_disable_keyword:
																		set_quantizer_by_name(A				,       args.quant_disable_keyword				,       _disabled=A					)

												if args.quant_disable_layer_module:
																		set_quantizer_by_name(A				,       [R'''layer.\d+.''' + args.quant_disable_layer_module]				,       _disabled=A					)

												if args.quant_enable_layer_module:
																		set_quantizer_by_name(A				,       [R'''layer.\d+.''' + args.quant_enable_layer_module]				,       _disabled=A					)

												if args.recalibrate_weights:
																		recalibrate_weights(A					)

												if args.fuse_qkv:
																		fuse_qkv(A				,       A					)

						if args.clip_gelu:
												clip_gelu(A				,       args.clip_gelu					)

						# if args.local_rank in [-1, 0] and not calib:
						print_quant_summary(A					)


def 				lowerCamelCase__				(	A       :      Dict					):




						'''simple docstring'''






						logger.info('''Enabling Calibration'''					)
						for name, module in model.named_modules():
												if name.endswith('''_quantizer'''					):
																		if module._calibrator is not None:
																								module.disable_quant()
																								module.enable_calib()
																		else:
																								module.disable()
																		logger.info(f"""{name:80}: {module}"""					)


def 				lowerCamelCase__				(	A       :      Tuple				,       A       :      str					):




						'''simple docstring'''






						logger.info('''Loading calibrated amax'''					)
						for name, module in model.named_modules():
												if name.endswith('''_quantizer'''					):
																		if module._calibrator is not None:
																								if isinstance(module._calibrator				,       calib.MaxCalibrator					):
																														module.load_calib_amax()
																								else:
																														module.load_calib_amax('''percentile'''				,       percentile=args.percentile					)
																								module.enable_quant()
																								module.disable_calib()
																		else:
																								module.enable()
						model.cuda()
						print_quant_summary(A					)


def 				lowerCamelCase__				(	A       :      int				,       A       :      Optional[Any]					):




						'''simple docstring'''






						def fusea(A       :      Union[str, Any]				,       A       :      Optional[Any]				,       A       :      str					):
												for mod in [qq, qk, qv]:
																		if not hasattr(A				,       '''_amax'''					):
																								print('''          WARNING: NO AMAX BUFFER'''					)
																								return
												UpperCAmelCase					    =     qq._amax.detach().item()
												UpperCAmelCase					    =     qk._amax.detach().item()
												UpperCAmelCase					    =     qv._amax.detach().item()

												UpperCAmelCase					    =     max(A				,       A				,       A					)
												qq._amax.fill_(A					)
												qk._amax.fill_(A					)
												qv._amax.fill_(A					)
												logger.info(f"""          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}"""					)

						for name, mod in model.named_modules():
												if name.endswith('''.attention.self'''					):
																		logger.info(f"""FUSE_QKV: {name:{name_width}}"""					)
																		fusea(mod.matmul_q_input_quantizer				,       mod.matmul_k_input_quantizer				,       mod.matmul_v_input_quantizer					)
																		if args.quant_per_tensor:
																								fusea(mod.query._weight_quantizer				,       mod.key._weight_quantizer				,       mod.value._weight_quantizer					)


def 				lowerCamelCase__				(	A       :      int				,       A       :      Tuple					):




						'''simple docstring'''






						for name, mod in model.named_modules():
												if name.endswith('''.output.dense'''					) and not name.endswith('''attention.output.dense'''					):
																		UpperCAmelCase					    =     mod._input_quantizer._amax.data.detach().item()
																		mod._input_quantizer._amax.data.detach().clamp_(max=A					)
																		UpperCAmelCase					    =     mod._input_quantizer._amax.data.detach().item()
																		logger.info(f"""CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}"""					)


def 				lowerCamelCase__				(	A       :      Optional[Any]					):




						'''simple docstring'''






						for name, mod in model.named_modules():
												if hasattr(A				,       '''_weight_quantizer'''					) and mod._weight_quantizer.axis is not None:
																		UpperCAmelCase					    =     mod.weight.shape[0]
																		UpperCAmelCase					    =     mod._weight_quantizer._amax.detach()
																		UpperCAmelCase					    =     torch.ones(A				,       dtype=amax.dtype				,       device=amax.device					) * amax
																		print(f"""expanding {name} {amax} -> {mod._weight_quantizer._amax}"""					)


def 				lowerCamelCase__				(	A       :      Union[str, Any]					):




						'''simple docstring'''






						for name, mod in model.named_modules():
												if hasattr(A				,       '''_weight_quantizer'''					):
																		if not hasattr(mod.weight_quantizer				,       '''_amax'''					):
																								print('''RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER'''					)
																								continue

																		# determine which axes to reduce across
																		# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
																		UpperCAmelCase					    =     set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis					)
																		UpperCAmelCase					    =     set(range(len(mod.weight.size()					)					)					) - axis_set
																		UpperCAmelCase					    =     pytorch_quantization.utils.reduce_amax(mod.weight				,       axis=A				,       keepdims=A					).detach()
																		logger.info(f"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""					)
																		UpperCAmelCase					    =     amax


def 				lowerCamelCase__				(	A       :      List[str]				,       A       :      Union[str, Any]=25				,       A       :      Tuple=1_80				,       A       :      List[Any]=None					):




						'''simple docstring'''






						if ignore is None:
												UpperCAmelCase					    =     []
						elif not isinstance(A				,       A					):
												UpperCAmelCase					    =     [ignore]

						UpperCAmelCase					    =     0
						for name, mod in model.named_modules():
												if not hasattr(A				,       '''weight'''					):
																		continue
												UpperCAmelCase					    =     max(A				,       len(A					)					)

						for name, mod in model.named_modules():
												UpperCAmelCase					    =     getattr(A				,       '''_input_quantizer'''				,       A					)
												UpperCAmelCase					    =     getattr(A				,       '''_weight_quantizer'''				,       A					)
												if not hasattr(A				,       '''weight'''					):
																		continue
												if type(A					) in ignore:
																		continue
												if [True for s in ignore if type(A					) is str and s in name]:
																		continue
												UpperCAmelCase					    =     f"""Act:{input_q.extra_repr()}"""
												UpperCAmelCase					    =     f"""Wgt:{weight_q.extra_repr()}"""
												UpperCAmelCase					    =     f"""{name:{name_width}} {act_str} {wgt_str}"""
												if len(A					) <= line_width:
																		logger.info(A					)
												else:
																		logger.info(f"""{name:{name_width}} {act_str}"""					)
																		logger.info(f"""{"  ":{name_width}} {wgt_str}"""					)


def 				lowerCamelCase__				(	A       :      Optional[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     0
						for name, mod in model.named_modules():
												if isinstance(A				,       pytorch_quantization.nn.TensorQuantizer					):
																		print(f"""{name:80} {mod}"""					)
																		count += 1
						print(f"""{count} TensorQuantizers found in model"""					)


def 				lowerCamelCase__				(	A       :      Any				,       A       :      Any				,       A       :      Dict				,       A       :      List[str]				,       A       :      Any					):




						'''simple docstring'''






						UpperCAmelCase					    =     getattr(A				,       A				,       A					)
						if quantizer_mod is not None:
												assert hasattr(A				,       A					)
												setattr(A				,       A				,       A					)
						else:
												logger.warning(f"""{name} has no {quantizer}"""					)


def 				lowerCamelCase__				(	A       :      Dict				,       A       :      List[str]				,       A       :      Optional[Any]="both"				,       **A       :      Optional[int]					):




						'''simple docstring'''






						UpperCAmelCase					    =     f"""Warning: changing {which} quantizers of {name:{qname_width}}"""
						for k, v in kwargs.items():
												s += f""" {k}={v}"""
												if which in ["input", "both"]:
																		set_quantizer(A				,       A				,       '''_input_quantizer'''				,       A				,       A					)
												if which in ["weight", "both"]:
																		set_quantizer(A				,       A				,       '''_weight_quantizer'''				,       A				,       A					)
						logger.info(A					)




def 				lowerCamelCase__				(	A       :      Any				,       A       :      Tuple				,       **A       :      Optional[int]					):




						'''simple docstring'''






						for name, mod in model.named_modules():
												if hasattr(A				,       '''_input_quantizer'''					) or hasattr(A				,       '''_weight_quantizer'''					):
																		for n in names:
																								if re.search(A				,       A					):
																														set_quantizers(A				,       A				,       **A					)
												elif name.endswith('''_quantizer'''					):
																		for n in names:
																								if re.search(A				,       A					):
																														UpperCAmelCase					    =     f"""Warning: changing {name:{name_width}}"""
																														for k, v in kwargs.items():
																																				s += f""" {k}={v}"""
																																				setattr(A				,       A				,       A					)
																														logger.info(A					)









'''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

	_lowercase      :      List[str]            =					"""0.12"""  # assumed parallelism: 8


if is_torch_available():
	import torch


def 				lowerCamelCase__				(	A       :      str				,       A       :      str				,       A       :      List[Any]=None					):




						'''simple docstring'''






						if rng is None:
												UpperCAmelCase					    =     random.Random()

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

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

						UpperCAmelCase					    =     np.array(A				,       dtype=jnp.intaa					).reshape(A					)

						return output


def 				lowerCamelCase__				(	A       :      int				,       A       :      Optional[int]=None					):




						'''simple docstring'''






						UpperCAmelCase					    =     ids_tensor(A				,       vocab_size=2				,       rng=A					)
						# make sure that at least one token is attended to for each batch
						UpperCAmelCase					    =     1
						return attn_mask






@require_flax
class 	UpperCamelCase__:
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[Any]  		=	()




				def 					a__(		self      :			str				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

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

										UpperCAmelCase					    =     jnp.ones_like(lowerCAmelCase				)
										UpperCAmelCase					    =     attention_mask[:max_batch_size, :sequence_length]

										# generate max 5 tokens
										UpperCAmelCase					    =     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()`
																UpperCAmelCase					    =     config.eos_token_id
										return config, input_ids, attention_mask, max_length




				@is_pt_flax_cross_test
				def 					a__(		self      :			Dict				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

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

																UpperCAmelCase					    =     flax_model.generate(lowerCAmelCase				).sequences
																UpperCAmelCase					    =     pt_model.generate(torch.tensor(lowerCAmelCase				,   dtype=torch.long				)				)

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

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




				def 					a__(		self      :			Any				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

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

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

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




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

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

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

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




				def 					a__(		self      :			str				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

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

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

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




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

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




				def 					a__(		self      :			Tuple				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0.8
										UpperCAmelCase					    =     10
										UpperCAmelCase					    =     0.3
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

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

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

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




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

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

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

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




				def 					a__(		self      :			Tuple				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

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

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

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




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

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

										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

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

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

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




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

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

										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

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

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

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





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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

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

										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

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

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

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







@require_flax
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			Union[str, Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''				)
										UpperCAmelCase					    =     FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)

										UpperCAmelCase					    =     '''Hello world'''
										UpperCAmelCase					    =     tokenizer(lowerCAmelCase				,   return_tensors='''np'''				).input_ids

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

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





