



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_lowercase     							=						logging.get_logger(__name__)

_lowercase     							=						{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
_lowercase     							=						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]






def      _A				(UpperCamelCase		:      Optional[int] )  ->Optional[int]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		{}
			with open(UpperCamelCase      ,			"""r""" ) as file:
						for line_number, line in enumerate(UpperCamelCase ):
									lowerCamelCase__ :      List[str]								=		line.strip()
									if line:
												lowerCamelCase__ :      List[str]								=		line.split()
												lowerCamelCase__ :      Optional[int]								=		line_number
												lowerCamelCase__ :      Union[str, Any]								=		words[0]
												lowerCamelCase__ :      Optional[int]								=		value
			return result






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Tuple      ,			UpperCamelCase		:      Any      ,			UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      List[Any] )  ->Optional[int]:



			'''simple docstring'''







			for attribute in key.split(""".""" ):
						lowerCamelCase__ :      Any								=		getattr(UpperCamelCase      ,			UpperCamelCase )

			lowerCamelCase__ :      Optional[int]								=		None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(UpperCamelCase ):
									lowerCamelCase__ :      List[str]								=		PARAM_MAPPING[full_name.split(""".""" )[-1]]
									lowerCamelCase__ :      int								=		"""param"""

			if weight_type is not None and weight_type != "param":
						lowerCamelCase__ :      Union[str, Any]								=		getattr(UpperCamelCase      ,			UpperCamelCase ).shape
			elif weight_type is not None and weight_type == "param":
						lowerCamelCase__ :      Optional[int]								=		hf_pointer
						for attribute in hf_param_name.split(""".""" ):
									lowerCamelCase__ :      Optional[Any]								=		getattr(UpperCamelCase      ,			UpperCamelCase )
						lowerCamelCase__ :      List[Any]								=		shape_pointer.shape

						# let's reduce dimension
						lowerCamelCase__ :      Union[str, Any]								=		value[0]
			else:
						lowerCamelCase__ :      List[str]								=		hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
						    f" {value.shape} for {full_name}" )

			if weight_type == "weight":
						lowerCamelCase__ :      List[str]								=		value
			elif weight_type == "weight_g":
						lowerCamelCase__ :      Any								=		value
			elif weight_type == "weight_v":
						lowerCamelCase__ :      Optional[Any]								=		value
			elif weight_type == "bias":
						lowerCamelCase__ :      Any								=		value
			elif weight_type == "param":
						for attribute in hf_param_name.split(""".""" ):
									lowerCamelCase__ :      int								=		getattr(UpperCamelCase      ,			UpperCamelCase )
						lowerCamelCase__ :      int								=		value
			else:
						lowerCamelCase__ :      Optional[int]								=		value

			logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )






def      _A				(UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      Dict      ,			UpperCamelCase		:      List[Any]      ,			UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      Optional[int] )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(UpperCamelCase ):
									lowerCamelCase__ :      List[str]								=		PARAM_MAPPING[full_name.split(""".""" )[-1]]
									lowerCamelCase__ :      Tuple								=		"""param"""

			if weight_type is not None and weight_type != "param":
						lowerCamelCase__ :      Dict								=		""".""".join([key, weight_type] )
			elif weight_type is not None and weight_type == "param":
						lowerCamelCase__ :      Tuple								=		""".""".join([key, hf_param_name] )
			else:
						lowerCamelCase__ :      Dict								=		key

			lowerCamelCase__ :      int								=		value if """lm_head""" in full_key else value[0]


_lowercase     							=						{
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}






def      _A				(UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Tuple      ,			UpperCamelCase		:      Optional[Any]=None      ,			UpperCamelCase		:      Any=None )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		False
			for key, mapped_key in MAPPING.items():
						lowerCamelCase__ :      Tuple								=		"""wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
						if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
									lowerCamelCase__ :      List[Any]								=		True
									if "*" in mapped_key:
												lowerCamelCase__ :      int								=		name.split(UpperCamelCase )[0].split(""".""" )[-2]
												lowerCamelCase__ :      Tuple								=		mapped_key.replace("""*"""      ,			UpperCamelCase )
									if "weight_g" in name:
												lowerCamelCase__ :      Optional[int]								=		"""weight_g"""
									elif "weight_v" in name:
												lowerCamelCase__ :      List[Any]								=		"""weight_v"""
									elif "bias" in name:
												lowerCamelCase__ :      List[str]								=		"""bias"""
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowerCamelCase__ :      List[str]								=		"""weight"""
									else:
												lowerCamelCase__ :      Optional[int]								=		None
									if hf_dict is not None:
												rename_dict(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
									else:
												set_recursively(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
									return is_used
			return is_used






def      _A				(UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Tuple )  ->int:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		[]
			lowerCamelCase__ :      Dict								=		fairseq_model.state_dict()

			lowerCamelCase__ :      List[Any]								=		hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCamelCase__ :      Dict								=		False
						if "conv_layers" in name:
									load_conv_layer(
									    UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			hf_model.config.feat_extract_norm == """group"""      ,			)
									lowerCamelCase__ :      Dict								=		True
						else:
									lowerCamelCase__ :      int								=		load_wavaveca_layer(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
						if not is_used:
									unused_weights.append(UpperCamelCase )

			logger.warning(f"Unused weights: {unused_weights}" )






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      str      ,			UpperCamelCase		:      Tuple      ,			UpperCamelCase		:      Union[str, Any] )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		full_name.split("""conv_layers.""" )[-1]
			lowerCamelCase__ :      List[str]								=		name.split(""".""" )
			lowerCamelCase__ :      Optional[Any]								=		int(items[0] )
			lowerCamelCase__ :      Dict								=		int(items[1] )

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    f"{full_name} has size {value.shape}, but"
												    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." )
									lowerCamelCase__ :      Optional[int]								=		value
									logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    f"{full_name} has size {value.shape}, but"
												    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." )
									lowerCamelCase__ :      List[str]								=		value
									logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    f"{full_name} has size {value.shape}, but"
												    f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found." )
									lowerCamelCase__ :      str								=		value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    f"{full_name} has size {value.shape}, but"
												    f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found." )
									lowerCamelCase__ :      Tuple								=		value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
			else:
						unused_weights.append(UpperCamelCase )






@torch.no_grad()
def      _A				(UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      str      ,			UpperCamelCase		:      List[Any]=None      ,			UpperCamelCase		:      List[Any]=None      ,			UpperCamelCase		:      Any=True      ,			UpperCamelCase		:      Optional[int]=False )  ->List[str]:



			'''simple docstring'''







			if config_path is not None:
						lowerCamelCase__ :      Optional[Any]								=		WavaVecaConfig.from_pretrained(UpperCamelCase )
			else:
						lowerCamelCase__ :      Any								=		WavaVecaConfig()

			if is_seq_class:
						lowerCamelCase__ :      int								=		read_txt_into_dict(UpperCamelCase )
						lowerCamelCase__ :      List[str]								=		idalabel
						lowerCamelCase__ :      Union[str, Any]								=		WavaVecaForSequenceClassification(UpperCamelCase )
						lowerCamelCase__ :      Optional[Any]								=		WavaVecaFeatureExtractor(
						    feature_size=1      ,			sampling_rate=16000      ,			padding_value=0      ,			do_normalize=UpperCamelCase      ,			return_attention_mask=UpperCamelCase      ,			)
						feature_extractor.save_pretrained(UpperCamelCase )

			elif is_finetuned:
						if dict_path:
									lowerCamelCase__ :      List[Any]								=		Dictionary.load(UpperCamelCase )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									lowerCamelCase__ :      Optional[Any]								=		target_dict.pad_index
									lowerCamelCase__ :      Any								=		target_dict.bos_index
									lowerCamelCase__ :      Any								=		target_dict.eos_index
									lowerCamelCase__ :      str								=		len(target_dict.symbols )
									lowerCamelCase__ :      Tuple								=		os.path.join(UpperCamelCase      ,			"""vocab.json""" )
									if not os.path.isdir(UpperCamelCase ):
												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(UpperCamelCase ) )
												return
									os.makedirs(UpperCamelCase      ,			exist_ok=UpperCamelCase )
									lowerCamelCase__ :      Optional[int]								=		target_dict.indices

									# fairseq has the <pad> and <s> switched
									lowerCamelCase__ :      List[str]								=		0
									lowerCamelCase__ :      List[str]								=		1
									with open(UpperCamelCase      ,			"""w"""      ,			encoding="""utf-8""" ) as vocab_handle:
												json.dump(UpperCamelCase      ,			UpperCamelCase )
									lowerCamelCase__ :      Optional[int]								=		WavaVecaCTCTokenizer(
									    UpperCamelCase      ,			unk_token=target_dict.unk_word      ,			pad_token=target_dict.pad_word      ,			bos_token=target_dict.bos_word      ,			eos_token=target_dict.eos_word      ,			word_delimiter_token="""|"""      ,			do_lower_case=UpperCamelCase      ,			)
									lowerCamelCase__ :      Optional[int]								=		True if config.feat_extract_norm == """layer""" else False
									lowerCamelCase__ :      Optional[int]								=		WavaVecaFeatureExtractor(
									    feature_size=1      ,			sampling_rate=16000      ,			padding_value=0      ,			do_normalize=UpperCamelCase      ,			return_attention_mask=UpperCamelCase      ,			)
									lowerCamelCase__ :      Optional[int]								=		WavaVecaProcessor(feature_extractor=UpperCamelCase      ,			tokenizer=UpperCamelCase )
									processor.save_pretrained(UpperCamelCase )

						lowerCamelCase__ :      List[str]								=		WavaVecaForCTC(UpperCamelCase )
			else:
						lowerCamelCase__ :      Optional[Any]								=		WavaVecaForPreTraining(UpperCamelCase )

			if is_finetuned or is_seq_class:
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]      ,			arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
			else:
						lowerCamelCase__ :      int								=		argparse.Namespace(task="""audio_pretraining""" )
						lowerCamelCase__ :      Tuple								=		fairseq.tasks.setup_task(UpperCamelCase )

						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Any								=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,			task=UpperCamelCase )

			lowerCamelCase__ :      Any								=		model[0].eval()

			recursively_load_weights(UpperCamelCase      ,			UpperCamelCase      ,			not is_finetuned )

			hf_wavavec.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_lowercase     							=						argparse.ArgumentParser()
				parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
				parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
				parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
				parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
				parser.add_argument(
				    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
				)
				parser.add_argument(
				    '''--is_seq_class''',
				    action='''store_true''',
				    help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
				)
				_lowercase     							=						parser.parse_args()

				_lowercase     							=						not args.not_finetuned and not args.is_seq_class
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.dict_path,
				    is_finetuned,
				    args.is_seq_class,
				)








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     							=						'''0.12'''  # assumed parallelism: 8


if is_torch_available():
				import torch






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      Optional[Any]=None )  ->Optional[int]:



			'''simple docstring'''







			if rng is None:
						lowerCamelCase__ :      List[Any]								=		random.Random()

			lowerCamelCase__ :      Tuple								=		1
			for dim in shape:
						total_dims *= dim

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

			lowerCamelCase__ :      str								=		np.array(UpperCamelCase      ,			dtype=jnp.intaa ).reshape(UpperCamelCase )

			return output






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      Dict=None )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		ids_tensor(UpperCamelCase      ,			vocab_size=2      ,			rng=UpperCamelCase )
			# make sure that at least one token is attended to for each batch
			lowerCamelCase__ :      int								=		1
			return attn_mask
@require_flax
class 				__A					:
			UpperCamelCase					:List[Any]     =			None
			UpperCamelCase					:Dict     =			()





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__ :      Dict								=		self.model_tester.prepare_config_and_inputs_for_common()

						# cut to half length & take max batch_size 3
						lowerCamelCase__ :      List[Any]								=		2
						lowerCamelCase__ :      Any								=		inputs["""input_ids"""].shape[-1] // 2
						lowerCamelCase__ :      Dict								=		inputs["""input_ids"""][:max_batch_size, :sequence_length]

						lowerCamelCase__ :      List[Any]								=		jnp.ones_like(__magic_name__				)
						lowerCamelCase__ :      Dict								=		attention_mask[:max_batch_size, :sequence_length]

						# generate max 5 tokens
						lowerCamelCase__ :      Tuple								=		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()`
									lowerCamelCase__ :      List[Any]								=		config.eos_token_id
						return config, input_ids, attention_mask, max_length





			@is_pt_flax_cross_test
			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      List[Any]								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Tuple								=		False
						lowerCamelCase__ :      Tuple								=		max_length
						lowerCamelCase__ :      List[Any]								=		0

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      List[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Tuple								=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCamelCase__ :      Union[str, Any]								=		getattr(__magic_name__							,    __magic_name__				)
									lowerCamelCase__ :      Dict								=		pt_model_class(__magic_name__				).eval()
									lowerCamelCase__ :      Union[str, Any]								=		load_flax_weights_in_pytorch_model(__magic_name__							,    flax_model.params				)

									lowerCamelCase__ :      str								=		flax_model.generate(__magic_name__				).sequences
									lowerCamelCase__ :      str								=		pt_model.generate(torch.tensor(__magic_name__							,    dtype=torch.long				)				)

									if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
												lowerCamelCase__ :      Optional[Any]								=		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Tuple								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Dict								=		False
						lowerCamelCase__ :      str								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Tuple								=		model_class(__magic_name__				)

									lowerCamelCase__ :      int								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      int								=		jit(model.generate				)
									lowerCamelCase__ :      List[str]								=		jit_generate(__magic_name__				).sequences

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





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      List[str]								=		True
						lowerCamelCase__ :      Dict								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      str								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Dict								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Tuple								=		jit(model.generate				)
									lowerCamelCase__ :      List[str]								=		jit_generate(__magic_name__				).sequences

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





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Dict								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Union[str, Any]								=		False
						lowerCamelCase__ :      Dict								=		max_length
						lowerCamelCase__ :      str								=		2

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[int]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Union[str, Any]								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Dict								=		jit(model.generate				)
									lowerCamelCase__ :      Optional[int]								=		jit_generate(__magic_name__				).sequences

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





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      int								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Dict								=		False
						lowerCamelCase__ :      List[Any]								=		max_length
						lowerCamelCase__ :      List[Any]								=		2
						lowerCamelCase__ :      List[str]								=		2

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Union[str, Any]								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[0]							,    input_ids.shape[0] * config.num_return_sequences				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Tuple								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Optional[int]								=		True
						lowerCamelCase__ :      str								=		max_length
						lowerCamelCase__ :      Optional[int]								=		0.8
						lowerCamelCase__ :      List[Any]								=		10
						lowerCamelCase__ :      int								=		0.3
						lowerCamelCase__ :      Union[str, Any]								=		1
						lowerCamelCase__ :      int								=		8
						lowerCamelCase__ :      Tuple								=		9

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Any								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Optional[int]								=		jit(model.generate				)
									lowerCamelCase__ :      List[Any]								=		jit_generate(__magic_name__				).sequences

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





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      str								=		max_length
						lowerCamelCase__ :      List[str]								=		1
						lowerCamelCase__ :      Optional[int]								=		8
						lowerCamelCase__ :      Union[str, Any]								=		9

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Dict								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Tuple								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Optional[int]								=		jit(model.generate				)
									lowerCamelCase__ :      Dict								=		jit_generate(__magic_name__				).sequences

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





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      int								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Tuple								=		max_length
						lowerCamelCase__ :      Optional[int]								=		2
						lowerCamelCase__ :      Union[str, Any]								=		1
						lowerCamelCase__ :      Tuple								=		8
						lowerCamelCase__ :      Tuple								=		9

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[int]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      List[Any]								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      List[Any]								=		jit(model.generate				)
									lowerCamelCase__ :      Union[str, Any]								=		jit_generate(__magic_name__				).sequences

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





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Tuple								=		self._get_input_ids_and_config()

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

						lowerCamelCase__ :      List[str]								=		False
						lowerCamelCase__ :      str								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Dict								=		model_class(__magic_name__				)

									lowerCamelCase__ :      List[str]								=		model.generate(__magic_name__							,    attention_mask=__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Tuple								=		jit(model.generate				)
									lowerCamelCase__ :      Any								=		jit_generate(__magic_name__							,    attention_mask=__magic_name__				).sequences

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





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		self._get_input_ids_and_config()

						# pad attention mask on the left
						lowerCamelCase__ :      Optional[Any]								=		attention_mask.at[(0, 0)].set(0				)

						lowerCamelCase__ :      int								=		True
						lowerCamelCase__ :      str								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      int								=		model_class(__magic_name__				)

									lowerCamelCase__ :      str								=		model.generate(__magic_name__							,    attention_mask=__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      List[Any]								=		jit(model.generate				)
									lowerCamelCase__ :      List[Any]								=		jit_generate(__magic_name__							,    attention_mask=__magic_name__				).sequences

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





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		self._get_input_ids_and_config()

						# pad attention mask on the left
						lowerCamelCase__ :      Union[str, Any]								=		attention_mask.at[(0, 0)].set(0				)

						lowerCamelCase__ :      Tuple								=		2
						lowerCamelCase__ :      List[Any]								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Union[str, Any]								=		model.generate(__magic_name__							,    attention_mask=__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      int								=		jit(model.generate				)
									lowerCamelCase__ :      Any								=		jit_generate(__magic_name__							,    attention_mask=__magic_name__				).sequences

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







@require_flax
class 				__A					( unittest.TestCase      ):





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""				)
						lowerCamelCase__ :      Tuple								=		FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)

						lowerCamelCase__ :      Dict								=		"""Hello world"""
						lowerCamelCase__ :      Union[str, Any]								=		tokenizer(__magic_name__							,    return_tensors="""np"""				).input_ids

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

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



