





"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCamelCase__      =      {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_						,lowercase_						,lowercase_	)    ->						Tuple:




      """simple docstring"""

      for attribute in key.split("."	):
            _UpperCamelCase					:		Any  =				getattr(lowercase_						,lowercase_	)

      if weight_type is not None:
            _UpperCamelCase					:		Union[str, Any]  =				getattr(lowercase_						,lowercase_	).shape
      else:
            _UpperCamelCase					:		List[str]  =				hf_pointer.shape

      assert hf_shape == value.shape, (
          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":
            _UpperCamelCase					:		str  =				value
      elif weight_type == "weight_g":
            _UpperCamelCase					:		Dict  =				value
      elif weight_type == "weight_v":
            _UpperCamelCase					:		Dict  =				value
      elif weight_type == "bias":
            _UpperCamelCase					:		int  =				value
      else:
            _UpperCamelCase					:		Optional[Any]  =				value

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




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		List[Any]  =				[]
      _UpperCamelCase					:		Optional[Any]  =				fairseq_model.state_dict()

      _UpperCamelCase					:		int  =				hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            _UpperCamelCase					:		List[str]  =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowercase_						,lowercase_						,lowercase_						,lowercase_						,hf_model.config.feat_extract_norm == "group"						,)
                  _UpperCamelCase					:		Any  =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        _UpperCamelCase					:		List[str]  =				"sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

                        if key in name or key.split("w2v_model."	)[-1] == name.split("."	)[0]:
                              _UpperCamelCase					:		Dict  =				True
                              if "*" in mapped_key:
                                    _UpperCamelCase					:		Any  =				name.split(lowercase_	)[0].split("."	)[-2]
                                    if not layer_index.isnumeric():
                                          continue
                                    _UpperCamelCase					:		Optional[Any]  =				mapped_key.replace("*"						,lowercase_	)
                              if "weight_g" in name:
                                    _UpperCamelCase					:		Any  =				"weight_g"
                              elif "weight_v" in name:
                                    _UpperCamelCase					:		Any  =				"weight_v"
                              elif "weight" in name:
                                    _UpperCamelCase					:		List[Any]  =				"weight"
                              elif "bias" in name:
                                    _UpperCamelCase					:		List[Any]  =				"bias"
                              else:
                                    _UpperCamelCase					:		Union[str, Any]  =				None
                              set_recursively(lowercase_						,lowercase_						,lowercase_						,lowercase_						,lowercase_	)
                        continue
            if not is_used:
                  unused_weights.append(lowercase_	)

      logger.warning(F'''Unused weights: {unused_weights}'''	)




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_						,lowercase_						,lowercase_	)    ->						Optional[Any]:




      """simple docstring"""

      _UpperCamelCase					:		int  =				full_name.split("conv_layers."	)[-1]
      _UpperCamelCase					:		Union[str, Any]  =				name.split("."	)
      _UpperCamelCase					:		Any  =				int(items[0]	)
      _UpperCamelCase					:		Dict  =				int(items[1]	)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      F'''{full_name} has size {value.shape}, but'''
                      F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
                  )
                  _UpperCamelCase					:		List[str]  =				value
                  logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      F'''{full_name} has size {value.shape}, but'''
                      F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
                  )
                  _UpperCamelCase					:		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:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                      " found."
                  )
                  _UpperCamelCase					:		List[Any]  =				value
                  logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      F'''{full_name} has size {value.shape}, but'''
                      F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
                  )
                  _UpperCamelCase					:		Union[str, Any]  =				value
                  logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
      else:
            unused_weights.append(lowercase_	)




def     lowercase__			(    lowercase_						,lowercase_	)    ->						Optional[Any]:




      """simple docstring"""

      _UpperCamelCase					:		Union[str, Any]  =				SEWDConfig()
      if is_finetuned:
            _UpperCamelCase					:		str  =				model.wav_encoder.wav_model.cfg
      else:
            _UpperCamelCase					:		Optional[int]  =				model.cfg

      _UpperCamelCase					:		str  =				fs_config.conv_bias
      _UpperCamelCase					:		Dict  =				eval(fs_config.conv_feature_layers	)
      _UpperCamelCase					:		Union[str, Any]  =				[x[0] for x in conv_layers]
      _UpperCamelCase					:		str  =				[x[1] for x in conv_layers]
      _UpperCamelCase					:		Union[str, Any]  =				[x[2] for x in conv_layers]
      _UpperCamelCase					:		Optional[int]  =				"gelu"
      _UpperCamelCase					:		List[str]  =				"layer" if fs_config.extractor_mode == "layer_norm" else "group"
      _UpperCamelCase					:		Optional[Any]  =				0.0
      _UpperCamelCase					:		Any  =				fs_config.activation_fn.name
      _UpperCamelCase					:		Optional[int]  =				fs_config.encoder_embed_dim
      _UpperCamelCase					:		Union[str, Any]  =				0.02
      _UpperCamelCase					:		int  =				fs_config.encoder_ffn_embed_dim
      _UpperCamelCase					:		Optional[int]  =				1e-5
      _UpperCamelCase					:		int  =				fs_config.encoder_layerdrop
      _UpperCamelCase					:		List[str]  =				fs_config.encoder_attention_heads
      _UpperCamelCase					:		str  =				fs_config.conv_pos_groups
      _UpperCamelCase					:		List[str]  =				fs_config.conv_pos
      _UpperCamelCase					:		Union[str, Any]  =				len(lowercase_	)
      _UpperCamelCase					:		Optional[int]  =				fs_config.encoder_layers
      _UpperCamelCase					:		Tuple  =				fs_config.squeeze_factor
      # DeBERTa-specific parameters:
      _UpperCamelCase					:		Union[str, Any]  =				fs_config.max_position_embeddings
      _UpperCamelCase					:		Any  =				fs_config.position_buckets
      _UpperCamelCase					:		str  =				fs_config.share_att_key
      _UpperCamelCase					:		List[Any]  =				fs_config.relative_attention
      _UpperCamelCase					:		Optional[int]  =				fs_config.position_biased_input
      _UpperCamelCase					:		Dict  =				tuple(fs_config.pos_att_type.split("|"	)	)
      _UpperCamelCase					:		int  =				fs_config.norm_rel_ebd

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            _UpperCamelCase					:		Tuple  =				model.cfg
            _UpperCamelCase					:		Union[str, Any]  =				fs_config.final_dropout
            _UpperCamelCase					:		Union[str, Any]  =				fs_config.layerdrop
      _UpperCamelCase					:		Dict  =				fs_config.activation_dropout
      _UpperCamelCase					:		Any  =				fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      _UpperCamelCase					:		Optional[int]  =				fs_config.attention_dropout
      _UpperCamelCase					:		Optional[Any]  =				fs_config.dropout_input
      _UpperCamelCase					:		Optional[int]  =				fs_config.dropout
      _UpperCamelCase					:		Union[str, Any]  =				fs_config.mask_channel_length
      _UpperCamelCase					:		Dict  =				fs_config.mask_channel_prob
      _UpperCamelCase					:		List[str]  =				fs_config.mask_length
      _UpperCamelCase					:		Union[str, Any]  =				fs_config.mask_prob

      _UpperCamelCase					:		Optional[int]  =				"Wav2Vec2FeatureExtractor"
      _UpperCamelCase					:		List[str]  =				"Wav2Vec2CTCTokenizer"

      return config




@torch.no_grad()
def     lowercase__			(    lowercase_						,lowercase_						,lowercase_=None						,lowercase_=None						,lowercase_=True	)    ->						Any:




      """simple docstring"""

      if is_finetuned:
            _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		Union[str, Any]  =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]						,arg_overrides={"data": "/".join(dict_path.split("/"	)[:-1]	)}	)
      else:
            _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		Optional[int]  =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

      if config_path is not None:
            _UpperCamelCase					:		int  =				SEWDConfig.from_pretrained(lowercase_	)
      else:
            _UpperCamelCase					:		Optional[Any]  =				convert_config(model[0]						,lowercase_	)
      _UpperCamelCase					:		Dict  =				model[0].eval()

      _UpperCamelCase					:		Optional[Any]  =				True if config.feat_extract_norm == "layer" else False
      _UpperCamelCase					:		Optional[Any]  =				WavaVecaFeatureExtractor(
          feature_size=1						,sampling_rate=16_000						,padding_value=0						,do_normalize=lowercase_						,return_attention_mask=lowercase_						,)

      if is_finetuned:
            if dict_path:
                  _UpperCamelCase					:		Union[str, Any]  =				Dictionary.load(lowercase_	)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  _UpperCamelCase					:		Tuple  =				target_dict.pad_index
                  _UpperCamelCase					:		int  =				target_dict.bos_index
                  _UpperCamelCase					:		Tuple  =				target_dict.pad_index
                  _UpperCamelCase					:		Dict  =				target_dict.bos_index
                  _UpperCamelCase					:		Dict  =				target_dict.eos_index
                  _UpperCamelCase					:		Optional[int]  =				len(target_dict.symbols	)
                  _UpperCamelCase					:		List[Any]  =				os.path.join(lowercase_						,"vocab.json"	)
                  if not os.path.isdir(lowercase_	):
                        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowercase_	)	)
                        return
                  os.makedirs(lowercase_						,exist_ok=lowercase_	)
                  with open(lowercase_						,"w"						,encoding="utf-8"	) as vocab_handle:
                        json.dump(target_dict.indices						,lowercase_	)
                  _UpperCamelCase					:		List[Any]  =				WavaVecaCTCTokenizer(
                      lowercase_						,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=lowercase_						,)
                  _UpperCamelCase					:		List[str]  =				WavaVecaProcessor(feature_extractor=lowercase_						,tokenizer=lowercase_	)
                  processor.save_pretrained(lowercase_	)

            _UpperCamelCase					:		List[Any]  =				SEWDForCTC(lowercase_	)
      else:
            _UpperCamelCase					:		Dict  =				SEWDModel(lowercase_	)
            feature_extractor.save_pretrained(lowercase_	)

      recursively_load_weights(lowercase_						,lowercase_						,lowercase_	)

      hf_model.save_pretrained(lowercase_	)


if __name__ == "__main__":
  lowerCamelCase__      =      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(
      "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )







"""simple docstring"""


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
  import torch



class    __SCREAMING_SNAKE_CASE			:







  '''simple docstring'''


  def __init__(						self      :				Any    ,      __a      :				Dict    ,      __a      :				Optional[Any]=14    ,      __a      :				Tuple=7    ,      __a      :				Dict=True    ,      __a      :				str=True    ,      __a      :				int=False    ,      __a      :				Optional[Any]=True    ,      __a      :				Any=99    ,      __a      :				int=32    ,      __a      :				Any=5    ,      __a      :				Dict=4    ,      __a      :				List[Any]=37    ,      __a      :				List[Any]="gelu"    ,      __a      :				List[Any]=0.1    ,      __a      :				Any=0.1    ,      __a      :				Optional[int]=512    ,      __a      :				Dict=0.02    ,      )						-> Union[str, Any]:
        _UpperCamelCase					:		Union[str, Any]  =				parent
        _UpperCamelCase					:		Any  =				batch_size
        _UpperCamelCase					:		Optional[int]  =				seq_length
        _UpperCamelCase					:		Tuple  =				is_training
        _UpperCamelCase					:		Optional[int]  =				use_input_mask
        _UpperCamelCase					:		Any  =				use_token_type_ids
        _UpperCamelCase					:		List[str]  =				use_labels
        _UpperCamelCase					:		List[str]  =				vocab_size
        _UpperCamelCase					:		Any  =				hidden_size
        _UpperCamelCase					:		Dict  =				num_hidden_layers
        _UpperCamelCase					:		str  =				num_attention_heads
        _UpperCamelCase					:		List[str]  =				intermediate_size
        _UpperCamelCase					:		Optional[Any]  =				hidden_act
        _UpperCamelCase					:		Tuple  =				hidden_dropout_prob
        _UpperCamelCase					:		str  =				attention_probs_dropout_prob
        _UpperCamelCase					:		Tuple  =				max_position_embeddings
        _UpperCamelCase					:		str  =				initializer_range
        _UpperCamelCase					:		List[str]  =				None
        _UpperCamelCase					:		str  =				vocab_size - 1
        _UpperCamelCase					:		Any  =				vocab_size - 1
        _UpperCamelCase					:		Dict  =				vocab_size - 1

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> Union[str, Any]:
        _UpperCamelCase					:		int  =				ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size		)

        _UpperCamelCase					:		List[Any]  =				None
        if self.use_input_mask:
              _UpperCamelCase					:		int  =				random_attention_mask([self.batch_size, self.seq_length]		)

        _UpperCamelCase					:		Optional[Any]  =				GPTaConfig(
            vocab_size=self.vocab_size    ,      n_embd=self.hidden_size    ,      n_layer=self.num_hidden_layers    ,      n_head=self.num_attention_heads    ,      n_positions=self.max_position_embeddings    ,      use_cache=__a    ,      bos_token_id=self.bos_token_id    ,      eos_token_id=self.eos_token_id    ,      pad_token_id=self.pad_token_id    ,      )

        return (config, input_ids, input_mask)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> List[Any]:
        _UpperCamelCase					:		Optional[Any]  =				self.prepare_config_and_inputs()
        _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		Union[str, Any]  =				config_and_inputs
        _UpperCamelCase					:		Optional[int]  =				{"input_ids": input_ids, "attention_mask": attention_mask}
        return config, inputs_dict

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> List[Any]:
        _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		int  =				self.prepare_config_and_inputs()

        _UpperCamelCase					:		Tuple  =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
        _UpperCamelCase					:		Optional[int]  =				ids_tensor([self.batch_size, self.seq_length]    ,      vocab_size=2		)

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple    ,      __a      :				Any    ,      __a      :				int    ,      __a      :				List[str]    ,      __a      :				List[Any]		)						-> List[Any]:
        _UpperCamelCase					:		List[str]  =				20
        _UpperCamelCase					:		Tuple  =				model_class_name(__a		)

        _UpperCamelCase					:		Optional[Any]  =				model.init_cache(input_ids.shape[0]    ,      __a		)
        _UpperCamelCase					:		Union[str, Any]  =				jnp.ones((input_ids.shape[0], max_decoder_length)    ,      dtype="i4"		)

        _UpperCamelCase					:		Optional[Any]  =				jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1		)[None, :]    ,      (input_ids.shape[0], input_ids.shape[-1] - 1)		)
        _UpperCamelCase					:		Optional[int]  =				model(
            input_ids[:, :-1]    ,      attention_mask=__a    ,      past_key_values=__a    ,      position_ids=__a    ,      )

        _UpperCamelCase					:		Dict  =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype="i4"		)
        _UpperCamelCase					:		Optional[Any]  =				model(
            input_ids[:, -1:]    ,      attention_mask=__a    ,      past_key_values=outputs_cache.past_key_values    ,      position_ids=__a    ,      )

        _UpperCamelCase					:		Optional[Any]  =				model(__a		)

        _UpperCamelCase					:		Optional[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}'''		)


  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]    ,      __a      :				Dict    ,      __a      :				str    ,      __a      :				Any    ,      __a      :				Dict		)						-> int:
        _UpperCamelCase					:		Optional[Any]  =				20
        _UpperCamelCase					:		Tuple  =				model_class_name(__a		)

        _UpperCamelCase					:		Any  =				jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])		)]    ,      axis=-1    ,      )

        _UpperCamelCase					:		str  =				model.init_cache(input_ids.shape[0]    ,      __a		)
        _UpperCamelCase					:		str  =				jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1		)[None, :]    ,      (input_ids.shape[0], input_ids.shape[-1] - 1)		)

        _UpperCamelCase					:		Tuple  =				model(
            input_ids[:, :-1]    ,      attention_mask=__a    ,      past_key_values=__a    ,      position_ids=__a    ,      )
        _UpperCamelCase					:		Any  =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype="i4"		)
        _UpperCamelCase					:		List[Any]  =				model(
            input_ids[:, -1:]    ,      past_key_values=outputs_cache.past_key_values    ,      attention_mask=__a    ,      position_ids=__a    ,      )

        _UpperCamelCase					:		Tuple  =				model(__a    ,      attention_mask=__a		)

        _UpperCamelCase					:		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    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase						,     _UpperCamelCase						,     unittest.TestCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple		   =    (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
  SCREAMING_SNAKE_CASE__							:List[str]		   =    (FlaxGPTaLMHeadModel,) if is_flax_available() else ()

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> Optional[int]:
        _UpperCamelCase					:		str  =				FlaxGPTaModelTester(self		)

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> Optional[Any]:
        for model_class_name in self.all_model_classes:
              _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[str]  =				self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(__a    ,      __a    ,      __a    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> int:
        for model_class_name in self.all_model_classes:
              _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		int  =				self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  __a    ,      __a    ,      __a    ,      __a		)

  @slow
  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> Dict:
        _UpperCamelCase					:		Optional[int]  =				GPTaTokenizer.from_pretrained("gpt2"    ,      pad_token="</s>"    ,      padding_side="left"		)
        _UpperCamelCase					:		Optional[int]  =				tokenizer(["Hello this is a long string", "Hey"]    ,      return_tensors="np"    ,      padding=__a    ,      truncation=__a		)

        _UpperCamelCase					:		Tuple  =				FlaxGPTaLMHeadModel.from_pretrained("gpt2"		)
        _UpperCamelCase					:		Optional[Any]  =				False
        _UpperCamelCase					:		str  =				model.config.eos_token_id

        _UpperCamelCase					:		Any  =				jax.jit(model.generate		)

        _UpperCamelCase					:		Optional[Any]  =				jit_generate(inputs["input_ids"]    ,      attention_mask=inputs["attention_mask"]		).sequences

        _UpperCamelCase					:		Tuple  =				tokenizer.batch_decode(__a    ,      skip_special_tokens=__a		)

        _UpperCamelCase					:		Any  =				[
            "Hello this is a long string of words. I'm going to start with the first one.\n",
            "Hey, I'm not sure if I'm going to be able to do",
        ]

        self.assertListEqual(__a    ,      __a		)

  @is_pt_flax_cross_test
  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> List[str]:
        _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__		):
                    # prepare inputs
                    _UpperCamelCase					:		Optional[Any]  =				self._prepare_for_class(__a    ,      __a		)
                    _UpperCamelCase					:		int  =				{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

                    # load corresponding PyTorch class
                    _UpperCamelCase					:		str  =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
                    _UpperCamelCase					:		Union[str, Any]  =				getattr(__a    ,      __a		)

                    _UpperCamelCase,     _UpperCamelCase					:		Optional[Any]  =				pt_inputs["input_ids"].shape
                    _UpperCamelCase					:		Tuple  =				np.random.randint(0    ,      seq_length - 1    ,      size=(batch_size,)		)
                    for batch_idx, start_index in enumerate(__a		):
                          _UpperCamelCase					:		Optional[int]  =				0
                          _UpperCamelCase					:		List[str]  =				1
                          _UpperCamelCase					:		Dict  =				0
                          _UpperCamelCase					:		Optional[Any]  =				1
                    _UpperCamelCase					:		Union[str, Any]  =				pt_model_class(__a		).eval()
                    _UpperCamelCase					:		Any  =				model_class(__a    ,      dtype=jnp.floataa		)

                    _UpperCamelCase					:		int  =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,      __a		)
                    _UpperCamelCase					:		List[Any]  =				fx_state

                    with torch.no_grad():
                          _UpperCamelCase					:		List[str]  =				pt_model(**__a		).to_tuple()

                    _UpperCamelCase					:		str  =				fx_model(**__a		).to_tuple()
                    self.assertEqual(len(__a		)    ,      len(__a		)    ,      "Output lengths differ between Flax and PyTorch"		)
                    for fx_output, pt_output in zip(__a    ,      __a		):
                          self.assert_almost_equals(fx_output[:, -1]    ,      pt_output[:, -1].numpy()    ,      4e-2		)

                    with tempfile.TemporaryDirectory() as tmpdirname:
                          pt_model.save_pretrained(__a		)
                          _UpperCamelCase					:		Any  =				model_class.from_pretrained(__a    ,      from_pt=__a		)

                    _UpperCamelCase					:		Tuple  =				fx_model_loaded(**__a		).to_tuple()
                    self.assertEqual(
                        len(__a		)    ,      len(__a		)    ,      "Output lengths differ between Flax and PyTorch"		)
                    for fx_output_loaded, pt_output in zip(__a    ,      __a		):
                          self.assert_almost_equals(fx_output_loaded[:, -1]    ,      pt_output[:, -1].numpy()    ,      4e-2		)

  @is_pt_flax_cross_test
  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> Dict:
        _UpperCamelCase,     _UpperCamelCase					:		Optional[Any]  =				self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__		):
                    # prepare inputs
                    _UpperCamelCase					:		Union[str, Any]  =				self._prepare_for_class(__a    ,      __a		)
                    _UpperCamelCase					:		Optional[Any]  =				{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

                    # load corresponding PyTorch class
                    _UpperCamelCase					:		Any  =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
                    _UpperCamelCase					:		Union[str, Any]  =				getattr(__a    ,      __a		)

                    _UpperCamelCase					:		Dict  =				pt_model_class(__a		).eval()
                    _UpperCamelCase					:		Tuple  =				model_class(__a    ,      dtype=jnp.floataa		)

                    _UpperCamelCase					:		Dict  =				load_flax_weights_in_pytorch_model(__a    ,      fx_model.params		)
                    _UpperCamelCase,     _UpperCamelCase					:		int  =				pt_inputs["input_ids"].shape
                    _UpperCamelCase					:		Optional[Any]  =				np.random.randint(0    ,      seq_length - 1    ,      size=(batch_size,)		)
                    for batch_idx, start_index in enumerate(__a		):
                          _UpperCamelCase					:		Dict  =				0
                          _UpperCamelCase					:		Union[str, Any]  =				1
                          _UpperCamelCase					:		Dict  =				0
                          _UpperCamelCase					:		Optional[int]  =				1

                    # make sure weights are tied in PyTorch
                    pt_model.tie_weights()

                    with torch.no_grad():
                          _UpperCamelCase					:		Optional[int]  =				pt_model(**__a		).to_tuple()

                    _UpperCamelCase					:		List[Any]  =				fx_model(**__a		).to_tuple()
                    self.assertEqual(len(__a		)    ,      len(__a		)    ,      "Output lengths differ between Flax and PyTorch"		)
                    for fx_output, pt_output in zip(__a    ,      __a		):
                          self.assert_almost_equals(fx_output[:, -1]    ,      pt_output[:, -1].numpy()    ,      4e-2		)

                    with tempfile.TemporaryDirectory() as tmpdirname:
                          fx_model.save_pretrained(__a		)
                          _UpperCamelCase					:		Tuple  =				pt_model_class.from_pretrained(__a    ,      from_flax=__a		)

                    with torch.no_grad():
                          _UpperCamelCase					:		int  =				pt_model_loaded(**__a		).to_tuple()

                    self.assertEqual(
                        len(__a		)    ,      len(__a		)    ,      "Output lengths differ between Flax and PyTorch"		)
                    for fx_output, pt_output in zip(__a    ,      __a		):
                          self.assert_almost_equals(fx_output[:, -1]    ,      pt_output[:, -1].numpy()    ,      4e-2		)


  @slow
  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> int:
        for model_class_name in self.all_model_classes:
              _UpperCamelCase					:		Union[str, Any]  =				model_class_name.from_pretrained("gpt2"    ,      from_pt=__a		)
              _UpperCamelCase					:		List[str]  =				model(np.ones((1, 1)		)		)
              self.assertIsNotNone(__a		)
