
'''simple docstring'''






import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


UpperCAmelCase_							: Tuple =							float('nan')

class 			UpperCAmelCase__    :



    def __init__(						self   :							str,__A   :							int  ):
          _lowerCamelCase					: List[str]		    = sys.stdout
          _lowerCamelCase					: List[str]		    = open(lowercase__,"a"  )



    def __getattr__(						self   :							Dict,__A   :							Optional[int]  ):
          return getattr(self.stdout,lowercase__  )



    def 	lowerCamelCase_	(						self   :							List[Any],__A   :							Dict  ):
          self.stdout.write(lowercase__  )
          # strip tqdm codes
          self.file.write(re.sub(r"^.*\r","",lowercase__,0,re.M  )  )







def 		A_							(						_lowerCAmelCase :     str=80					,						_lowerCAmelCase :     List[str]=False				):



      """simple docstring"""




      _lowerCamelCase					: str		    = []

      # deal with critical env vars
      _lowerCamelCase					: Optional[int]		    = ["""CUDA_VISIBLE_DEVICES"""]
      for key in env_keys:
            _lowerCamelCase					: Tuple		    = os.environ.get(UpperCamelCase__					,						UpperCamelCase__				)
            if val is not None:
                  cmd.append(F'{key}={val}'				)

    # python executable (not always needed if the script is executable)
      _lowerCamelCase					: str		    = sys.executable if full_python_path else sys.executable.split("/"				)[-1]
      cmd.append(UpperCamelCase__				)

      # now the normal args
      cmd += list(map(shlex.quote					,						sys.argv				)				)

      # split up into up to MAX_WIDTH lines with shell multi-line escapes
      _lowerCamelCase					: Any		    = []
      _lowerCamelCase					: Tuple		    = """"""
      while len(UpperCamelCase__				) > 0:
            current_line += F'{cmd.pop(0				)} '
            if len(UpperCamelCase__				) == 0 or len(UpperCamelCase__				) + len(cmd[0]				) + 1 > max_width - 1:
                  lines.append(UpperCamelCase__				)
                  _lowerCamelCase					: Union[str, Any]		    = """"""
      return "\\\n".join(UpperCamelCase__				)







def 		A_							(						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     str				):



      """simple docstring"""




      _lowerCamelCase					: Union[str, Any]		    = re.sub(r"[\\\n]+"					,						" "					,						args.base_cmd				)

      # remove --output_dir if any and set our own
      _lowerCamelCase					: Tuple		    = re.sub("--output_dir\s+[^\s]+"					,						""					,						args.base_cmd				)
      args.base_cmd += F' --output_dir {output_dir}'

      # ensure we have --overwrite_output_dir
      _lowerCamelCase					: Optional[Any]		    = re.sub("--overwrite_output_dir\s+"					,						""					,						args.base_cmd				)
      args.base_cmd += " --overwrite_output_dir"

      return [sys.executable] + shlex.split(args.base_cmd				)







def 		A_							(						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     Optional[int]					,						_lowerCAmelCase :     Union[str, Any]				):



      """simple docstring"""




      if 0:
            import random
            from time import sleep

            sleep(0				)
            return dict(
                {k: random.uniform(0					,						100				) for k in metric_keys}					,						**{target_metric_key: random.choice([nan, 1_0.3_1, 1_0_0.2, 5_5.6_6_6_6, 2_2_2.2_2_2_2_2_2_2_2]				)}					,						)

      _lowerCamelCase					: Tuple		    = subprocess.run(UpperCamelCase__					,						capture_output=UpperCamelCase__					,						text=UpperCamelCase__				)

      if verbose:
            print("STDOUT"					,						result.stdout				)
            print("STDERR"					,						result.stderr				)

      # save the streams
      _lowerCamelCase					: str		    = variation.replace(" "					,						"-"				)
      with open(Path(UpperCamelCase__				) / F'log.{prefix}.stdout.txt'					,						"w"				) as f:
            f.write(result.stdout				)
      with open(Path(UpperCamelCase__				) / F'log.{prefix}.stderr.txt'					,						"w"				) as f:
            f.write(result.stderr				)

      if result.returncode != 0:
            if verbose:
                  print("failed"				)
            return {target_metric_key: nan}

      with io.open(F'{output_dir}/all_results.json'					,						"r"					,						encoding="utf-8"				) as f:
            _lowerCamelCase					: List[str]		    = json.load(UpperCamelCase__				)

      # filter out just the keys we want
      return {k: v for k, v in metrics.items() if k in metric_keys}







def 		A_							(						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     Optional[int]					,						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     Dict					,						_lowerCAmelCase :     int					,						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     Union[str, Any]					,						):



      """simple docstring"""




      _lowerCamelCase					: str		    = []
      _lowerCamelCase					: Tuple		    = []
      _lowerCamelCase					: List[Any]		    = F'{id}: {variation:<{longest_variation_len}}'
      _lowerCamelCase					: List[str]		    = F'{preamble}: '
      _lowerCamelCase					: Tuple		    = set(report_metric_keys + [target_metric_key]				)
      for i in tqdm(range(UpperCamelCase__				)					,						desc=UpperCamelCase__					,						leave=UpperCamelCase__				):
            _lowerCamelCase					: List[Any]		    = process_run_single(
                UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__				)
            _lowerCamelCase					: Tuple		    = single_run_metrics[target_metric_key]
            if not math.isnan(UpperCamelCase__				):
                  metrics.append(UpperCamelCase__				)
                  results.append(UpperCamelCase__				)
                  outcome += "✓"
            else:
                  outcome += "✘"
      _lowerCamelCase					: int		    = F'\33[2K\r{outcome}'
      if len(UpperCamelCase__				) > 0:
            _lowerCamelCase					: Union[str, Any]		    = {k: fmean([x[k] for x in metrics]				) for k in metrics[0].keys()}
            _lowerCamelCase					: str		    = round(mean_metrics[target_metric_key]					,						2				)
            _lowerCamelCase					: int		    = F'{outcome} {mean_target}'
            if len(UpperCamelCase__				) > 1:
                  results_str += F' {tuple(round(UpperCamelCase__					,						2				) for x in results				)}'
            print(UpperCamelCase__				)
            _lowerCamelCase					: str		    = variation
            return mean_metrics
      else:
            print(UpperCamelCase__				)
            return {variation_key: variation, target_metric_key: nan}







def 		A_							(						):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = torch.cuda.get_device_properties(torch.device("cuda"				)				)
      return F'\nDatetime    : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"				)}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch       : {torch.__version__}\ncuda        : {torch.version.cuda}\npython      : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n'







def 		A_							(						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Union[str, Any]					,						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     Union[str, Any]				):



      """simple docstring"""




      _lowerCamelCase					: Optional[int]		    = pd.DataFrame(UpperCamelCase__				)
      _lowerCamelCase					: int		    = """variation"""
      _lowerCamelCase					: Tuple		    = """diff_%"""

      _lowerCamelCase					: Any		    = nan
      if base_variation is not None and len(df[df[variation_key] == base_variation]				):
            # this may still return nan
            _lowerCamelCase					: Any		    = df.loc[df[variation_key] == base_variation][target_metric_key].item()
      if math.isnan(UpperCamelCase__				):
            # as a fallback, use the minimal value as the sentinel
            _lowerCamelCase					: int		    = df.loc[df[target_metric_key] != nan][target_metric_key].min()

      # create diff column if possible
      if not math.isnan(UpperCamelCase__				):
            _lowerCamelCase					: Optional[int]		    = df.apply(
                lambda _lowerCAmelCase				: round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value				)
                if not math.isnan(r[target_metric_key]				)
                else 0					,						axis="columns"					,						)

      # re-order columns
      _lowerCamelCase					: int		    = [variation_key, target_metric_key, diff_key, *report_metric_keys]
      _lowerCamelCase					: Optional[int]		    = df.reindex(UpperCamelCase__					,						axis="columns"				)  # reorder cols

      # capitalize
      _lowerCamelCase					: Any		    = df.rename(str.capitalize					,						axis="columns"				)

      # make the cols as narrow as possible
      _lowerCamelCase					: int		    = df.rename(lambda _lowerCAmelCase				: c.replace("_"					,						"<br>"				)					,						axis="columns"				)
      _lowerCamelCase					: Any		    = df.rename(lambda _lowerCAmelCase				: c.replace("_"					,						"\n"				)					,						axis="columns"				)

      _lowerCamelCase					: Union[str, Any]		    = ["""""", """Copy between the cut-here-lines and paste as is to github or a forum"""]
      report += ["----------8<-----------------8<--------"]
      report += ["*** Results:", df_github.to_markdown(index=UpperCamelCase__					,						floatfmt=".2f"				)]
      report += ["```"]
      report += ["*** Setup:", get_versions()]
      report += ["*** The benchmark command line was:", get_original_command()]
      report += ["```"]
      report += ["----------8<-----------------8<--------"]
      report += ["*** Results (console):", df_console.to_markdown(index=UpperCamelCase__					,						floatfmt=".2f"				)]

      print("\n\n".join(UpperCamelCase__				)				)







def 		A_							(						):



      """simple docstring"""




      _lowerCamelCase					: int		    = argparse.ArgumentParser()
      parser.add_argument(
          "--base-cmd"					,						default=UpperCamelCase__					,						type=UpperCamelCase__					,						required=UpperCamelCase__					,						help="Base cmd"					,						)
      parser.add_argument(
          "--variations"					,						default=UpperCamelCase__					,						type=UpperCamelCase__					,						nargs="+"					,						required=UpperCamelCase__					,						help="Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'"					,						)
      parser.add_argument(
          "--base-variation"					,						default=UpperCamelCase__					,						type=UpperCamelCase__					,						help="Baseline variation to compare to. if None the minimal target value will be used to compare against"					,						)
      parser.add_argument(
          "--target-metric-key"					,						default=UpperCamelCase__					,						type=UpperCamelCase__					,						required=UpperCamelCase__					,						help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second"					,						)
      parser.add_argument(
          "--report-metric-keys"					,						default=""					,						type=UpperCamelCase__					,						help="Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples"					,						)
      parser.add_argument(
          "--repeat-times"					,						default=1					,						type=UpperCamelCase__					,						help="How many times to re-run each variation - an average will be reported"					,						)
      parser.add_argument(
          "--output_dir"					,						default="output_benchmark"					,						type=UpperCamelCase__					,						help="The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked"					,						)
      parser.add_argument(
          "--verbose"					,						default=UpperCamelCase__					,						action="store_true"					,						help="Whether to show the outputs of each run or just the benchmark progress"					,						)
      _lowerCamelCase					: Optional[int]		    = parser.parse_args()

      _lowerCamelCase					: Union[str, Any]		    = args.output_dir
      Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
      _lowerCamelCase					: int		    = get_base_command(UpperCamelCase__					,						UpperCamelCase__				)

      # split each dimension into its --foo variations
      _lowerCamelCase					: Tuple		    = [list(map(str.strip					,						re.split(r"\|"					,						UpperCamelCase__				)				)				) for x in args.variations]
      # build a cartesian product of dimensions and convert those back into cmd-line arg strings,
      # while stripping white space for inputs that were empty
      _lowerCamelCase					: Optional[Any]		    = list(map(str.strip					,						map(" ".join					,						itertools.product(*UpperCamelCase__				)				)				)				)
      _lowerCamelCase					: Tuple		    = max(len(UpperCamelCase__				) for x in variations				)

      # split wanted keys
      _lowerCamelCase					: Dict		    = args.report_metric_keys.split()

      # capture prints into a log file for convenience
      _lowerCamelCase					: Union[str, Any]		    = F'benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S"				)}.txt'
      print(F'\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt'				)
      print(F'and this script\'s output is also piped into {report_fn}'				)

      _lowerCamelCase					: str		    = Tee(UpperCamelCase__				)

      print(F'\n*** Running {len(UpperCamelCase__				)} benchmarks:'				)
      print(F'Base command: {" ".join(UpperCamelCase__				)}'				)

      _lowerCamelCase					: List[Any]		    = """variation"""
      _lowerCamelCase					: List[Any]		    = []
      for id, variation in enumerate(tqdm(UpperCamelCase__					,						desc="Total completion: "					,						leave=UpperCamelCase__				)				):
            _lowerCamelCase					: Tuple		    = base_cmd + variation.split()
            results.append(
                process_run(
                    id + 1					,						UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__					,						args.target_metric_key					,						UpperCamelCase__					,						args.repeat_times					,						UpperCamelCase__					,						args.verbose					,						)				)

      process_results(UpperCamelCase__					,						args.target_metric_key					,						UpperCamelCase__					,						args.base_variation					,						UpperCamelCase__				)


if __name__ == "__main__":
 main()





'''simple docstring'''


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, 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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
 import torch







class      SCREAMING_SNAKE_CASE    :






      def __init__( self   :  Optional[Any]						,						lowercase__   :  Tuple						,						lowercase__   :  Any=14						,						lowercase__   :  int=7						,						lowercase__   :  int=True						,						lowercase__   :  List[str]=True						,						lowercase__   :  Tuple=False						,						lowercase__   :  Optional[Any]=True						,						lowercase__   :  Any=99						,						lowercase__   :  Dict=32						,						lowercase__   :  Tuple=4						,						lowercase__   :  List[str]=4						,						lowercase__   :  Optional[Any]=4						,						lowercase__   :  Union[str, Any]=37						,						lowercase__   :  Union[str, Any]="gelu"						,						lowercase__   :  List[Any]=0.1						,						lowercase__   :  Union[str, Any]=0.1						,						lowercase__   :  List[str]=512						,						lowercase__   :  int=0.02						,						):


           '''simple docstring'''

           a_     :							Optional[int]     						=  parent
           a_     :							str     						=  batch_size
           a_     :							Dict     						=  seq_length
           a_     :							Tuple     						=  is_training
           a_     :							Dict     						=  use_input_mask
           a_     :							str     						=  use_token_type_ids
           a_     :							Tuple     						=  use_labels
           a_     :							List[str]     						=  vocab_size
           a_     :							Union[str, Any]     						=  hidden_size
           a_     :							Optional[Any]     						=  rotary_dim
           a_     :							int     						=  num_hidden_layers
           a_     :							int     						=  num_attention_heads
           a_     :							Any     						=  intermediate_size
           a_     :							Any     						=  hidden_act
           a_     :							Tuple     						=  hidden_dropout_prob
           a_     :							Dict     						=  attention_probs_dropout_prob
           a_     :							str     						=  max_position_embeddings
           a_     :							str     						=  initializer_range
           a_     :							Optional[int]     						=  None
           a_     :							Any     						=  vocab_size - 1
           a_     :							Dict     						=  vocab_size - 1
           a_     :							Tuple     						=  vocab_size - 1






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Any     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size       )

           a_     :							str     						=  None
           if self.use_input_mask:
                a_     :							Tuple     						=  random_attention_mask([self.batch_size, self.seq_length]       )

           a_     :							Optional[Any]     						=  GPTJConfig(
               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=lowercase__						,						bos_token_id=self.bos_token_id						,						eos_token_id=self.eos_token_id						,						pad_token_id=self.pad_token_id						,						rotary_dim=self.rotary_dim						,						)

           return (config, input_ids, input_mask)






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.prepare_config_and_inputs()
           a_						,     a_						,     a_     :							Dict     						=  config_and_inputs
           a_     :							str     						=  {"""input_ids""": input_ids, """attention_mask""": attention_mask}
           return config, inputs_dict






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[str]						,						lowercase__   :  Tuple						,						lowercase__   :  List[Any]						,						lowercase__   :  Any       ):


           '''simple docstring'''

           a_     :							str     						=  20
           a_     :							Tuple     						=  model_class_name(lowercase__       )

           a_     :							Union[str, Any]     						=  model.init_cache(input_ids.shape[0]						,						lowercase__       )
           a_     :							int     						=  jnp.ones((input_ids.shape[0], max_decoder_length)						,						dtype="""i4"""       )

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

           a_     :							Dict     						=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,						dtype="""i4"""       )
           a_     :							int     						=  model(
               input_ids[:, -1:]						,						attention_mask=lowercase__						,						past_key_values=outputs_cache.past_key_values						,						position_ids=lowercase__						,						)

           a_     :							Dict     						=  model(lowercase__       )

           a_     :							Union[str, Any]     						=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
           self.parent.assertTrue(diff < 1e-3						,						msg=F"Max diff is {diff}"       )






      def 							lowercase_						( self   :  Any						,						lowercase__   :  int						,						lowercase__   :  List[Any]						,						lowercase__   :  Dict						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           a_     :							Dict     						=  20
           a_     :							str     						=  model_class_name(lowercase__       )

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

           a_     :							Optional[int]     						=  model.init_cache(input_ids.shape[0]						,						lowercase__       )
           a_     :							Tuple     						=  jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,						(input_ids.shape[0], input_ids.shape[-1] - 1)       )

           a_     :							Union[str, Any]     						=  model(
               input_ids[:, :-1]						,						attention_mask=lowercase__						,						past_key_values=lowercase__						,						position_ids=lowercase__						,						)
           a_     :							List[str]     						=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,						dtype="""i4"""       )
           a_     :							List[str]     						=  model(
               input_ids[:, -1:]						,						past_key_values=outputs_cache.past_key_values						,						attention_mask=lowercase__						,						position_ids=lowercase__						,						)

           a_     :							List[str]     						=  model(lowercase__						,						attention_mask=lowercase__       )

           a_     :							Any     						=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
           self.parent.assertTrue(diff < 1e-3						,						msg=F"Max diff is {diff}"       )







@require_flax
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      snake_case_							,      unittest.TestCase				):
      __magic_name__  :       List[str]   							=					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      __magic_name__  :       str   							=					(FlaxGPTJForCausalLM,) if is_flax_available() else ()






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							str     						=  FlaxGPTJModelTester(self       )






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                a_						,     a_						,     a_     :							List[Any]     						=  self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward(lowercase__						,						lowercase__						,						lowercase__						,						lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                a_						,     a_						,     a_     :							List[str]     						=  self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward_with_attn_mask(
                    lowercase__						,						lowercase__						,						lowercase__						,						lowercase__       )






      @tooslow
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Any     						=  GPTaTokenizer.from_pretrained("""gpt2"""						,						pad_token="""<|endoftext|>"""						,						padding_side="""left"""       )
           a_     :							Optional[Any]     						=  tokenizer(["""Hello this is a long string""", """Hey"""]						,						return_tensors="""np"""						,						padding=lowercase__						,						truncation=lowercase__       )

           a_     :							Union[str, Any]     						=  FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""       )
           a_     :							List[str]     						=  False
           a_     :							Optional[int]     						=  model.config.eos_token_id

           a_     :							List[Any]     						=  jax.jit(model.generate       )

           a_     :							Dict     						=  jit_generate(
               inputs["""input_ids"""]						,						attention_mask=inputs["""attention_mask"""]						,						pad_token_id=tokenizer.pad_token_id       ).sequences

           a_     :							Tuple     						=  tokenizer.batch_decode(lowercase__						,						skip_special_tokens=lowercase__       )

           a_     :							List[str]     						=  [
               """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
               """Hey, I'm a little late to the party. I'm going to""",
           ]

           self.assertListEqual(lowercase__						,						lowercase__       )






      @is_pt_flax_cross_test
      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_						,     a_     :							Dict     						=  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
                     a_     :							Optional[Any]     						=  self._prepare_for_class(lowercase__						,						lowercase__       )
                     a_     :							int     						=  {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

                     # load corresponding PyTorch class
                     a_     :							Optional[Any]     						=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
                     a_     :							Any     						=  getattr(lowercase__						,						lowercase__       )

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

                     a_     :							Any     						=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,						lowercase__       )
                     a_     :							Tuple     						=  fx_state

                     with torch.no_grad():
                          a_     :							Dict     						=  pt_model(**lowercase__       ).to_tuple()

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

                     with tempfile.TemporaryDirectory() as tmpdirname:
                          pt_model.save_pretrained(lowercase__       )
                          a_     :							Union[str, Any]     						=  model_class.from_pretrained(lowercase__						,						from_pt=lowercase__       )

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






      @is_pt_flax_cross_test
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_						,     a_     :							Optional[int]     						=  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
                     a_     :							List[Any]     						=  self._prepare_for_class(lowercase__						,						lowercase__       )
                     a_     :							str     						=  {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

                     a_     :							Optional[Any]     						=  pt_model_class(lowercase__       ).eval()
                     a_     :							Any     						=  model_class(lowercase__						,						dtype=jnp.floataa       )

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

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

                     with torch.no_grad():
                          a_     :							List[str]     						=  pt_model(**lowercase__       ).to_tuple()

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

                     with tempfile.TemporaryDirectory() as tmpdirname:
                          fx_model.save_pretrained(lowercase__       )
                          a_     :							str     						=  pt_model_class.from_pretrained(lowercase__						,						from_flax=lowercase__       )

                     with torch.no_grad():
                          a_     :							Tuple     						=  pt_model_loaded(**lowercase__       ).to_tuple()

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






      @tooslow
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                a_     :							Union[str, Any]     						=  model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""       )
                a_     :							str     						=  model(np.ones((1, 1)       )       )
                self.assertIsNotNone(lowercase__       )
