


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

A      :    Tuple		=					logging.get_logger(__name__)


A      :    Union[str, Any]		=					{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

A      :    Optional[Any]		=					{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

A      :    List[Any]		=					{"facebook/blenderbot-3B": 128}

@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  (
          list(range(ord("!"						)    ,					ord("~"						) + 1						)						) + list(range(ord("¡"						)    ,					ord("¬"						) + 1						)						) + list(range(ord("®"						)    ,					ord("ÿ"						) + 1						)						)
      )
      lowerCamelCase__		:				Optional[int]    =  bs[:]
      lowerCamelCase__		:				str    =  0
      for b in range(2**8						):
            if b not in bs:
                  bs.append(_A						)
                  cs.append(2**8 + n						)
                  n += 1
      lowerCamelCase__		:				Tuple    =  [chr(_A						) for n in cs]
      return dict(zip(_A    ,					_A						)						)

def       lowercase_		(		_A			:    Optional[int]						):

      """simple docstring"""




      lowerCamelCase__		:				Any    =  set()
      lowerCamelCase__		:				Tuple    =  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)						)
            lowerCamelCase__		:				Any    =  char
      return pairs







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			VOCAB_FILES_NAMES
       A__											=			PRETRAINED_VOCAB_FILES_MAP
       A__											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A__											=			["input_ids", "attention_mask"]





       def __init__(   self							:			Union[str, Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Tuple="replace"					,      __lowerCamelCase							:			Optional[int]="<s>"					,      __lowerCamelCase							:			int="</s>"					,      __lowerCamelCase							:			int="</s>"					,      __lowerCamelCase							:			int="<s>"					,      __lowerCamelCase							:			Dict="<unk>"					,      __lowerCamelCase							:			Optional[int]="<pad>"					,      __lowerCamelCase							:			Optional[Any]="<mask>"					,      __lowerCamelCase							:			Tuple=False					,      **__lowerCamelCase							:			int					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase       ) if isinstance(__lowerCamelCase					,      __lowerCamelCase       ) else bos_token
             lowerCamelCase__		:				Optional[int]    =  AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase       ) if isinstance(__lowerCamelCase					,      __lowerCamelCase       ) else eos_token
             lowerCamelCase__		:				List[str]    =  AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase       ) if isinstance(__lowerCamelCase					,      __lowerCamelCase       ) else sep_token
             lowerCamelCase__		:				List[Any]    =  AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase       ) if isinstance(__lowerCamelCase					,      __lowerCamelCase       ) else cls_token
             lowerCamelCase__		:				Dict    =  AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase       ) if isinstance(__lowerCamelCase					,      __lowerCamelCase       ) else unk_token
             lowerCamelCase__		:				Optional[int]    =  AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase       ) if isinstance(__lowerCamelCase					,      __lowerCamelCase       ) else pad_token

             # Mask token behave like a normal word, i.e. include the space before it
             lowerCamelCase__		:				Optional[Any]    =  AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase       ) if isinstance(__lowerCamelCase					,      __lowerCamelCase       ) else mask_token

             super().__init__(
                 errors=__lowerCamelCase					,      bos_token=__lowerCamelCase					,      eos_token=__lowerCamelCase					,      unk_token=__lowerCamelCase					,      sep_token=__lowerCamelCase					,      cls_token=__lowerCamelCase					,      pad_token=__lowerCamelCase					,      mask_token=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      **__lowerCamelCase					,      )

             with open(__lowerCamelCase					,      encoding="utf-8"       ) as vocab_handle:
                   lowerCamelCase__		:				Dict    =  json.load(__lowerCamelCase       )
             lowerCamelCase__		:				List[Any]    =  {v: k for k, v in self.encoder.items()}
             lowerCamelCase__		:				Optional[Any]    =  errors  # how to handle errors in decoding
             lowerCamelCase__		:				Dict    =  bytes_to_unicode()
             lowerCamelCase__		:				int    =  {v: k for k, v in self.byte_encoder.items()}
             with open(__lowerCamelCase					,      encoding="utf-8"       ) as merges_handle:
                   lowerCamelCase__		:				List[Any]    =  merges_handle.read().split("\n"       )[1:-1]
             lowerCamelCase__		:				Optional[Any]    =  [tuple(merge.split()       ) for merge in bpe_merges]
             lowerCamelCase__		:				Tuple    =  dict(zip(__lowerCamelCase					,      range(len(__lowerCamelCase       )       )       )       )
             lowerCamelCase__		:				Tuple    =  {}
             lowerCamelCase__		:				Tuple    =  add_prefix_space

             # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
             lowerCamelCase__		:				str    =  re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"       )





       @property
       # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             return len(self.encoder       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             return dict(self.encoder					,      **self.added_tokens_encoder       )





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			Optional[int]       ):


             '''simple docstring'''



             if token in self.cache:
                   return self.cache[token]
             lowerCamelCase__		:				Any    =  tuple(__lowerCamelCase       )
             lowerCamelCase__		:				List[Any]    =  get_pairs(__lowerCamelCase       )

             if not pairs:
                   return token

             while True:
                   lowerCamelCase__		:				Union[str, Any]    =  min(__lowerCamelCase					,      key=lambda __lowerCamelCase       : self.bpe_ranks.get(__lowerCamelCase					,      float("inf"       )       )       )
                   if bigram not in self.bpe_ranks:
                         break
                   lowerCamelCase__    ,   lowerCamelCase__		:				str    =  bigram
                   lowerCamelCase__		:				int    =  []
                   lowerCamelCase__		:				Optional[Any]    =  0
                   while i < len(__lowerCamelCase       ):
                         try:
                               lowerCamelCase__		:				Optional[Any]    =  word.index(__lowerCamelCase					,      __lowerCamelCase       )
                         except ValueError:
                               new_word.extend(word[i:]       )
                               break
                         else:
                               new_word.extend(word[i:j]       )
                               lowerCamelCase__		:				Dict    =  j

                         if word[i] == first and i < len(__lowerCamelCase       ) - 1 and word[i + 1] == second:
                               new_word.append(first + second       )
                               i += 2
                         else:
                               new_word.append(word[i]       )
                               i += 1
                   lowerCamelCase__		:				Union[str, Any]    =  tuple(__lowerCamelCase       )
                   lowerCamelCase__		:				Union[str, Any]    =  new_word
                   if len(__lowerCamelCase       ) == 1:
                         break
                   else:
                         lowerCamelCase__		:				Any    =  get_pairs(__lowerCamelCase       )
             lowerCamelCase__		:				Optional[int]    =  " ".join(__lowerCamelCase       )
             lowerCamelCase__		:				List[str]    =  word
             return word





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  []
             for token in re.findall(self.pat					,      __lowerCamelCase       ):
                   lowerCamelCase__		:				str    =  "".join(
                       self.byte_encoder[b] for b in token.encode("utf-8"       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                   bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase       ).split(" "       )       )
             return bpe_tokens





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             return self.encoder.get(__lowerCamelCase					,      self.encoder.get(self.unk_token       )       )





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			List[Any]       ):


             '''simple docstring'''



             return self.decoder.get(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  "".join(__lowerCamelCase       )
             lowerCamelCase__		:				Union[str, Any]    =  bytearray([self.byte_decoder[c] for c in text]       ).decode("utf-8"					,      errors=self.errors       )
             return text





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[str] = None       ):


             '''simple docstring'''



             if not os.path.isdir(__lowerCamelCase       ):
                   logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
                   return
             lowerCamelCase__		:				Tuple    =  os.path.join(
                 __lowerCamelCase					,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
             lowerCamelCase__		:				Tuple    =  os.path.join(
                 __lowerCamelCase					,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

             with open(__lowerCamelCase					,      "w"					,      encoding="utf-8"       ) as f:
                   f.write(json.dumps(self.encoder					,      indent=2					,      sort_keys=__lowerCamelCase					,      ensure_ascii=__lowerCamelCase       ) + "\n"       )

             lowerCamelCase__		:				str    =  0
             with open(__lowerCamelCase					,      "w"					,      encoding="utf-8"       ) as writer:
                   writer.write("#version: 0.2\n"       )
                   for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,      key=lambda __lowerCamelCase       : kv[1]       ):
                         if index != token_index:
                               logger.warning(
                                   f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                                   " Please check that the tokenizer is not corrupted!"       )
                               lowerCamelCase__		:				Optional[Any]    =  token_index
                         writer.write(" ".join(__lowerCamelCase       ) + "\n"       )
                         index += 1

             return vocab_file, merge_file





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			List[int]					,      __lowerCamelCase							:			Optional[List[int]] = None					,      __lowerCamelCase							:			bool = False       ):


             '''simple docstring'''



             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=__lowerCamelCase					,      token_ids_a=__lowerCamelCase					,      already_has_special_tokens=__lowerCamelCase       )

             if token_ids_a is None:
                   return [1] + ([0] * len(__lowerCamelCase       )) + [1]
             return [1] + ([0] * len(__lowerCamelCase       )) + [1, 1] + ([0] * len(__lowerCamelCase       )) + [1]





       def 						lowerCAmelCase     (   self							:			str					,      __lowerCamelCase							:			List[int]					,      __lowerCamelCase							:			Optional[List[int]] = None       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  [self.sep_token_id]
             lowerCamelCase__		:				Optional[int]    =  [self.cls_token_id]

             if token_ids_a is None:
                   return len(cls + token_ids_a + sep       ) * [0]
             return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			List[str]=False					,      **__lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  kwargs.pop("add_prefix_space"					,      self.add_prefix_space       )
             if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase       ) > 0 and not text[0].isspace()):
                   lowerCamelCase__		:				List[str]    =  " " + text
             return (text, kwargs)





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			List[int]					,      __lowerCamelCase							:			Optional[List[int]] = None       ):


             '''simple docstring'''



             return token_ids_a + [self.eos_token_id]





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			"Conversation"       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  []
             for is_user, text in conversation.iter_texts():
                   if is_user:
                         # We need to space prefix as it's being done within blenderbot
                         inputs.append(" " + text       )
                   else:
                         # Generated responses should contain them already.
                         inputs.append(__lowerCamelCase       )

             lowerCamelCase__		:				List[str]    =  "  ".join(__lowerCamelCase       )
             lowerCamelCase__		:				Union[str, Any]    =  self.encode(__lowerCamelCase       )
             if len(__lowerCamelCase       ) > self.model_max_length:
                   lowerCamelCase__		:				Any    =  input_ids[-self.model_max_length :]
                   logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."       )
             return input_ids






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 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Union[str, Any]=14					,      __lowerCamelCase							:			str=7					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			Optional[int]=False					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			List[Any]=99					,      __lowerCamelCase							:			Optional[Any]=32					,      __lowerCamelCase							:			List[Any]=4					,      __lowerCamelCase							:			List[Any]=4					,      __lowerCamelCase							:			List[str]=4					,      __lowerCamelCase							:			List[Any]=37					,      __lowerCamelCase							:			str="gelu"					,      __lowerCamelCase							:			Tuple=0.1					,      __lowerCamelCase							:			Any=0.1					,      __lowerCamelCase							:			Any=512					,      __lowerCamelCase							:			Dict=0.0_2					,      ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size       )

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

             lowerCamelCase__		:				Dict    =  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=__lowerCamelCase					,      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 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  self.prepare_config_and_inputs()
             lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  config_and_inputs
             lowerCamelCase__		:				Tuple    =  {"input_ids": input_ids, "attention_mask": attention_mask}
             return config, inputs_dict





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  20
             lowerCamelCase__		:				Tuple    =  model_class_name(__lowerCamelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  model.init_cache(input_ids.shape[0]					,      __lowerCamelCase       )
             lowerCamelCase__		:				List[str]    =  jnp.ones((input_ids.shape[0], max_decoder_length)					,      dtype="i4"       )

             lowerCamelCase__		:				str    =  jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1       )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)       )
             lowerCamelCase__		:				Tuple    =  model(
                 input_ids[:, :-1]					,      attention_mask=__lowerCamelCase					,      past_key_values=__lowerCamelCase					,      position_ids=__lowerCamelCase					,      )

             lowerCamelCase__		:				Tuple    =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype="i4"       )
             lowerCamelCase__		:				str    =  model(
                 input_ids[:, -1:]					,      attention_mask=__lowerCamelCase					,      past_key_values=outputs_cache.past_key_values					,      position_ids=__lowerCamelCase					,      )

             lowerCamelCase__		:				List[Any]    =  model(__lowerCamelCase       )

             lowerCamelCase__		:				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 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  20
             lowerCamelCase__		:				int    =  model_class_name(__lowerCamelCase       )

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

             lowerCamelCase__		:				Dict    =  model.init_cache(input_ids.shape[0]					,      __lowerCamelCase       )
             lowerCamelCase__		:				Any    =  jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1       )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)       )

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

             lowerCamelCase__		:				Tuple    =  model(__lowerCamelCase					,      attention_mask=__lowerCamelCase       )

             lowerCamelCase__		:				Dict    =  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 			_lowercase (							lowercase__							,  lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
       A__											=			(FlaxGPTJForCausalLM,) if is_flax_available() else ()





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  FlaxGPTJModelTester(self       )





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             for model_class_name in self.all_model_classes:
                   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  self.model_tester.prepare_config_and_inputs()
                   self.model_tester.check_use_cache_forward(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             for model_class_name in self.all_model_classes:
                   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  self.model_tester.prepare_config_and_inputs()
                   self.model_tester.check_use_cache_forward_with_attn_mask(
                       __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )





       @tooslow
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



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

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

             lowerCamelCase__		:				int    =  jax.jit(model.generate       )

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

             lowerCamelCase__		:				Tuple    =  tokenizer.batch_decode(__lowerCamelCase					,      skip_special_tokens=__lowerCamelCase       )

             lowerCamelCase__		:				Any    =  [
                 "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(__lowerCamelCase					,      __lowerCamelCase       )





       @is_pt_flax_cross_test
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



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

                         # load corresponding PyTorch class
                         lowerCamelCase__		:				List[str]    =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
                         lowerCamelCase__		:				int    =  getattr(__lowerCamelCase					,      __lowerCamelCase       )

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

                         lowerCamelCase__		:				Optional[int]    =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,      __lowerCamelCase       )
                         lowerCamelCase__		:				Optional[int]    =  fx_state

                         with torch.no_grad():
                               lowerCamelCase__		:				List[Any]    =  pt_model(**__lowerCamelCase       ).to_tuple()

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

                         with tempfile.TemporaryDirectory() as tmpdirname:
                               pt_model.save_pretrained(__lowerCamelCase       )
                               lowerCamelCase__		:				List[Any]    =  model_class.from_pretrained(__lowerCamelCase					,      from_pt=__lowerCamelCase       )

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





       @is_pt_flax_cross_test
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  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
                         lowerCamelCase__		:				Union[str, Any]    =  self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase       )
                         lowerCamelCase__		:				Union[str, Any]    =  {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

                         lowerCamelCase__		:				Optional[Any]    =  pt_model_class(__lowerCamelCase       ).eval()
                         lowerCamelCase__		:				Tuple    =  model_class(__lowerCamelCase					,      dtype=jnp.floataa       )

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

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

                         with torch.no_grad():
                               lowerCamelCase__		:				Dict    =  pt_model(**__lowerCamelCase       ).to_tuple()

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

                         with tempfile.TemporaryDirectory() as tmpdirname:
                               fx_model.save_pretrained(__lowerCamelCase       )
                               lowerCamelCase__		:				Any    =  pt_model_class.from_pretrained(__lowerCamelCase					,      from_flax=__lowerCamelCase       )

                         with torch.no_grad():
                               lowerCamelCase__		:				Optional[int]    =  pt_model_loaded(**__lowerCamelCase       ).to_tuple()

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





       @tooslow
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



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


