'''simple docstring'''

import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__a: Dict          =   logging.get_logger(__name__)
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    int  =					r'''\w+[.]\d+'''
 lowercase__				:    Tuple  =					re.findall(UpperCAmelCase		,     UpperCAmelCase     )
 for pat in pats:
  lowercase__				:    Optional[Any]  =					key.replace(UpperCAmelCase		,     '''_'''.join(pat.split('''.'''     )     )     )
 return key
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Optional[Any]  =					pt_tuple_key[:-1] + ('''scale''',)
 if (
     any('''norm''' in str_ for str_ in pt_tuple_key     )
     and (pt_tuple_key[-1] == "bias")
     and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
     and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
 ):
  lowercase__				:    Union[str, Any]  =					pt_tuple_key[:-1] + ('''scale''',)
  return renamed_pt_tuple_key, pt_tensor
 elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
  lowercase__				:    Optional[Any]  =					pt_tuple_key[:-1] + ('''scale''',)
  return renamed_pt_tuple_key, pt_tensor

 # embedding
 if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
  lowercase__				:    Optional[Any]  =					pt_tuple_key[:-1] + ('''embedding''',)
  return renamed_pt_tuple_key, pt_tensor

 # conv layer
 lowercase__				:    int  =					pt_tuple_key[:-1] + ('''kernel''',)
 if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
  lowercase__				:    Union[str, Any]  =					pt_tensor.transpose(2		,     3		,     1		,     0     )
  return renamed_pt_tuple_key, pt_tensor

 # linear layer
 lowercase__				:    Optional[Any]  =					pt_tuple_key[:-1] + ('''kernel''',)
 if pt_tuple_key[-1] == "weight":
  lowercase__				:    Tuple  =					pt_tensor.T
  return renamed_pt_tuple_key, pt_tensor

 # old PyTorch layer norm weight
 lowercase__				:    Union[str, Any]  =					pt_tuple_key[:-1] + ('''weight''',)
 if pt_tuple_key[-1] == "gamma":
  return renamed_pt_tuple_key, pt_tensor

 # old PyTorch layer norm bias
 lowercase__				:    Optional[Any]  =					pt_tuple_key[:-1] + ('''bias''',)
 if pt_tuple_key[-1] == "beta":
  return renamed_pt_tuple_key, pt_tensor

 return pt_tuple_key, pt_tensor





def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=42     ):
 # Step 1: Convert pytorch tensor to numpy
 lowercase__				:    int  =					{k: v.numpy() for k, v in pt_state_dict.items()}

 # Step 2: Since the model is stateless, get random Flax params
 lowercase__				:    Optional[int]  =					flax_model.init_weights(PRNGKey(UpperCAmelCase     )     )

 lowercase__				:    List[str]  =					flatten_dict(UpperCAmelCase     )
 lowercase__				:    Any  =					{}

 # Need to change some parameters name to match Flax names
 for pt_key, pt_tensor in pt_state_dict.items():
  lowercase__				:    List[Any]  =					rename_key(UpperCAmelCase     )
  lowercase__				:    List[Any]  =					tuple(renamed_pt_key.split('''.'''     )     )

  # Correctly rename weight parameters
  lowercase__ ,							lowercase__				:    List[Any]  =					rename_key_and_reshape_tensor(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )

  if flax_key in random_flax_state_dict:
   if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
    raise ValueError(
        F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
        F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""     )

        # also add unexpected weight so that warning is thrown
  lowercase__				:    List[Any]  =					jnp.asarray(UpperCAmelCase     )

 return unflatten_dict(UpperCAmelCase     )


'''simple docstring'''

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    BlenderbotSmallTokenizer
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        super().setUp()

        lowercase__				:    List[Any]  =					['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
        lowercase__				:    Any  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )

        lowercase__				:    Optional[Any]  =					['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
        lowercase__				:    Dict  =					{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

        lowercase__				:    List[str]  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Any  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  Tuple:
        kwargs.update(self.special_tokens_map )
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  int:
        lowercase__				:    str  =					'''adapt act apte'''
        lowercase__				:    Any  =					'''adapt act apte'''
        return input_text, output_text

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Optional[int]  =					BlenderbotSmallTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Tuple  =					'''adapt act apte'''
        lowercase__				:    Dict  =					['''adapt''', '''act''', '''ap@@''', '''te''']
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    Any  =					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

        lowercase__				:    int  =					[0, 1, 2, 3, 4, 5]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    int  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        assert tok('''sam''' ).input_ids == [1384]
        lowercase__				:    str  =					'''I am a small frog.'''
        lowercase__				:    Union[str, Any]  =					tok([src_text]	,   padding=__lowerCAmelCase	,   truncation=__lowerCAmelCase )['''input_ids''']
        lowercase__				:    List[str]  =					tok.batch_decode(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase )[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Optional[Any]  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        lowercase__				:    Optional[Any]  =					'''I am a small frog .'''
        lowercase__				:    Any  =					'''.'''
        lowercase__				:    List[Any]  =					tok(__lowerCAmelCase )['''input_ids''']
        lowercase__				:    Optional[Any]  =					tok(__lowerCAmelCase )['''input_ids''']

        assert encoded[-1] == encoded_dot[0]

