import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


lowerCAmelCase__							= get_logger(__name__)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__ = None	,  ):
       # Model states
       for i, state in enumerate(lowerCAmelCase__	):
              lowerCAmelCase__     	=					F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
              lowerCAmelCase__     	=					os.path.join(lowerCAmelCase__	,  lowerCAmelCase__	)
              save(lowerCAmelCase__	,  lowerCAmelCase__	)
              logger.info(F"""Model weights saved in {output_model_file}"""	)
       # Optimizer states
       for i, opt in enumerate(lowerCAmelCase__	):
              lowerCAmelCase__     	=					opt.state_dict()
              lowerCAmelCase__     	=					F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
              lowerCAmelCase__     	=					os.path.join(lowerCAmelCase__	,  lowerCAmelCase__	)
              save(lowerCAmelCase__	,  lowerCAmelCase__	)
              logger.info(F"""Optimizer state saved in {output_optimizer_file}"""	)
       # Scheduler states
       for i, scheduler in enumerate(lowerCAmelCase__	):
              lowerCAmelCase__     	=					scheduler.state_dict()
              lowerCAmelCase__     	=					F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
              lowerCAmelCase__     	=					os.path.join(lowerCAmelCase__	,  lowerCAmelCase__	)
              save(lowerCAmelCase__	,  lowerCAmelCase__	)
              logger.info(F"""Scheduler state saved in {output_scheduler_file}"""	)
       # GradScaler state
       if scaler is not None:
              lowerCAmelCase__     	=					scaler.state_dict()
              lowerCAmelCase__     	=					os.path.join(lowerCAmelCase__	,  lowerCAmelCase__	)
              torch.save(lowerCAmelCase__	,  lowerCAmelCase__	)
              logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""	)
       # Random number generator states
       lowerCAmelCase__     	=					{}
       lowerCAmelCase__     	=					F"""{RNG_STATE_NAME}_{process_index}.pkl"""
       lowerCAmelCase__     	=					random.getstate()
       lowerCAmelCase__     	=					np.random.get_state()
       lowerCAmelCase__     	=					torch.get_rng_state()
       if is_xpu_available():
              lowerCAmelCase__     	=					torch.xpu.get_rng_state_all()
       else:
              lowerCAmelCase__     	=					torch.cuda.get_rng_state_all()
       if is_tpu_available():
              lowerCAmelCase__     	=					xm.get_rng_state()
       lowerCAmelCase__     	=					os.path.join(lowerCAmelCase__	,  lowerCAmelCase__	)
       torch.save(lowerCAmelCase__	,  lowerCAmelCase__	)
       logger.info(F"""Random states saved in {output_states_file}"""	)
       return output_dir






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__=None	,  lowerCAmelCase__=None	,  **lowerCAmelCase__	,  ):
       if map_location not in [None, "cpu", "on_device"]:
              raise TypeError(
                  'Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'	)
       if map_location is None:
              lowerCAmelCase__     	=					'cpu'
       elif map_location == "on_device":
              lowerCAmelCase__     	=					PartialState().device
       # Model states
       for i, model in enumerate(lowerCAmelCase__	):
              lowerCAmelCase__     	=					F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
              lowerCAmelCase__     	=					os.path.join(lowerCAmelCase__	,  lowerCAmelCase__	)
              models[i].load_state_dict(torch.load(lowerCAmelCase__	,  map_location=lowerCAmelCase__	)	,  **lowerCAmelCase__	)
       logger.info('All model weights loaded successfully'	)

       # Optimizer states
       for i, opt in enumerate(lowerCAmelCase__	):
              lowerCAmelCase__     	=					F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
              lowerCAmelCase__     	=					os.path.join(lowerCAmelCase__	,  lowerCAmelCase__	)
              lowerCAmelCase__     	=					torch.load(lowerCAmelCase__	,  map_location=lowerCAmelCase__	)
              optimizers[i].load_state_dict(lowerCAmelCase__	)
       logger.info('All optimizer states loaded successfully'	)

       # Scheduler states
       for i, scheduler in enumerate(lowerCAmelCase__	):
              lowerCAmelCase__     	=					F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
              lowerCAmelCase__     	=					os.path.join(lowerCAmelCase__	,  lowerCAmelCase__	)
              scheduler.load_state_dict(torch.load(lowerCAmelCase__	)	)
       logger.info('All scheduler states loaded successfully'	)

       # GradScaler state
       if scaler is not None:
              lowerCAmelCase__     	=					os.path.join(lowerCAmelCase__	,  lowerCAmelCase__	)
              scaler.load_state_dict(torch.load(lowerCAmelCase__	)	)
              logger.info('GradScaler state loaded successfully'	)

       # Random states
       try:
              lowerCAmelCase__     	=					torch.load(os.path.join(lowerCAmelCase__	,  F"""{RNG_STATE_NAME}_{process_index}.pkl"""	)	)
              random.setstate(states['random_state']	)
              np.random.set_state(states['numpy_random_seed']	)
              torch.set_rng_state(states['torch_manual_seed']	)
              if is_xpu_available():
                     torch.xpu.set_rng_state_all(states['torch_xpu_manual_seed']	)
              else:
                     torch.cuda.set_rng_state_all(states['torch_cuda_manual_seed']	)
              if is_tpu_available():
                     xm.set_rng_state(states['xm_seed']	)
              logger.info('All random states loaded successfully'	)
       except Exception:
              logger.info('Could not load random states'	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__ = 0	):
       lowerCAmelCase__     	=					Path(lowerCAmelCase__	) / F"""custom_checkpoint_{index}.pkl"""
       logger.info(F"""Saving the state of {get_pretty_name(lowerCAmelCase__	)} to {save_location}"""	)
       torch.save(obj.state_dict()	,  lowerCAmelCase__	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__ = 0	):
       lowerCAmelCase__     	=					F"""{path}/custom_checkpoint_{index}.pkl"""
       logger.info(F"""Loading the state of {get_pretty_name(lowerCAmelCase__	)} from {load_location}"""	)
       obj.load_state_dict(torch.load(lowerCAmelCase__	,  map_location='cpu'	)	)

from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


lowerCAmelCase__							= logging.get_logger(__name__)

lowerCAmelCase__							= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__							= {
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase__							= {
    'openai/clip-vit-base-patch32': 77,
}
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					VOCAB_FILES_NAMES
 UpperCAmelCase_	 =					PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_	 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_	 =					['input_ids', 'attention_mask']
 UpperCAmelCase_	 =					CLIPTokenizer




 def __init__(			self					:  Optional[int]			,    lowercase__					:  Optional[int]=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  str="<|endoftext|>"			,    lowercase__					:  List[str]="<|startoftext|>"			,    lowercase__					:  Union[str, Any]="<|endoftext|>"			,    lowercase__					:  Any="<|endoftext|>"			,    **lowercase__					:  Any			,    ):







        '''simple docstring'''



        super().__init__(
            lowercase__			,    lowercase__			,    tokenizer_file=lowercase__			,    unk_token=lowercase__			,    bos_token=lowercase__			,    eos_token=lowercase__			,    pad_token=lowercase__			,    **lowercase__			,    )

        if not isinstance(self.backend_tokenizer.pre_tokenizer			,    pre_tokenizers.Sequence):
               raise ValueError(
                   'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
                   ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
                   ' to be compatible with this version.The easiest way to do so is'
                   ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
                   ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
                   ' transformers.')

        self._wrap_decode_method_backend_tokenizer()




 def 		__snake_case						(			self					:  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.backend_tokenizer.decode

        def new_decode_method(*lowercase__					:  str			,    **lowercase__					:  int):
               lowerCAmelCase__     	=					orig_decode_method(*lowercase__			,    **lowercase__)
               lowerCAmelCase__     	=					text.replace(self.backend_tokenizer.model.end_of_word_suffix			,    ' ').strip()
               return text

        lowerCAmelCase__     	=					new_decode_method




 def 		__snake_case						(			self					:  Tuple			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id]
        lowerCAmelCase__     	=					[self.eos_token_id]

        if token_ids_a is None:
               return bos_token + token_ids_a + eos_token
        return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token




 def 		__snake_case						(			self					:  Any			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id]
        lowerCAmelCase__     	=					[self.eos_token_id]

        if token_ids_a is None:
               return len(bos_token + token_ids_a + eos_token) * [0]
        return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token) * [0]




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  str			,    lowercase__					:  Optional[str] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					self._tokenizer.model.save(lowercase__			,    name=lowercase__)
        return tuple(lowercase__)
