
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
     import transformers





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    random.seed(__lowerCAmelCase							)
    np.random.seed(__lowerCAmelCase							)
    torch.manual_seed(__lowerCAmelCase							)
    torch.cuda.manual_seed_all(__lowerCAmelCase							)
    # ^^ safe to call this function even if cuda is not available





class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase = 0.9_9_9_9		,	lowerCamelCase = 0.0		,	lowerCamelCase = 0		,	lowerCamelCase = False		,	lowerCamelCase = 1.0		,	lowerCamelCase = 2 / 3		,	lowerCamelCase = None		,	lowerCamelCase = None		,	**lowerCamelCase		,	):

          if isinstance(lowerCamelCase		,	torch.nn.Module ):
              snake_case__						=				(
                  "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
                  "Please pass the parameters of the module instead."
              )
              deprecate(
                  "passing a `torch.nn.Module` to `ExponentialMovingAverage`"		,	"1.0.0"		,	lowerCamelCase		,	standard_warn=lowerCamelCase		,	)
              snake_case__						=				parameters.parameters()

              # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
              snake_case__						=				True

          if kwargs.get("max_value"		,	lowerCamelCase ) is not None:
              snake_case__						=				"The `max_value` argument is deprecated. Please use `decay` instead."
              deprecate("max_value"		,	"1.0.0"		,	lowerCamelCase		,	standard_warn=lowerCamelCase )
              snake_case__						=				kwargs["max_value"]

          if kwargs.get("min_value"		,	lowerCamelCase ) is not None:
              snake_case__						=				"The `min_value` argument is deprecated. Please use `min_decay` instead."
              deprecate("min_value"		,	"1.0.0"		,	lowerCamelCase		,	standard_warn=lowerCamelCase )
              snake_case__						=				kwargs["min_value"]

          snake_case__						=				list(lowerCamelCase )
          snake_case__						=				[p.clone().detach() for p in parameters]

          if kwargs.get("device"		,	lowerCamelCase ) is not None:
              snake_case__						=				"The `device` argument is deprecated. Please use `to` instead."
              deprecate("device"		,	"1.0.0"		,	lowerCamelCase		,	standard_warn=lowerCamelCase )
              self.to(device=kwargs["device"] )

          snake_case__						=				None

          snake_case__						=				decay
          snake_case__						=				min_decay
          snake_case__						=				update_after_step
          snake_case__						=				use_ema_warmup
          snake_case__						=				inv_gamma
          snake_case__						=				power
          snake_case__						=				0
          snake_case__						=				None  # set in `step()`

          snake_case__						=				model_cls
          snake_case__						=				model_config
      @classmethod
      def A_			(			cls		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						,					snake_case__						=				model_cls.load_config(lowerCamelCase		,	return_unused_kwargs=lowerCamelCase )
          snake_case__						=				model_cls.from_pretrained(lowerCamelCase )

          snake_case__						=				cls(model.parameters()		,	model_cls=lowerCamelCase		,	model_config=model.config )

          ema_model.load_state_dict(lowerCamelCase )
          return ema_model
      def A_			(			self		,	lowerCamelCase ):
          if self.model_cls is None:
              raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__." )

          if self.model_config is None:
              raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__." )

          snake_case__						=				self.model_cls.from_config(self.model_config )
          snake_case__						=				self.state_dict()
          state_dict.pop("shadow_params"		,	lowerCamelCase )

          model.register_to_config(**lowerCamelCase )
          self.copy_to(model.parameters() )
          model.save_pretrained(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				max(0		,	optimization_step - self.update_after_step - 1 )

          if step <= 0:
              return 0.0

          if self.use_ema_warmup:
              snake_case__						=				1 - (1 + step / self.inv_gamma) ** -self.power
          else:
              snake_case__						=				(1 + step) / (10 + step)

          snake_case__						=				min(lowerCamelCase		,	self.decay )
          # make sure decay is not smaller than min_decay
          snake_case__						=				max(lowerCamelCase		,	self.min_decay )
          return cur_decay_value
      @torch.no_grad()
      def A_			(			self		,	lowerCamelCase ):
          if isinstance(lowerCamelCase		,	torch.nn.Module ):
              snake_case__						=				(
                  "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
                  "Please pass the parameters of the module instead."
              )
              deprecate(
                  "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"		,	"1.0.0"		,	lowerCamelCase		,	standard_warn=lowerCamelCase		,	)
              snake_case__						=				parameters.parameters()

          snake_case__						=				list(lowerCamelCase )

          self.optimization_step += 1

          # Compute the decay factor for the exponential moving average.
          snake_case__						=				self.get_decay(self.optimization_step )
          snake_case__						=				decay
          snake_case__						=				1 - decay

          snake_case__						=				contextlib.nullcontext
          if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
              import deepspeed

          for s_param, param in zip(self.shadow_params		,	lowerCamelCase ):
              if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                  snake_case__						=				deepspeed.zero.GatheredParameters(lowerCamelCase		,	modifier_rank=lowerCamelCase )

              with context_manager():
                  if param.requires_grad:
                      s_param.sub_(one_minus_decay * (s_param - param) )
                  else:
                      s_param.copy_(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				list(lowerCamelCase )
          for s_param, param in zip(self.shadow_params		,	lowerCamelCase ):
              param.data.copy_(s_param.to(param.device ).data )
      def A_			(			self		,	lowerCamelCase=None		,	lowerCamelCase=None ):
          snake_case__						=				[
              p.to(device=lowerCamelCase		,	dtype=lowerCamelCase ) if p.is_floating_point() else p.to(device=lowerCamelCase )
              for p in self.shadow_params
          ]
      def A_			(			self ):
          return {
              "decay": self.decay,
              "min_decay": self.min_decay,
              "optimization_step": self.optimization_step,
              "update_after_step": self.update_after_step,
              "use_ema_warmup": self.use_ema_warmup,
              "inv_gamma": self.inv_gamma,
              "power": self.power,
              "shadow_params": self.shadow_params,
          }
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				[param.detach().cpu().clone() for param in parameters]
      def A_			(			self		,	lowerCamelCase ):
          if self.temp_stored_params is None:
              raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`" )
          for c_param, param in zip(self.temp_stored_params		,	lowerCamelCase ):
              param.data.copy_(c_param.data )

          # Better memory-wise.
          snake_case__						=				None



      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				copy.deepcopy(lowerCamelCase )

          snake_case__						=				state_dict.get("decay"		,	self.decay )
          if self.decay < 0.0 or self.decay > 1.0:
              raise ValueError("Decay must be between 0 and 1" )

          snake_case__						=				state_dict.get("min_decay"		,	self.min_decay )
          if not isinstance(self.min_decay		,	lowerCamelCase ):
              raise ValueError("Invalid min_decay" )

          snake_case__						=				state_dict.get("optimization_step"		,	self.optimization_step )
          if not isinstance(self.optimization_step		,	lowerCamelCase ):
              raise ValueError("Invalid optimization_step" )

          snake_case__						=				state_dict.get("update_after_step"		,	self.update_after_step )
          if not isinstance(self.update_after_step		,	lowerCamelCase ):
              raise ValueError("Invalid update_after_step" )

          snake_case__						=				state_dict.get("use_ema_warmup"		,	self.use_ema_warmup )
          if not isinstance(self.use_ema_warmup		,	lowerCamelCase ):
              raise ValueError("Invalid use_ema_warmup" )

          snake_case__						=				state_dict.get("inv_gamma"		,	self.inv_gamma )
          if not isinstance(self.inv_gamma		,	(float, int) ):
              raise ValueError("Invalid inv_gamma" )

          snake_case__						=				state_dict.get("power"		,	self.power )
          if not isinstance(self.power		,	(float, int) ):
              raise ValueError("Invalid power" )

          snake_case__						=				state_dict.get("shadow_params"		,	lowerCamelCase )
          if shadow_params is not None:
              snake_case__						=				shadow_params
              if not isinstance(self.shadow_params		,	lowerCamelCase ):
                  raise ValueError("shadow_params must be a list" )
              if not all(isinstance(lowerCamelCase		,	torch.Tensor ) for p in self.shadow_params ):
                  raise ValueError("shadow_params must all be Tensors" )





import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow




class 					_SCREAMING_SNAKE_CASE  :
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          return None





class 					_SCREAMING_SNAKE_CASE  :
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          return None





class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      _A						:		int    	=     [
          # (model_name, model_kwargs)
          ('bert-base-cased', {}),
          ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
      ]
      @require_tf
      @slow
      def A_			(			self ):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(lowerCamelCase		,	"tf"		,	12		,	**lowerCamelCase )
      @require_torch
      @slow
      def A_			(			self ):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(lowerCamelCase		,	"pt"		,	12		,	**lowerCamelCase )
      @require_torch
      @slow
      def A_			(			self ):
          from transformers import BertModel

          snake_case__						=				["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
          with NamedTemporaryFile(mode="w+t" ) as vocab_file:
              vocab_file.write("\n".join(lowerCamelCase ) )
              vocab_file.flush()
              snake_case__						=				BertTokenizerFast(vocab_file.name )

          with TemporaryDirectory() as bert_save_dir:
              snake_case__						=				BertModel(BertConfig(vocab_size=len(lowerCamelCase ) ) )
              model.save_pretrained(lowerCamelCase )
              self._test_export(lowerCamelCase		,	"pt"		,	12		,	lowerCamelCase )
      @require_tf
      @slow
      def A_			(			self ):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              snake_case__						=				self._test_export(lowerCamelCase		,	"tf"		,	12		,	**lowerCamelCase )
              snake_case__						=				quantize(Path(lowerCamelCase ) )

              # Ensure the actual quantized model is not bigger than the original one
              if quantized_path.stat().st_size >= Path(lowerCamelCase ).stat().st_size:
                  self.fail("Quantized model is bigger than initial ONNX model" )
      @require_torch
      @slow
      def A_			(			self ):
          for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              snake_case__						=				self._test_export(lowerCamelCase		,	"pt"		,	12		,	**lowerCamelCase )
              snake_case__						=				quantize(lowerCamelCase )

              # Ensure the actual quantized model is not bigger than the original one
              if quantized_path.stat().st_size >= Path(lowerCamelCase ).stat().st_size:
                  self.fail("Quantized model is bigger than initial ONNX model" )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
          try:
              # Compute path
              with TemporaryDirectory() as tempdir:
                  snake_case__						=				Path(lowerCamelCase ).joinpath("model.onnx" )

              # Remove folder if exists
              if path.parent.exists():
                  path.parent.rmdir()

              # Export
              convert(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase )

              return path
          except Exception as e:
              self.fail(lowerCamelCase )
      @require_torch
      @require_tokenizers
      @slow
      def A_			(			self ):
          from transformers import BertModel

          snake_case__						=				BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
          snake_case__						=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
          self._test_infer_dynamic_axis(lowerCamelCase		,	lowerCamelCase		,	"pt" )
      @require_tf
      @require_tokenizers
      @slow
      def A_			(			self ):
          from transformers import TFBertModel

          snake_case__						=				TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
          snake_case__						=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
          self._test_infer_dynamic_axis(lowerCamelCase		,	lowerCamelCase		,	"tf" )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				FeatureExtractionPipeline(lowerCamelCase		,	lowerCamelCase )

          snake_case__						=				["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
          snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				infer_shapes(lowerCamelCase		,	lowerCamelCase )

          # Assert all variables are present
          self.assertEqual(len(lowerCamelCase )		,	len(lowerCamelCase ) )
          self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
          self.assertSequenceEqual(variable_names[:3]		,	lowerCamelCase )
          self.assertSequenceEqual(variable_names[3:]		,	lowerCamelCase )

          # Assert inputs are {0: batch, 1: sequence}
          for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
              self.assertDictEqual(shapes[var_name]		,	{0: "batch", 1: "sequence"} )

          # Assert outputs are {0: batch, 1: sequence} and {0: batch}
          self.assertDictEqual(shapes["output_0"]		,	{0: "batch", 1: "sequence"} )
          self.assertDictEqual(shapes["output_1"]		,	{0: "batch"} )
      def A_			(			self ):
          snake_case__						=				["input_ids", "attention_mask", "token_type_ids"]
          snake_case__						=				{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
          snake_case__						,					snake_case__						=				ensure_valid_input(FuncContiguousArgs()		,	lowerCamelCase		,	lowerCamelCase )

          # Should have exactly the same number of args (all are valid)
          self.assertEqual(len(lowerCamelCase )		,	3 )

          # Should have exactly the same input names
          self.assertEqual(set(lowerCamelCase )		,	set(lowerCamelCase ) )

          # Parameter should be reordered according to their respective place in the function:
          # (input_ids, token_type_ids, attention_mask)
          self.assertEqual(lowerCamelCase		,	(tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"]) )

          # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
          snake_case__						,					snake_case__						=				ensure_valid_input(FuncNonContiguousArgs()		,	lowerCamelCase		,	lowerCamelCase )

          # Should have exactly the one arg (all before the one not provided "some_other_args")
          self.assertEqual(len(lowerCamelCase )		,	1 )
          self.assertEqual(len(lowerCamelCase )		,	1 )

          # Should have only "input_ids"
          self.assertEqual(inputs_args[0]		,	tokens["input_ids"] )
          self.assertEqual(ordered_input_names[0]		,	"input_ids" )



      def A_			(			self ):
          snake_case__						=				generate_identified_filename(Path("/home/something/my_fake_model.onnx" )		,	"-test" )
          self.assertEqual("/home/something/my_fake_model-test.onnx"		,	generated.as_posix() )



