
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 			A_  :




	'''simple docstring'''


	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case			):
		return None







class 			A_  :




	'''simple docstring'''


	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case			):
		return None







class 			A_  (     unittest.TestCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Optional[Any]									=			[
	    # (model_name, model_kwargs)
	    ("""bert-base-cased""", {}),
	    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
	]





	@require_tf
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case						,       'tf'						,       12						,       **snake_case			)





	@require_torch
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case						,       'pt'						,       12						,       **snake_case			)





	@require_torch
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		from transformers import BertModel

		lowercase	    =       ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
		with NamedTemporaryFile(mode='w+t'			) as vocab_file:
			vocab_file.write('\n'.join(snake_case			)			)
			vocab_file.flush()
			lowercase	    =       BertTokenizerFast(vocab_file.name			)

		with TemporaryDirectory() as bert_save_dir:
			lowercase	    =       BertModel(BertConfig(vocab_size=len(snake_case			)			)			)
			model.save_pretrained(snake_case			)
			self._test_export(snake_case						,       'pt'						,       12						,       snake_case			)





	@require_tf
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase	    =       self._test_export(snake_case						,       'tf'						,       12						,       **snake_case			)
			lowercase	    =       quantize(Path(snake_case			)			)

			# Ensure the actual quantized model is not bigger than the original one
			if quantized_path.stat().st_size >= Path(snake_case			).stat().st_size:
				self.fail('Quantized model is bigger than initial ONNX model'			)





	@require_torch
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase	    =       self._test_export(snake_case						,       'pt'						,       12						,       **snake_case			)
			lowercase	    =       quantize(snake_case			)

			# Ensure the actual quantized model is not bigger than the original one
			if quantized_path.stat().st_size >= Path(snake_case			).stat().st_size:
				self.fail('Quantized model is bigger than initial ONNX model'			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case=None						,       **snake_case			):
		try:
			# Compute path
			with TemporaryDirectory() as tempdir:
				lowercase	    =       Path(snake_case			).joinpath('model.onnx'			)

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

			# Export
			convert(snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       **snake_case			)

			return path
		except Exception as e:
			self.fail(snake_case			)





	@require_torch
	@require_tokenizers
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		from transformers import BertModel

		lowercase	    =       BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'			)			)
		lowercase	    =       BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'			)
		self._test_infer_dynamic_axis(snake_case						,       snake_case						,       'pt'			)





	@require_tf
	@require_tokenizers
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		from transformers import TFBertModel

		lowercase	    =       TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'			)			)
		lowercase	    =       BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'			)
		self._test_infer_dynamic_axis(snake_case						,       snake_case						,       'tf'			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case			):
		lowercase	    =       FeatureExtractionPipeline(snake_case						,       snake_case			)

		lowercase	    =       ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       infer_shapes(snake_case						,       snake_case			)

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

		# 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       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       ['input_ids', 'attention_mask', 'token_type_ids']
		lowercase	    =       {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
		lowercase    ,   lowercase	    =       ensure_valid_input(FuncContiguousArgs()						,       snake_case						,       snake_case			)

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

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

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

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       generate_identified_filename(Path('/home/something/my_fake_model.onnx'			)						,       '-test'			)
		self.assertEqual('/home/something/my_fake_model-test.onnx'						,       generated.as_posix()			)









"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      UpperCamelCase		(__snake_case     ):






						def __init__(		self  :Any			, __magic_name__  :Union[str, Any]			, __magic_name__  :Optional[Any]				)      ->Dict:
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase    :      Optional[Any]       = DDIMScheduler.from_config(scheduler.config				)

										self.register_modules(unet=__magic_name__			, scheduler=__magic_name__				)






						@torch.no_grad()
						def __call__(		self  :Optional[int]			, __magic_name__  :int = 1			, __magic_name__  :Optional[Union[torch.Generator, List[torch.Generator]]] = None			, __magic_name__  :float = 0.0			, __magic_name__  :int = 50			, __magic_name__  :Optional[bool] = None			, __magic_name__  :Optional[str] = "pil"			, __magic_name__  :bool = True			, )      ->Union[ImagePipelineOutput, Tuple]:

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size			, __magic_name__				):
														lowercase    :      int       = (
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
										else:
														lowercase    :      Tuple       = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(__magic_name__			, __magic_name__				) and len(__magic_name__				) != batch_size:
														raise ValueError(
														    f"""You have passed a list of generators of length {len(__magic_name__				)}, but requested an effective batch"""
														    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

										lowercase    :      Dict       = randn_tensor(__magic_name__			, generator=__magic_name__			, device=self.device			, dtype=self.unet.dtype				)

										# set step values
										self.scheduler.set_timesteps(__magic_name__				)

										for t in self.progress_bar(self.scheduler.timesteps				):
														# 1. predict noise model_output
														lowercase    :      Union[str, Any]       = self.unet(__magic_name__			, __magic_name__				).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														lowercase    :      int       = self.scheduler.step(
														    __magic_name__			, __magic_name__			, __magic_name__			, eta=__magic_name__			, use_clipped_model_output=__magic_name__			, generator=__magic_name__				).prev_sample

										lowercase    :      Optional[Any]       = (image / 2 + 0.5).clamp(0			, 1				)
										lowercase    :      Tuple       = image.cpu().permute(0			, 2			, 3			, 1				).numpy()
										if output_type == "pil":
														lowercase    :      Any       = self.numpy_to_pil(__magic_name__				)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__magic_name__				)
