


'''simple docstring'''
from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowerCAmelCase   : List[Any]             =				8




def 	_A  (       A							,A=BITS							)  ->				Tuple:
	lowercase						:		List[str]   					=		x.device

	lowercase						:		List[str]   					=		(x * 2_5_5).int().clamp(0							,2_5_5							)

	lowercase						:		int   					=		2 ** torch.arange(bits - 1							,-1							,-1							,device=A							)
	lowercase						:		List[Any]   					=		rearrange(A							,"d -> d 1 1"							)
	lowercase						:		List[Any]   					=		rearrange(A							,"b c h w -> b c 1 h w"							)

	lowercase						:		str   					=		((x & mask) != 0).float()
	lowercase						:		Optional[Any]   					=		rearrange(A							,"b c d h w -> b (c d) h w"							)
	lowercase						:		Tuple   					=		bits * 2 - 1
	return bits




def 	_A  (       A							,A=BITS							)  ->				Tuple:
	lowercase						:		Tuple   					=		x.device

	lowercase						:		str   					=		(x > 0).int()
	lowercase						:		Optional[Any]   					=		2 ** torch.arange(bits - 1							,-1							,-1							,device=A							,dtype=torch.intaa							)

	lowercase						:		Optional[int]   					=		rearrange(A							,"d -> d 1 1"							)
	lowercase						:		List[Any]   					=		rearrange(A							,"b (c d) h w -> b c d h w"							,d=8							)
	lowercase						:		List[str]   					=		reduce(x * mask							,"b c d h w -> b c h w"							,"sum"							)
	return (dec / 2_5_5).clamp(0.0							,1.0							)




def 	_A  (       self							,A							,A							,A							,A = 0.0							,A = True							,A=None							,A = True							,)  ->				Union[DDIMSchedulerOutput, Tuple]:
	if self.num_inference_steps is None:
		raise ValueError(
		    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"							)

	# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
	# Ideally, read DDIM paper in-detail understanding

	# Notation (<variable name> -> <name in paper>
	# - pred_noise_t -> e_theta(x_t, t)
	# - pred_original_sample -> f_theta(x_t, t) or x_0
	# - std_dev_t -> sigma_t
	# - eta -> η
	# - pred_sample_direction -> "direction pointing to x_t"
	# - pred_prev_sample -> "x_t-1"

	# 1. get previous step value (=t-1)
	lowercase						:		Dict   					=		timestep - self.config.num_train_timesteps // self.num_inference_steps

	# 2. compute alphas, betas
	lowercase						:		Tuple   					=		self.alphas_cumprod[timestep]
	lowercase						:		Tuple   					=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

	lowercase						:		Any   					=		1 - alpha_prod_t

	# 3. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	lowercase						:		Tuple   					=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

	# 4. Clip "predicted x_0"
	lowercase						:		Dict   					=		self.bit_scale
	if self.config.clip_sample:
		lowercase						:		str   					=		torch.clamp(A							,-scale							,A							)

	# 5. compute variance: "sigma_t(η)" -> see formula (16)
	# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
	lowercase						:		Optional[Any]   					=		self._get_variance(A							,A							)
	lowercase						:		Tuple   					=		eta * variance ** 0.5

	if use_clipped_model_output:
		# the model_output is always re-derived from the clipped x_0 in Glide
		lowercase						:		Any   					=		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

	# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	lowercase						:		str   					=		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

	# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	lowercase						:		int   					=		alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

	if eta > 0:
		# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
		lowercase						:		Any   					=		model_output.device if torch.is_tensor(A							) else "cpu"
		lowercase						:		Any   					=		torch.randn(model_output.shape							,dtype=model_output.dtype							,generator=A							).to(A							)
		lowercase						:		int   					=		self._get_variance(A							,A							) ** 0.5 * eta * noise

		lowercase						:		List[str]   					=		prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=A							,pred_original_sample=A							)




def 	_A  (       self							,A							,A							,A							,A="epsilon"							,A=None							,A = True							,)  ->				Union[DDPMSchedulerOutput, Tuple]:
	lowercase						:		Optional[int]   					=		timestep

	if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
		lowercase					,		lowercase						:		int   					=		torch.split(A							,sample.shape[1]							,dim=1							)
	else:
		lowercase						:		Union[str, Any]   					=		None

	# 1. compute alphas, betas
	lowercase						:		Optional[int]   					=		self.alphas_cumprod[t]
	lowercase						:		int   					=		self.alphas_cumprod[t - 1] if t > 0 else self.one
	lowercase						:		Optional[int]   					=		1 - alpha_prod_t
	lowercase						:		int   					=		1 - alpha_prod_t_prev

	# 2. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
	if prediction_type == "epsilon":
		lowercase						:		Tuple   					=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		lowercase						:		str   					=		model_output
	else:
		raise ValueError(F'''Unsupported prediction_type {prediction_type}.'''							)

	# 3. Clip "predicted x_0"
	lowercase						:		int   					=		self.bit_scale
	if self.config.clip_sample:
		lowercase						:		Dict   					=		torch.clamp(A							,-scale							,A							)

	# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	lowercase						:		List[str]   					=		(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
	lowercase						:		Optional[int]   					=		self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

	# 5. Compute predicted previous sample µ_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	lowercase						:		Optional[Any]   					=		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

	# 6. Add noise
	lowercase						:		List[Any]   					=		0
	if t > 0:
		lowercase						:		List[Any]   					=		torch.randn(
		    model_output.size()							,dtype=model_output.dtype							,layout=model_output.layout							,generator=A							).to(model_output.device							)
		lowercase						:		int   					=		(self._get_variance(A							,predicted_variance=A							) ** 0.5) * noise

	lowercase						:		str   					=		pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=A							,pred_original_sample=A							)






class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_							,      a_ = 1.0							,      )    ->  Any:
					super().__init__()
					lowercase						:		Dict   					=		bit_scale
					lowercase						:		List[Any]   					=		(
					    ddim_bit_scheduler_step if isinstance(a_							,      a_							) else ddpm_bit_scheduler_step
					)

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


				@torch.no_grad()
				def __call__(     self							,      a_ = 2_5_6							,      a_ = 2_5_6							,      a_ = 5_0							,      a_ = None							,      a_ = 1							,      a_ = "pil"							,      a_ = True							,      **a_							,      )    ->  Union[Tuple, ImagePipelineOutput]:
					lowercase						:		List[str]   					=		torch.randn(
					    (batch_size, self.unet.config.in_channels, height, width)							,      generator=a_							,      )
					lowercase						:		List[Any]   					=		decimal_to_bits(a_							) * self.bit_scale
					lowercase						:		Any   					=		latents.to(self.device							)

					self.scheduler.set_timesteps(a_							)

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

						# compute the previous noisy sample x_t -> x_t-1
						lowercase						:		Any   					=		self.scheduler.step(a_							,      a_							,      a_							).prev_sample

					lowercase						:		Dict   					=		bits_to_decimal(a_							)

					if output_type == "pil":
						lowercase						:		Any   					=		self.numpy_to_pil(a_							)

					if not return_dict:
						return (image,)

					return ImagePipelineOutput(images=a_							)






'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path


lowerCAmelCase   : List[str]             =				"""src/transformers"""


# Matches is_xxx_available()
lowerCAmelCase   : List[str]             =				re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase   : str             =				re.compile(R"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCAmelCase   : Optional[int]             =				re.compile(R"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
lowerCAmelCase   : Optional[int]             =				re.compile(R"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
lowerCAmelCase   : str             =				re.compile(R"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCAmelCase   : List[Any]             =				re.compile(R"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCAmelCase   : Dict             =				re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCAmelCase   : Tuple             =				re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
lowerCAmelCase   : Optional[int]             =				re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
lowerCAmelCase   : Optional[int]             =				re.compile(R"""^\s*try:""")
# Catches a line with else:
lowerCAmelCase   : Optional[int]             =				re.compile(R"""^\s*else:""")




def 	_A  (       A							)  ->				List[Any]:
	if _re_test_backend.search(A							) is None:
		return None
	lowercase						:		Union[str, Any]   					=		[b[0] for b in _re_backend.findall(A							)]
	backends.sort()
	return "_and_".join(A							)




def 	_A  (       A							)  ->				Optional[Any]:
	with open(A							,"r"							,encoding="utf-8"							,newline="\n"							) as f:
		lowercase						:		Tuple   					=		f.readlines()

	lowercase						:		List[str]   					=		0
	while line_index < len(A							) and not lines[line_index].startswith("_import_structure = {"							):
		line_index += 1

	# If this is a traditional init, just return.
	if line_index >= len(A							):
		return None

	# First grab the objects without a specific backend in _import_structure
	lowercase						:		Union[str, Any]   					=		[]
	while not lines[line_index].startswith("if TYPE_CHECKING"							) and find_backend(lines[line_index]							) is None:
		lowercase						:		Any   					=		lines[line_index]
		# If we have everything on a single line, let's deal with it.
		if _re_one_line_import_struct.search(A							):
			lowercase						:		int   					=		_re_one_line_import_struct.search(A							).groups()[0]
			lowercase						:		Union[str, Any]   					=		re.findall("\[([^\]]+)\]"							,A							)
			for imp in imports:
				objects.extend([obj[1:-1] for obj in imp.split(", "							)]							)
			line_index += 1
			continue
		lowercase						:		Optional[Any]   					=		_re_import_struct_key_value.search(A							)
		if single_line_import_search is not None:
			lowercase						:		List[Any]   					=		[obj[1:-1] for obj in single_line_import_search.groups()[0].split(", "							) if len(A							) > 0]
			objects.extend(A							)
		elif line.startswith(" " * 8 + "\""							):
			objects.append(line[9:-3]							)
		line_index += 1

	lowercase						:		Tuple   					=		{"none": objects}
	# Let's continue with backend-specific objects in _import_structure
	while not lines[line_index].startswith("if TYPE_CHECKING"							):
		# If the line is an if not is_backend_available, we grab all objects associated.
		lowercase						:		int   					=		find_backend(lines[line_index]							)
		# Check if the backend declaration is inside a try block:
		if _re_try.search(lines[line_index - 1]							) is None:
			lowercase						:		str   					=		None

		if backend is not None:
			line_index += 1

			# Scroll until we hit the else block of try-except-else
			while _re_else.search(lines[line_index]							) is None:
				line_index += 1

			line_index += 1

			lowercase						:		Any   					=		[]
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]							) <= 1 or lines[line_index].startswith(" " * 4							):
				lowercase						:		List[Any]   					=		lines[line_index]
				if _re_import_struct_add_one.search(A							) is not None:
					objects.append(_re_import_struct_add_one.search(A							).groups()[0]							)
				elif _re_import_struct_add_many.search(A							) is not None:
					lowercase						:		Dict   					=		_re_import_struct_add_many.search(A							).groups()[0].split(", "							)
					lowercase						:		Any   					=		[obj[1:-1] for obj in imports if len(A							) > 0]
					objects.extend(A							)
				elif _re_between_brackets.search(A							) is not None:
					lowercase						:		Optional[Any]   					=		_re_between_brackets.search(A							).groups()[0].split(", "							)
					lowercase						:		Tuple   					=		[obj[1:-1] for obj in imports if len(A							) > 0]
					objects.extend(A							)
				elif _re_quote_object.search(A							) is not None:
					objects.append(_re_quote_object.search(A							).groups()[0]							)
				elif line.startswith(" " * 8 + "\""							):
					objects.append(line[9:-3]							)
				elif line.startswith(" " * 1_2 + "\""							):
					objects.append(line[1_3:-3]							)
				line_index += 1

			lowercase						:		Optional[int]   					=		objects
		else:
			line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
	lowercase						:		str   					=		[]
	while (
	    line_index < len(A							)
	    and find_backend(lines[line_index]							) is None
	    and not lines[line_index].startswith("else"							)
	):
		lowercase						:		int   					=		lines[line_index]
		lowercase						:		Any   					=		_re_import.search(A							)
		if single_line_import_search is not None:
			objects.extend(single_line_import_search.groups()[0].split(", "							)							)
		elif line.startswith(" " * 8							):
			objects.append(line[8:-2]							)
		line_index += 1

	lowercase						:		Optional[Any]   					=		{"none": objects}
	# Let's continue with backend-specific objects
	while line_index < len(A							):
		# If the line is an if is_backend_available, we grab all objects associated.
		lowercase						:		List[Any]   					=		find_backend(lines[line_index]							)
		# Check if the backend declaration is inside a try block:
		if _re_try.search(lines[line_index - 1]							) is None:
			lowercase						:		int   					=		None

		if backend is not None:
			line_index += 1

			# Scroll until we hit the else block of try-except-else
			while _re_else.search(lines[line_index]							) is None:
				line_index += 1

			line_index += 1

			lowercase						:		Optional[Any]   					=		[]
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]							) <= 1 or lines[line_index].startswith(" " * 8							):
				lowercase						:		List[Any]   					=		lines[line_index]
				lowercase						:		Tuple   					=		_re_import.search(A							)
				if single_line_import_search is not None:
					objects.extend(single_line_import_search.groups()[0].split(", "							)							)
				elif line.startswith(" " * 1_2							):
					objects.append(line[1_2:-2]							)
				line_index += 1

			lowercase						:		Dict   					=		objects
		else:
			line_index += 1

	return import_dict_objects, type_hint_objects




def 	_A  (       A							,A							)  ->				List[str]:

	def find_duplicates(A							):
		return [k for k, v in collections.Counter(A							).items() if v > 1]

	if list(import_dict_objects.keys()							) != list(type_hint_objects.keys()							):
		return ["Both sides of the init do not have the same backends!"]

	lowercase						:		str   					=		[]
	for key in import_dict_objects.keys():
		lowercase						:		List[str]   					=		find_duplicates(import_dict_objects[key]							)
		if duplicate_imports:
			errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}'''							)
		lowercase						:		List[Any]   					=		find_duplicates(type_hint_objects[key]							)
		if duplicate_type_hints:
			errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''							)

		if sorted(set(import_dict_objects[key]							)							) != sorted(set(type_hint_objects[key]							)							):
			lowercase						:		Optional[int]   					=		"base imports" if key == "none" else F'''{key} backend'''
			errors.append(F'''Differences for {name}:'''							)
			for a in type_hint_objects[key]:
				if a not in import_dict_objects[key]:
					errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.'''							)
			for a in import_dict_objects[key]:
				if a not in type_hint_objects[key]:
					errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.'''							)
	return errors




def 	_A  (       )  ->				List[Any]:
	lowercase						:		Any   					=		[]
	for root, _, files in os.walk(A							):
		if "__init__.py" in files:
			lowercase						:		Optional[Any]   					=		os.path.join(A							,"__init__.py"							)
			lowercase						:		Tuple   					=		parse_init(A							)
			if objects is not None:
				lowercase						:		Tuple   					=		analyze_results(*A							)
				if len(A							) > 0:
					lowercase						:		Tuple   					=		F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
					failures.append("\n".join(A							)							)
	if len(A							) > 0:
		raise ValueError("\n\n".join(A							)							)




def 	_A  (       )  ->				Union[str, Any]:
	lowercase						:		Dict   					=		[]
	for path, directories, files in os.walk(A							):
		for folder in directories:
			# Ignore private modules
			if folder.startswith("_"							):
				directories.remove(A							)
				continue
			# Ignore leftovers from branches (empty folders apart from pycache)
			if len(list((Path(A							) / folder).glob("*.py"							)							)							) == 0:
				continue
			lowercase						:		str   					=		str((Path(A							) / folder).relative_to(A							)							)
			lowercase						:		Optional[Any]   					=		short_path.replace(os.path.sep							,"."							)
			submodules.append(A							)
		for fname in files:
			if fname == "__init__.py":
				continue
			lowercase						:		Tuple   					=		str((Path(A							) / fname).relative_to(A							)							)
			lowercase						:		int   					=		short_path.replace(".py"							,""							).replace(os.path.sep							,"."							)
			if len(submodule.split("."							)							) == 1:
				submodules.append(A							)
	return submodules


lowerCAmelCase   : Dict             =				[
    """convert_pytorch_checkpoint_to_tf2""",
    """modeling_flax_pytorch_utils""",
]




def 	_A  (       )  ->				Optional[int]:
	# This is to make sure the transformers module imported is the one in the repo.
	lowercase						:		int   					=		importlib.util.spec_from_file_location(
	    "transformers"							,os.path.join(A							,"__init__.py"							)							,submodule_search_locations=[PATH_TO_TRANSFORMERS]							,)
	lowercase						:		Optional[int]   					=		spec.loader.load_module()

	lowercase						:		Dict   					=		[
	    module
	    for module in get_transformers_submodules()
	    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
	]
	if len(A							) > 0:
		lowercase						:		Optional[int]   					=		"\n".join(F'''- {module}''' for module in module_not_registered							)
		raise ValueError(
		    "The following submodules are not properly registered in the main init of Transformers:\n"
		    F'''{list_of_modules}\n'''
		    "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."							)


if __name__ == "__main__":
					check_all_inits()
					check_submodules()


