





"""simple docstring"""



import collections
import os
import re
from pathlib import Path


a_ =			"""src/transformers"""


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







def 		__lowercase     (   snake_case_  :  int						)   ->Dict:



    '''simple docstring'''







    if _re_test_backend.search(snake_case_						) is None:
        return None
    __A    :						Tuple						 =     [b[0] for b in _re_backend.findall(snake_case_						)]
    backends.sort()
    return "_and_".join(snake_case_						)







def 		__lowercase     (   snake_case_  :  Any						)   ->Tuple:



    '''simple docstring'''







    with open(snake_case_				,'''r'''				,encoding='''utf-8'''				,newline='''\n'''						) as f:
        __A    :						str						 =     f.readlines()

    __A    :						Union[str, Any]						 =     0
    while line_index < len(snake_case_						) and not lines[line_index].startswith('''_import_structure = {'''						):
        line_index += 1

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

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

    __A    :						Optional[int]						 =     {'''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.
        __A    :						Tuple						 =     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:
            __A    :						Dict						 =     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

            __A    :						Optional[int]						 =     []
            # Until we unindent, add backend objects to the list
            while len(lines[line_index]						) <= 1 or lines[line_index].startswith(''' ''' * 4						):
                __A    :						int						 =     lines[line_index]
                if _re_import_struct_add_one.search(snake_case_						) is not None:
                    objects.append(_re_import_struct_add_one.search(snake_case_						).groups()[0]						)
                elif _re_import_struct_add_many.search(snake_case_						) is not None:
                    __A    :						List[Any]						 =     _re_import_struct_add_many.search(snake_case_						).groups()[0].split(''', '''						)
                    __A    :						List[str]						 =     [obj[1:-1] for obj in imports if len(snake_case_						) > 0]
                    objects.extend(snake_case_						)
                elif _re_between_brackets.search(snake_case_						) is not None:
                    __A    :						Tuple						 =     _re_between_brackets.search(snake_case_						).groups()[0].split(''', '''						)
                    __A    :						Any						 =     [obj[1:-1] for obj in imports if len(snake_case_						) > 0]
                    objects.extend(snake_case_						)
                elif _re_quote_object.search(snake_case_						) is not None:
                    objects.append(_re_quote_object.search(snake_case_						).groups()[0]						)
                elif line.startswith(''' ''' * 8 + '''"'''						):
                    objects.append(line[9:-3]						)
                elif line.startswith(''' ''' * 12 + '''"'''						):
                    objects.append(line[13:-3]						)
                line_index += 1

            __A    :						Any						 =     objects
        else:
            line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
    __A    :						Any						 =     []
    while (
        line_index < len(snake_case_						)
        and find_backend(lines[line_index]						) is None
        and not lines[line_index].startswith('''else'''						)
    ):
        __A    :						Union[str, Any]						 =     lines[line_index]
        __A    :						Optional[int]						 =     _re_import.search(snake_case_						)
        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

    __A    :						List[str]						 =     {'''none''': objects}
    # Let's continue with backend-specific objects
    while line_index < len(snake_case_						):
        # If the line is an if is_backend_available, we grab all objects associated.
        __A    :						str						 =     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:
            __A    :						Optional[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

            __A    :						List[Any]						 =     []
            # Until we unindent, add backend objects to the list
            while len(lines[line_index]						) <= 1 or lines[line_index].startswith(''' ''' * 8						):
                __A    :						Optional[int]						 =     lines[line_index]
                __A    :						Optional[Any]						 =     _re_import.search(snake_case_						)
                if single_line_import_search is not None:
                    objects.extend(single_line_import_search.groups()[0].split(''', '''						)						)
                elif line.startswith(''' ''' * 12						):
                    objects.append(line[12:-2]						)
                line_index += 1

            __A    :						str						 =     objects
        else:
            line_index += 1

    return import_dict_objects, type_hint_objects







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  Tuple						)   ->List[Any]:



    '''simple docstring'''







    def find_duplicates(snake_case_  :  Dict						):
        return [k for k, v in collections.Counter(snake_case_						).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!"]

    __A    :						Optional[Any]						 =     []
    for key in import_dict_objects.keys():
        __A    :						Optional[int]						 =     find_duplicates(import_dict_objects[key]						)
        if duplicate_imports:
            errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}"""						)
        __A    :						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]						)						):
            __A    :						List[str]						 =     '''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 		__lowercase     (   )   ->int:



    '''simple docstring'''







    __A    :						Dict						 =     []
    for root, _, files in os.walk(snake_case_						):
        if "__init__.py" in files:
            __A    :						List[str]						 =     os.path.join(snake_case_				,'''__init__.py'''						)
            __A    :						int						 =     parse_init(snake_case_						)
            if objects is not None:
                __A    :						str						 =     analyze_results(*snake_case_						)
                if len(snake_case_						) > 0:
                    __A    :						str						 =     F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
                    failures.append('''\n'''.join(snake_case_						)						)
    if len(snake_case_						) > 0:
        raise ValueError('''\n\n'''.join(snake_case_						)						)







def 		__lowercase     (   )   ->List[Any]:



    '''simple docstring'''







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


a_ =			[
    """convert_pytorch_checkpoint_to_tf2""",
    """modeling_flax_pytorch_utils""",
    """models.esm.openfold_utils""",
]







def 		__lowercase     (   )   ->List[Any]:



    '''simple docstring'''







    from transformers.utils import direct_transformers_import

    __A    :						str						 =     direct_transformers_import(snake_case_						)

    __A    :						int						 =     set(transformers._import_structure.keys()						)
    # This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
    # some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
    # (potentiall re-) add them.
    with open(os.path.join(snake_case_				,'''__init__.py'''						)				,'''r'''						) as f:
        __A    :						Union[str, Any]						 =     f.read()
    import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]'''				,snake_case_						)						)						)

    __A    :						str						 =     [
        module
        for module in get_transformers_submodules()
        if module not in IGNORE_SUBMODULES and module not in import_structure_keys
    ]

    if len(snake_case_						) > 0:
        __A    :						List[str]						 =     '''\n'''.join(F"""- {module}""" for module in module_not_registered						)
        raise ValueError(
            '''The following submodules are not properly registed 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()











"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42

 # setable values
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     None

 @classmethod
 def 					UpperCamelCase__(    cls					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     return cls(common=__lowerCamelCase					,						init_noise_sigma=__lowerCamelCase					,						timesteps=__lowerCamelCase  )






@dataclass
class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     42






class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCamelCase        =     42

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return True

 @register_to_config
 def __init__(    self					,						__lowerCamelCase = 1000					,						__lowerCamelCase = 0.0_0_0_1					,						__lowerCamelCase = 0.0_2					,						__lowerCamelCase = "linear"					,						__lowerCamelCase = None					,						__lowerCamelCase = "fixed_small"					,						__lowerCamelCase = True					,						__lowerCamelCase = "epsilon"					,						__lowerCamelCase = jnp.floataa					,						):



     '''simple docstring'''




     __A    :						Tuple						 =     dtype

 def 					UpperCamelCase__(    self					,						__lowerCamelCase = None  ):



     '''simple docstring'''




     if common is None:
         __A    :						Tuple						 =     CommonSchedulerState.create(self  )

     # standard deviation of the initial noise distribution
     __A    :						Tuple						 =     jnp.array(1.0					,						dtype=self.dtype  )

     __A    :						Optional[int]						 =     jnp.arange(0					,						self.config.num_train_timesteps  ).round()[::-1]

     return DDPMSchedulerState.create(
         common=__lowerCamelCase					,						init_noise_sigma=__lowerCamelCase					,						timesteps=__lowerCamelCase					,						)

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None  ):



     '''simple docstring'''




     return sample

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = ()  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     self.config.num_train_timesteps // num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # rounding to avoid issues when num_inference_step is power of 3
     __A    :						Optional[Any]						 =     (jnp.arange(0					,						__lowerCamelCase  ) * step_ratio).round()[::-1]

     return state.replace(
         num_inference_steps=__lowerCamelCase					,						timesteps=__lowerCamelCase					,						)

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase=None					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     __A    :						int						 =     state.common.alphas_cumprod[t]
     __A    :						List[str]						 =     jnp.where(t > 0					,						state.common.alphas_cumprod[t - 1]					,						jnp.array(1.0					,						dtype=self.dtype  )  )

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
     # and sample from it to get previous sample
     # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
     __A    :						str						 =     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

     if variance_type is None:
         __A    :						Dict						 =     self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small":
         __A    :						List[Any]						 =     jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )
     # for rl-diffuser https://arxiv.org/abs/2205.09991
     elif variance_type == "fixed_small_log":
         __A    :						Optional[Any]						 =     jnp.log(jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )  )
     elif variance_type == "fixed_large":
         __A    :						Tuple						 =     state.common.betas[t]
     elif variance_type == "fixed_large_log":
         # Glide max_log
         __A    :						Union[str, Any]						 =     jnp.log(state.common.betas[t]  )
     elif variance_type == "learned":
         return predicted_variance
     elif variance_type == "learned_range":
         __A    :						Optional[Any]						 =     variance
         __A    :						Optional[Any]						 =     state.common.betas[t]
         __A    :						Any						 =     (predicted_variance + 1) / 2
         __A    :						Union[str, Any]						 =     frac * max_log + (1 - frac) * min_log

     return variance

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None					,						__lowerCamelCase = True					,						):



     '''simple docstring'''




     __A    :						Optional[int]						 =     timestep

     if key is None:
         __A    :						List[Any]						 =     jax.random.PRNGKey(0  )

     if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
         __A ,       __A    :						Tuple						 =     jnp.split(__lowerCamelCase					,						sample.shape[1]					,						axis=1  )
     else:
         __A    :						List[str]						 =     None

     # 1. compute alphas, betas
     __A    :						Dict						 =     state.common.alphas_cumprod[t]
     __A    :						int						 =     jnp.where(t > 0					,						state.common.alphas_cumprod[t - 1]					,						jnp.array(1.0					,						dtype=self.dtype  )  )
     __A    :						Tuple						 =     1 - alpha_prod_t
     __A    :						Optional[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 self.config.prediction_type == "epsilon":
         __A    :						Optional[Any]						 =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
         __A    :						Any						 =     model_output
     elif self.config.prediction_type == "v_prediction":
         __A    :						str						 =     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
             ''' for the FlaxDDPMScheduler.'''  )

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
         __A    :						str						 =     jnp.clip(__lowerCamelCase					,						-1					,						1  )

     # 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
     __A    :						Optional[Any]						 =     (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     __A    :						Union[str, Any]						 =     state.common.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
     __A    :						Optional[Any]						 =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     def random_variance():
         __A    :						List[Any]						 =     jax.random.split(__lowerCamelCase					,						num=1  )
         __A    :						List[str]						 =     jax.random.normal(__lowerCamelCase					,						shape=model_output.shape					,						dtype=self.dtype  )
         return (self._get_variance(__lowerCamelCase					,						__lowerCamelCase					,						predicted_variance=__lowerCamelCase  ) ** 0.5) * noise

     __A    :						Optional[Any]						 =     jnp.where(t > 0					,						random_variance()					,						jnp.zeros(model_output.shape					,						dtype=self.dtype  )  )

     __A    :						Any						 =     pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__lowerCamelCase					,						state=__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     return add_noise_common(state.common					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     return get_velocity_common(state.common					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )







 def __len__(    self  ):



     '''simple docstring'''




     return self.config.num_train_timesteps




