





import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map


def 							__snake_case							(    lowerCAmelCase_	)				->						List[Tuple[int, ...]]:
    SCREAMING_SNAKE_CASE__											=			[]
    if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
        for v in tree.values():
            shapes.extend(_fetch_dims(lowerCAmelCase_	)	)
    elif isinstance(lowerCAmelCase_ ,							(list, tuple)	):
        for t in tree:
            shapes.extend(_fetch_dims(lowerCAmelCase_	)	)
    elif isinstance(lowerCAmelCase_ ,							torch.Tensor	):
        shapes.append(tree.shape	)
    else:
        raise ValueError('''Not supported'''	)

    return shapes


@torch.jit.ignore
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple[int, ...]:
    SCREAMING_SNAKE_CASE__											=			[]
    for d in reversed(lowerCAmelCase_	):
        idx.append(flat_idx % d	)
        SCREAMING_SNAKE_CASE__											=			flat_idx // d

    return tuple(reversed(lowerCAmelCase_	)	)


@torch.jit.ignore
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = None ,							lowerCAmelCase_ = None ,							)				->						List[Tuple[slice, ...]]:

    # start_edges and end_edges both indicate whether, starting from any given
    # dimension, the start/end index is at the top/bottom edge of the
    # corresponding tensor, modeled as a tree
    def reduce_edge_list(lowerCAmelCase_	) -> None:
        SCREAMING_SNAKE_CASE__											=			True
        for i in range(len(lowerCAmelCase_	)	):
            SCREAMING_SNAKE_CASE__											=			-1 * (i + 1)
            l[reversed_idx] &= tally
            SCREAMING_SNAKE_CASE__											=			l[reversed_idx]

    if start_edges is None:
        SCREAMING_SNAKE_CASE__											=			[s == 0 for s in start]
        reduce_edge_list(lowerCAmelCase_	)
    if end_edges is None:
        SCREAMING_SNAKE_CASE__											=			[e == (d - 1) for e, d in zip(lowerCAmelCase_ ,							lowerCAmelCase_	)]
        reduce_edge_list(lowerCAmelCase_	)

    # Base cases. Either start/end are empty and we're done, or the final,
    # one-dimensional tensor can be simply sliced
    if len(lowerCAmelCase_	) == 0:
        return [()]
    elif len(lowerCAmelCase_	) == 1:
        return [(slice(start[0] ,							end[0] + 1	),)]

    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]

    # Dimensions common to start and end can be selected directly
    for s, e in zip(lowerCAmelCase_ ,							lowerCAmelCase_	):
        if s == e:
            path_list.append(slice(lowerCAmelCase_ ,							s + 1	)	)
        else:
            break

    SCREAMING_SNAKE_CASE__											=			tuple(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			len(lowerCAmelCase_	)

    # start == end, and we're done
    if divergence_idx == len(lowerCAmelCase_	):
        return [path]

    def upper() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        SCREAMING_SNAKE_CASE__											=			start[divergence_idx]
        return tuple(
            path + (slice(lowerCAmelCase_ ,							sdi + 1	),) + s
            for s in _get_minimal_slice_set(
                start[divergence_idx + 1 :] ,							[d - 1 for d in dims[divergence_idx + 1 :]] ,							dims[divergence_idx + 1 :] ,							start_edges=start_edges[divergence_idx + 1 :] ,							end_edges=[True for _ in end_edges[divergence_idx + 1 :]] ,							)	)

    def lower() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        SCREAMING_SNAKE_CASE__											=			end[divergence_idx]
        return tuple(
            path + (slice(lowerCAmelCase_ ,							edi + 1	),) + s
            for s in _get_minimal_slice_set(
                [0 for _ in start[divergence_idx + 1 :]] ,							end[divergence_idx + 1 :] ,							dims[divergence_idx + 1 :] ,							start_edges=[True for _ in start_edges[divergence_idx + 1 :]] ,							end_edges=end_edges[divergence_idx + 1 :] ,							)	)

    # If both start and end are at the edges of the subtree rooted at
    # divergence_idx, we can just select the whole subtree at once
    if start_edges[divergence_idx] and end_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx] ,							end[divergence_idx] + 1	),)	)
    # If just start is at the edge, we can grab almost all of the subtree,
    # treating only the ragged bottom edge as an edge case
    elif start_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx] ,							end[divergence_idx]	),)	)
        slices.extend(lower()	)
    # Analogous to the previous case, but the top is ragged this time
    elif end_edges[divergence_idx]:
        slices.extend(upper()	)
        slices.append(path + (slice(start[divergence_idx] + 1 ,							end[divergence_idx] + 1	),)	)
    # If both sides of the range are ragged, we need to handle both sides
    # separately. If there's contiguous meat in between them, we can index it
    # in one big chunk
    else:
        slices.extend(upper()	)
        SCREAMING_SNAKE_CASE__											=			end[divergence_idx] - start[divergence_idx]
        if middle_ground > 1:
            slices.append(path + (slice(start[divergence_idx] + 1 ,							end[divergence_idx]	),)	)
        slices.extend(lower()	)

    return slices


@torch.jit.ignore
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						torch.Tensor:
    SCREAMING_SNAKE_CASE__											=			t.shape[:no_batch_dims]
    SCREAMING_SNAKE_CASE__											=			list(_flat_idx_to_idx(lowerCAmelCase_ ,							lowerCAmelCase_	)	)
    # _get_minimal_slice_set is inclusive
    SCREAMING_SNAKE_CASE__											=			list(_flat_idx_to_idx(flat_end - 1 ,							lowerCAmelCase_	)	)

    # Get an ordered list of slices to perform
    SCREAMING_SNAKE_CASE__											=			_get_minimal_slice_set(
        lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							)

    SCREAMING_SNAKE_CASE__											=			[t[s] for s in slices]

    return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]	) for s in sliced_tensors]	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = False ,							lowerCAmelCase_ = None ,							lowerCAmelCase_ = False ,							)				->						Any:
    if not (len(lowerCAmelCase_	) > 0):
        raise ValueError('''Must provide at least one input'''	)

    SCREAMING_SNAKE_CASE__											=			[shape[:no_batch_dims] for shape in _fetch_dims(lowerCAmelCase_	)]
    SCREAMING_SNAKE_CASE__											=			tuple([max(lowerCAmelCase_	) for s in zip(*lowerCAmelCase_	)]	)

    def _prep_inputs(lowerCAmelCase_	) -> torch.Tensor:
        if not low_mem:
            if not sum(t.shape[:no_batch_dims]	) == no_batch_dims:
                SCREAMING_SNAKE_CASE__											=			t.expand(orig_batch_dims + t.shape[no_batch_dims:]	)
            SCREAMING_SNAKE_CASE__											=			t.reshape(-1 ,							*t.shape[no_batch_dims:]	)
        else:
            SCREAMING_SNAKE_CASE__											=			t.expand(orig_batch_dims + t.shape[no_batch_dims:]	)
        return t

    SCREAMING_SNAKE_CASE__											=			tensor_tree_map(_prep_inputs ,							lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			None
    if _out is not None:
        SCREAMING_SNAKE_CASE__											=			tensor_tree_map(lambda lowerCAmelCase_	: t.view([-1] + list(t.shape[no_batch_dims:]	)	) ,							_out	)

    SCREAMING_SNAKE_CASE__											=			1
    for d in orig_batch_dims:
        flat_batch_dim *= d

    SCREAMING_SNAKE_CASE__											=			flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

    def _select_chunk(lowerCAmelCase_	) -> torch.Tensor:
        return t[i : i + chunk_size] if t.shape[0] != 1 else t

    SCREAMING_SNAKE_CASE__											=			0
    SCREAMING_SNAKE_CASE__											=			prepped_outputs
    for _ in range(lowerCAmelCase_	):
        # Chunk the input
        if not low_mem:
            SCREAMING_SNAKE_CASE__											=			_select_chunk
        else:
            SCREAMING_SNAKE_CASE__											=			partial(
                _chunk_slice ,							flat_start=lowerCAmelCase_ ,							flat_end=min(lowerCAmelCase_ ,							i + chunk_size	) ,							no_batch_dims=len(lowerCAmelCase_	) ,							)

        SCREAMING_SNAKE_CASE__											=			tensor_tree_map(lowerCAmelCase_ ,							lowerCAmelCase_	)

        # Run the layer on the chunk
        SCREAMING_SNAKE_CASE__											=			layer(**lowerCAmelCase_	)

        # Allocate space for the output
        if out is None:
            SCREAMING_SNAKE_CASE__											=			tensor_tree_map(lambda lowerCAmelCase_	: t.new_zeros((flat_batch_dim,) + t.shape[1:]	) ,							lowerCAmelCase_	)

        # Put the chunk in its pre-allocated space
        if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):

            def assign(lowerCAmelCase_ ,							lowerCAmelCase_	) -> None:
                for k, v in da.items():
                    if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
                        assign(lowerCAmelCase_ ,							da[k]	)
                    else:
                        if _add_into_out:
                            v[i : i + chunk_size] += da[k]
                        else:
                            SCREAMING_SNAKE_CASE__											=			da[k]

            assign(lowerCAmelCase_ ,							lowerCAmelCase_	)
        elif isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
            for xa, xa in zip(lowerCAmelCase_ ,							lowerCAmelCase_	):
                if _add_into_out:
                    xa[i : i + chunk_size] += xa
                else:
                    SCREAMING_SNAKE_CASE__											=			xa
        elif isinstance(lowerCAmelCase_ ,							torch.Tensor	):
            if _add_into_out:
                out[i : i + chunk_size] += output_chunk
            else:
                SCREAMING_SNAKE_CASE__											=			output_chunk
        else:
            raise ValueError('''Not supported'''	)

        i += chunk_size

    SCREAMING_SNAKE_CASE__											=			tensor_tree_map(lambda lowerCAmelCase_	: t.view(orig_batch_dims + t.shape[1:]	) ,							lowerCAmelCase_	)

    return out



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_ = 5_12   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			max_chunk_size
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        logging.info('''Tuning chunk size...'''     )

        if min_chunk_size >= self.max_chunk_size:
            return min_chunk_size

        SCREAMING_SNAKE_CASE__											=			[2**l for l in range(int(math.log(self.max_chunk_size   ,				2     )     ) + 1     )]
        SCREAMING_SNAKE_CASE__											=			[c for c in candidates if c > min_chunk_size]
        SCREAMING_SNAKE_CASE__											=			[min_chunk_size] + candidates
        candidates[-1] += 4

        def test_chunk_size(A_     ) -> bool:
            try:
                with torch.no_grad():
                    fn(*A_   ,				chunk_size=A_     )
                return True
            except RuntimeError:
                return False

        SCREAMING_SNAKE_CASE__											=			0
        SCREAMING_SNAKE_CASE__											=			len(A_     ) - 1
        while i > min_viable_chunk_size_index:
            SCREAMING_SNAKE_CASE__											=			test_chunk_size(candidates[i]     )
            if not viable:
                SCREAMING_SNAKE_CASE__											=			(min_viable_chunk_size_index + i) // 2
            else:
                SCREAMING_SNAKE_CASE__											=			i
                SCREAMING_SNAKE_CASE__											=			(i + len(A_     ) - 1) // 2

        return candidates[min_viable_chunk_size_index]



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			True
        for aa, aa in zip(A_   ,				A_     ):
            assert type(A_     ) == type(A_     )
            if isinstance(A_   ,				(list, tuple)     ):
                consistent &= self._compare_arg_caches(A_   ,				A_     )
            elif isinstance(A_   ,				A_     ):
                SCREAMING_SNAKE_CASE__											=			[v for _, v in sorted(aa.items()   ,				key=lambda A_     : x[0]     )]
                SCREAMING_SNAKE_CASE__											=			[v for _, v in sorted(aa.items()   ,				key=lambda A_     : x[0]     )]
                consistent &= self._compare_arg_caches(A_   ,				A_     )
            else:
                consistent &= aa == aa

        return consistent




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			tree_map(lambda A_     : a.shape if isinstance(A_   ,				torch.Tensor     ) else a   ,				A_   ,				A_     )
        if self.cached_arg_data is not None:
            # If args have changed shape/value, we need to re-tune
            assert len(self.cached_arg_data     ) == len(A_     )
            SCREAMING_SNAKE_CASE__											=			self._compare_arg_caches(self.cached_arg_data   ,				A_     )
        else:
            # Otherwise, we can reuse the precomputed value
            SCREAMING_SNAKE_CASE__											=			False

        if not consistent:
            SCREAMING_SNAKE_CASE__											=			self._determine_favorable_chunk_size(
                A_   ,				A_   ,				A_   ,				)
            SCREAMING_SNAKE_CASE__											=			arg_data

        assert self.cached_chunk_size is not None

        return self.cached_chunk_size







'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      List[Any]						      ={
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip_2_vision_model"""
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=14_08		,			lowercase_ :int=61_44		,			lowercase_ :List[str]=39		,			lowercase_ :List[Any]=16		,			lowercase_ :Union[str, Any]=2_24		,			lowercase_ :List[str]=14		,			lowercase_ :Dict="gelu"		,			lowercase_ :Tuple=0.0_0_0_0_1		,			lowercase_ :Dict=0.0		,			lowercase_ :Optional[int]=1E-10		,			lowercase_ :Optional[Any]=True		,			**lowercase_ :List[str]		,			)->				int:
     super().__init__(**lowercase_				)

     A__							=     hidden_size
     A__							=     intermediate_size
     A__							=     num_hidden_layers
     A__							=     num_attention_heads
     A__							=     patch_size
     A__							=     image_size
     A__							=     initializer_range
     A__							=     attention_dropout
     A__							=     layer_norm_eps
     A__							=     hidden_act
     A__							=     qkv_bias



 @classmethod
 def 	UpperCAmelCase_				(							cls :Optional[Any]		,			lowercase_ :Union[str, os.PathLike]		,			**lowercase_ :Optional[Any]				)->				"PretrainedConfig":
     cls._set_token_in_kwargs(lowercase_				)

     A__,      A__							=     cls.get_config_dict(lowercase_		,			**lowercase_				)

     # get the vision config dict if we are loading from Blip2Config
     if config_dict.get("model_type"				) == "blip-2":
         A__							=     config_dict["vision_config"]

     if "model_type" in config_dict and hasattr(cls		,			"model_type"				) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

     return cls.from_dict(lowercase_		,			**lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip_2_qformer"""
 def __init__(							self :Dict		,			lowercase_ :int=3_05_22		,			lowercase_ :Union[str, Any]=7_68		,			lowercase_ :Tuple=12		,			lowercase_ :Union[str, Any]=12		,			lowercase_ :str=30_72		,			lowercase_ :Any="gelu"		,			lowercase_ :Optional[int]=0.1		,			lowercase_ :List[str]=0.1		,			lowercase_ :Dict=5_12		,			lowercase_ :Optional[int]=0.0_2		,			lowercase_ :Dict=1E-12		,			lowercase_ :List[Any]=0		,			lowercase_ :str="absolute"		,			lowercase_ :List[str]=2		,			lowercase_ :Tuple=14_08		,			**lowercase_ :Tuple		,			)->				List[Any]:
     super().__init__(pad_token_id=lowercase_		,			**lowercase_				)

     A__							=     vocab_size
     A__							=     hidden_size
     A__							=     num_hidden_layers
     A__							=     num_attention_heads
     A__							=     hidden_act
     A__							=     intermediate_size
     A__							=     hidden_dropout_prob
     A__							=     attention_probs_dropout_prob
     A__							=     max_position_embeddings
     A__							=     initializer_range
     A__							=     layer_norm_eps
     A__							=     position_embedding_type
     A__							=     cross_attention_frequency
     A__							=     encoder_hidden_size



 @classmethod
 def 	UpperCAmelCase_				(							cls :int		,			lowercase_ :Union[str, os.PathLike]		,			**lowercase_ :str				)->				"PretrainedConfig":
     cls._set_token_in_kwargs(lowercase_				)

     A__,      A__							=     cls.get_config_dict(lowercase_		,			**lowercase_				)

     # get the qformer config dict if we are loading from Blip2Config
     if config_dict.get("model_type"				) == "blip-2":
         A__							=     config_dict["qformer_config"]

     if "model_type" in config_dict and hasattr(cls		,			"model_type"				) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

     return cls.from_dict(lowercase_		,			**lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip-2"""
 __lowercase  						=							True
 def __init__(							self :int		,			lowercase_ :List[Any]=None		,			lowercase_ :Dict=None		,			lowercase_ :Tuple=None		,			lowercase_ :List[str]=32		,			**lowercase_ :Any				)->				Union[str, Any]:
     super().__init__(**lowercase_				)

     if vision_config is None:
         A__							=     {}
         logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."				)

     if qformer_config is None:
         A__							=     {}
         logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."				)

     if text_config is None:
         A__							=     {}
         logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."				)

     A__							=     BlipaVisionConfig(**lowercase_				)
     A__							=     BlipaQFormerConfig(**lowercase_				)
     A__							=     text_config["model_type"] if "model_type" in text_config else "opt"
     A__							=     CONFIG_MAPPING[text_model_type](**lowercase_				)

     A__							=     self.text_config.tie_word_embeddings
     A__							=     self.text_config.is_encoder_decoder

     A__							=     num_query_tokens
     A__							=     self.vision_config.hidden_size
     A__							=     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
     A__							=     1.0
     A__							=     0.0_2
 @classmethod
 def 	UpperCAmelCase_				(							cls :Optional[Any]		,			lowercase_ :BlipaVisionConfig		,			lowercase_ :BlipaQFormerConfig		,			lowercase_ :PretrainedConfig		,			**lowercase_ :Any		,			)->				Optional[int]:
     return cls(
         vision_config=vision_config.to_dict()		,			qformer_config=qformer_config.to_dict()		,			text_config=text_config.to_dict()		,			**lowercase_		,			)



 def 	UpperCAmelCase_				(							self :Dict				)->				Any:
     A__							=     copy.deepcopy(self.__dict__				)
     A__							=     self.vision_config.to_dict()
     A__							=     self.qformer_config.to_dict()
     A__							=     self.text_config.to_dict()
     A__							=     self.__class__.model_type
     return output



