





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 							snake_case_		(      _A    ,       _A    ,       _A):







		@register_to_config
		def __init__(		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase = False      ,  )					-> Any:
				super().__init__()

				lowerCamelCase :			str    		=nn.Linear(__lowercase      ,  __lowercase      ,  bias=__lowercase )

				lowerCamelCase :			Optional[Any]    		=nn.Embedding(__lowercase      ,  __lowercase )
				lowerCamelCase :			Tuple    		=False

				lowerCamelCase :			Optional[Any]    		=nn.Dropout(p=__lowercase )

				lowerCamelCase :			Tuple    		=TaConfig(
				    d_model=__lowercase      ,  num_heads=__lowercase      ,  d_kv=__lowercase      ,  d_ff=__lowercase      ,  feed_forward_proj=__lowercase      ,  dropout_rate=__lowercase      ,  is_decoder=__lowercase      ,  is_encoder_decoder=__lowercase      ,  )
				lowerCamelCase :			Union[str, Any]    		=nn.ModuleList()
				for lyr_num in range(__lowercase ):
						lowerCamelCase :			Any    		=TaBlock(__lowercase )
						self.encoders.append(__lowercase )

				lowerCamelCase :			List[Any]    		=TaLayerNorm(__lowercase )
				lowerCamelCase :			Dict    		=nn.Dropout(p=__lowercase )







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase )					-> int:
				lowerCamelCase :			List[str]    		=self.input_proj(__lowercase )

				# terminal relative positional encodings
				lowerCamelCase :			Tuple    		=encoder_inputs.shape[1]
				lowerCamelCase :			Dict    		=torch.arange(__lowercase      ,  device=encoder_inputs.device )

				lowerCamelCase :			int    		=encoder_inputs_mask.sum(-1 )
				lowerCamelCase :			List[Any]    		=torch.roll(input_positions.unsqueeze(0 )      ,  tuple(seq_lens.tolist() )      ,  dims=0 )
				x += self.position_encoding(__lowercase )

				lowerCamelCase :			str    		=self.dropout_pre(__lowercase )

				# inverted the attention mask
				lowerCamelCase :			Optional[Any]    		=encoder_inputs.size()
				lowerCamelCase :			Optional[Any]    		=self.get_extended_attention_mask(__lowercase      ,  __lowercase )

				for lyr in self.encoders:
						lowerCamelCase :			List[str]    		=lyr(__lowercase      ,  __lowercase )[0]
				lowerCamelCase :			Optional[int]    		=self.layer_norm(__lowercase )

				return self.dropout_post(__lowercase ), encoder_inputs_mask








import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
snake_case_				=      '''src/diffusers'''
snake_case_				=      '''docs/source/en'''
snake_case_				=      '''.'''


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		with open(SCREAMING_SNAKE_CASE_				,							'''r'''				,							encoding='''utf-8'''				,							newline='''\n'''		) as f:
				lowerCamelCase :			List[str]    		=f.readlines()
		# Find the start prompt.
		lowerCamelCase :			List[Any]    		=0
		while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_		):
				start_index += 1
		start_index += 1

		lowerCamelCase :			Dict    		=start_index
		while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_		):
				end_index += 1
		end_index -= 1

		while len(lines[start_index]		) <= 1:
				start_index += 1
		while len(lines[end_index]		) <= 1:
				end_index -= 1
		end_index += 1
		return "".join(lines[start_index:end_index]		), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
snake_case_				=      '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
snake_case_				=      re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
snake_case_				=      re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
snake_case_				=      re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the diffusers module imported is the one in the repo.
snake_case_				=      importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(TRANSFORMERS_PATH, '''__init__.py'''),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
snake_case_				=      spec.loader.load_module()


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  Tuple:
		lowerCamelCase :			Optional[Any]    		=re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''				,							SCREAMING_SNAKE_CASE_		)
		return [m.group(0		) for m in matches]


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		lowerCamelCase :			Dict    		=2 if text == '''✅''' or text == '''❌''' else len(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Union[str, Any]    		=(width - text_length) // 2
		lowerCamelCase :			List[str]    		=width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent


def        A__						(							) ->  Union[str, Any]:
		lowerCamelCase :			List[str]    		=diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		lowerCamelCase :			List[Any]    		={
		    name: config_mapping_names[code]
		    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
		    if code in config_mapping_names
		}
		lowerCamelCase :			Optional[int]    		={name: config.replace('''ConfigMixin'''				,							''''''		) for name, config in model_name_to_config.items()}

		# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			str    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Any    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)

		# Let's lookup through all diffusers object (once).
		for attr_name in dir(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase :			Union[str, Any]    		=None
				if attr_name.endswith('''Tokenizer'''		):
						lowerCamelCase :			List[str]    		=slow_tokenizers
						lowerCamelCase :			List[Any]    		=attr_name[:-9]
				elif attr_name.endswith('''TokenizerFast'''		):
						lowerCamelCase :			Any    		=fast_tokenizers
						lowerCamelCase :			Optional[Any]    		=attr_name[:-1_3]
				elif _re_tf_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			Optional[Any]    		=tf_models
						lowerCamelCase :			Any    		=_re_tf_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]
				elif _re_flax_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			List[str]    		=flax_models
						lowerCamelCase :			int    		=_re_flax_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]
				elif _re_pt_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			List[Any]    		=pt_models
						lowerCamelCase :			List[Any]    		=_re_pt_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]

				if lookup_dict is not None:
						while len(SCREAMING_SNAKE_CASE_		) > 0:
								if attr_name in model_name_to_prefix.values():
										lowerCamelCase :			Optional[Any]    		=True
										break
								# Try again after removing the last word in the name
								lowerCamelCase :			List[str]    		=''''''.join(camel_case_split(SCREAMING_SNAKE_CASE_		)[:-1]		)

    # Let's build that table!
		lowerCamelCase :			Optional[int]    		=list(model_name_to_config.keys()		)
		model_names.sort(key=str.lower		)
		lowerCamelCase :			Union[str, Any]    		=['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
		# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
		lowerCamelCase :			Tuple    		=[len(SCREAMING_SNAKE_CASE_		) + 2 for c in columns]
		lowerCamelCase :			Dict    		=max([len(SCREAMING_SNAKE_CASE_		) for name in model_names]		) + 2

		# Build the table per se
		lowerCamelCase :			List[Any]    		='''|''' + '''|'''.join([_center_text(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) for c, w in zip(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)]		) + '''|\n'''
		# Use ":-----:" format to center-aligned table cell texts
		table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]		) + "|\n"

		lowerCamelCase :			Optional[Any]    		={True: '''✅''', False: '''❌'''}
		for name in model_names:
				lowerCamelCase :			str    		=model_name_to_prefix[name]
				lowerCamelCase :			Dict    		=[
				    name,
				    check[slow_tokenizers[prefix]],
				    check[fast_tokenizers[prefix]],
				    check[pt_models[prefix]],
				    check[tf_models[prefix]],
				    check[flax_models[prefix]],
				]
				table += "|" + "|".join([_center_text(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) for l, w in zip(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)]		) + "|\n"
		return table


def        A__						(							SCREAMING_SNAKE_CASE_=False		) ->  int:
		lowerCamelCase		, lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Tuple    		=_find_text_in_file(
		    filename=os.path.join(SCREAMING_SNAKE_CASE_				,							'''index.mdx'''		)				,							start_prompt='''<!--This table is updated automatically from the auto modules'''				,							end_prompt='''<!-- End table-->'''				,							)
		lowerCamelCase :			List[Any]    		=get_model_table_from_auto_modules()

		if current_table != new_table:
				if overwrite:
						with open(os.path.join(SCREAMING_SNAKE_CASE_				,							'''index.mdx'''		)				,							'''w'''				,							encoding='''utf-8'''				,							newline='''\n'''		) as f:
								f.writelines(lines[:start_index] + [new_table] + lines[end_index:]		)
				else:
						raise ValueError(
						    '''The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'''		)


if __name__ == "__main__":
				snake_case_				=      argparse.ArgumentParser()
				parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
				snake_case_				=      parser.parse_args()

				check_model_table(args.fix_and_overwrite)

