
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
__magic_name__  :  str     			=   'src/diffusers'
__magic_name__  :  Any     			=   'docs/source/en'
__magic_name__  :  Any     			=   '.'





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      int				,					snake_case__   :      Optional[int] )->			Optional[int]:
    with open(snake_case__				,					"r"				,					encoding="utf-8"				,					newline="\n" ) as f:
        A_								=					f.readlines()
    # Find the start prompt.
    A_								=					0
    while not lines[start_index].startswith(snake_case__ ):
        start_index += 1
    start_index += 1

    A_								=					start_index
    while not lines[end_index].startswith(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 |
__magic_name__  :  Union[str, Any]     			=   'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__magic_name__  :  str     			=   re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__magic_name__  :  Optional[int]     			=   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.
__magic_name__  :  Any     			=   re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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





def        lowerCAmelCase       (				snake_case__   :      Any )->			Any:
    A_								=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"				,					snake_case__ )
    return [m.group(0 ) for m in matches]





def        lowerCAmelCase       (				snake_case__   :      Optional[Any]				,					snake_case__   :      Any )->			Union[str, Any]:
    A_								=					2 if text == "✅" or text == "❌" else len(snake_case__ )
    A_								=					(width - text_length) // 2
    A_								=					width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent





def        lowerCAmelCase       (				)->			Any:
    A_								=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    A_								=					{
        name: config_mapping_names[code]
        for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
        if code in config_mapping_names
    }
    A_								=					{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.
    A_								=					collections.defaultdict(snake_case__ )
    A_								=					collections.defaultdict(snake_case__ )
    A_								=					collections.defaultdict(snake_case__ )
    A_								=					collections.defaultdict(snake_case__ )
    A_								=					collections.defaultdict(snake_case__ )

    # Let's lookup through all diffusers object (once).
    for attr_name in dir(snake_case__ ):
        A_								=					None
        if attr_name.endswith("Tokenizer" ):
            A_								=					slow_tokenizers
            A_								=					attr_name[:-9]
        elif attr_name.endswith("TokenizerFast" ):
            A_								=					fast_tokenizers
            A_								=					attr_name[:-13]
        elif _re_tf_models.match(snake_case__ ) is not None:
            A_								=					tf_models
            A_								=					_re_tf_models.match(snake_case__ ).groups()[0]
        elif _re_flax_models.match(snake_case__ ) is not None:
            A_								=					flax_models
            A_								=					_re_flax_models.match(snake_case__ ).groups()[0]
        elif _re_pt_models.match(snake_case__ ) is not None:
            A_								=					pt_models
            A_								=					_re_pt_models.match(snake_case__ ).groups()[0]

        if lookup_dict is not None:
            while len(snake_case__ ) > 0:
                if attr_name in model_name_to_prefix.values():
                    A_								=					True
                    break
                # Try again after removing the last word in the name
                A_								=					"".join(camel_case_split(snake_case__ )[:-1] )

    # Let's build that table!
    A_								=					list(model_name_to_config.keys() )
    model_names.sort(key=str.lower )
    A_								=					["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).
    A_								=					[len(snake_case__ ) + 2 for c in columns]
    A_								=					max([len(snake_case__ ) for name in model_names] ) + 2

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

    A_								=					{True: "✅", False: "❌"}
    for name in model_names:
        A_								=					model_name_to_prefix[name]
        A_								=					[
            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(snake_case__				,					snake_case__ ) for l, w in zip(snake_case__				,					snake_case__ )] ) + "|\n"
    return table





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any]=False )->			List[Any]:
    A_	,	A_	,	A_	,	A_								=					_find_text_in_file(
        filename=os.path.join(snake_case__				,					"index.mdx" )				,					start_prompt="<!--This table is updated automatically from the auto modules"				,					end_prompt="<!-- End table-->"				,					)
    A_								=					get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(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__":
     __magic_name__  :  List[Any]     			=   argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     __magic_name__  :  str     			=   parser.parse_args()

     check_model_table(args.fix_and_overwrite)








import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
__magic_name__  :  List[str]     			=   logging.getLogger(__name__)





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			Optional[Any]:
    np.random.seed(args.seed )
    torch.manual_seed(args.seed )
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed )





def        lowerCAmelCase       (				)->			int:
    A_								=					argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path"				,					default=snake_case__				,					type=snake_case__				,					required=snake_case__				,					)
    parser.add_argument("--prompt"				,					type=snake_case__				,					default="" )
    parser.add_argument("--length"				,					type=snake_case__				,					default=20 )
    parser.add_argument("--stop_token"				,					type=snake_case__				,					default=snake_case__				,					help="Token at which text generation is stopped" )
    parser.add_argument(
        "--temperature"				,					type=snake_case__				,					default=1.0				,					help="temperature of 1.0 has no effect, lower tend toward greedy sampling"				,					)
    parser.add_argument(
        "--repetition_penalty"				,					type=snake_case__				,					default=1.0				,					help="primarily useful for CTRL model; in that case, use 1.2" )
    parser.add_argument("--k"				,					type=snake_case__				,					default=0 )
    parser.add_argument("--penalty_alpha"				,					type=snake_case__				,					default=0.0 )
    parser.add_argument("--p"				,					type=snake_case__				,					default=0.9 )

    parser.add_argument("--prefix"				,					type=snake_case__				,					default=""				,					help="Text added prior to input." )
    parser.add_argument("--padding_text"				,					type=snake_case__				,					default=""				,					help="Deprecated, the use of `--prefix` is preferred." )
    parser.add_argument("--xlm_language"				,					type=snake_case__				,					default=""				,					help="Optional language when used with the XLM model." )

    parser.add_argument("--seed"				,					type=snake_case__				,					default=42				,					help="random seed for initialization" )
    parser.add_argument("--no_cuda"				,					action="store_true"				,					help="Avoid using CUDA when available" )
    parser.add_argument(
        "--fp16"				,					action="store_true"				,					help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"				,					)
    A_								=					parser.parse_args()

    A_								=					torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
    A_								=					0 if args.no_cuda else torch.cuda.device_count()

    logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}' )

    set_seed(snake_case__ )

    # Initialize the model and tokenizer
    A_								=					AutoTokenizer.from_pretrained(args.model_name_or_path )
    A_								=					AutoModelForCausalLM.from_pretrained(args.model_name_or_path )

    # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
    # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
    model.to(args.device )

    if args.fpaa:
        model.half()

    logger.info(snake_case__ )
    A_								=					args.prompt if args.prompt else input("Model prompt >>> " )

    A_								=					tokenizer(snake_case__				,					return_tensors="pt"				,					add_special_tokens=snake_case__ )
    A_								=					{key: value.to(args.device ) for key, value in inputs.items()}

    A_								=					model.generate(
        **snake_case__				,					max_length=args.length + len(inputs["input_ids"][0] )				,					penalty_alpha=args.penalty_alpha				,					top_k=args.k				,					)

    A_								=					[]
    for generated_sequence_idx, generated_sequence in enumerate(snake_case__ ):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===' )
        A_								=					generated_sequence.tolist()

        # Decode text
        A_								=					tokenizer.decode(snake_case__				,					clean_up_tokenization_spaces=snake_case__				,					add_special_tokens=snake_case__ )

        # Remove all text after the stop token
        A_								=					text[: text.find(args.stop_token ) if args.stop_token else None]

        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
        A_								=					(
            prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]				,					clean_up_tokenization_spaces=snake_case__ ) ) :]
        )

        generated_sequences.append(snake_case__ )
        print(snake_case__ )

    return generated_sequences


if __name__ == "__main__":
     main()






