



from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


_lowerCamelCase         =	{
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase         =	[
          """Constraint""",
          """ConstraintListState""",
          """DisjunctiveConstraint""",
          """PhrasalConstraint""",
      ]
      _lowerCamelCase         =	[
          """BeamHypotheses""",
          """BeamScorer""",
          """BeamSearchScorer""",
          """ConstrainedBeamSearchScorer""",
      ]
      _lowerCamelCase         =	[
          """EpsilonLogitsWarper""",
          """EtaLogitsWarper""",
          """ForcedBOSTokenLogitsProcessor""",
          """ForcedEOSTokenLogitsProcessor""",
          """HammingDiversityLogitsProcessor""",
          """InfNanRemoveLogitsProcessor""",
          """LogitsProcessor""",
          """LogitsProcessorList""",
          """LogitsWarper""",
          """MinLengthLogitsProcessor""",
          """MinNewTokensLengthLogitsProcessor""",
          """NoBadWordsLogitsProcessor""",
          """NoRepeatNGramLogitsProcessor""",
          """PrefixConstrainedLogitsProcessor""",
          """RepetitionPenaltyLogitsProcessor""",
          """SequenceBiasLogitsProcessor""",
          """EncoderRepetitionPenaltyLogitsProcessor""",
          """TemperatureLogitsWarper""",
          """TopKLogitsWarper""",
          """TopPLogitsWarper""",
          """TypicalLogitsWarper""",
          """EncoderNoRepeatNGramLogitsProcessor""",
          """ExponentialDecayLengthPenalty""",
          """LogitNormalization""",
      ]
      _lowerCamelCase         =	[
          """MaxNewTokensCriteria""",
          """MaxLengthCriteria""",
          """MaxTimeCriteria""",
          """StoppingCriteria""",
          """StoppingCriteriaList""",
          """validate_stopping_criteria""",
      ]
      _lowerCamelCase         =	[
          """GenerationMixin""",
          """top_k_top_p_filtering""",
          """GreedySearchEncoderDecoderOutput""",
          """GreedySearchDecoderOnlyOutput""",
          """SampleEncoderDecoderOutput""",
          """SampleDecoderOnlyOutput""",
          """BeamSearchEncoderDecoderOutput""",
          """BeamSearchDecoderOnlyOutput""",
          """BeamSampleEncoderDecoderOutput""",
          """BeamSampleDecoderOnlyOutput""",
          """ContrastiveSearchEncoderDecoderOutput""",
          """ContrastiveSearchDecoderOnlyOutput""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase         =	[
          """TFForcedBOSTokenLogitsProcessor""",
          """TFForcedEOSTokenLogitsProcessor""",
          """TFLogitsProcessor""",
          """TFLogitsProcessorList""",
          """TFLogitsWarper""",
          """TFMinLengthLogitsProcessor""",
          """TFNoBadWordsLogitsProcessor""",
          """TFNoRepeatNGramLogitsProcessor""",
          """TFRepetitionPenaltyLogitsProcessor""",
          """TFTemperatureLogitsWarper""",
          """TFTopKLogitsWarper""",
          """TFTopPLogitsWarper""",
          """TFForceTokensLogitsProcessor""",
          """TFSuppressTokensAtBeginLogitsProcessor""",
          """TFSuppressTokensLogitsProcessor""",
      ]
      _lowerCamelCase         =	[
          """TFGenerationMixin""",
          """tf_top_k_top_p_filtering""",
          """TFGreedySearchDecoderOnlyOutput""",
          """TFGreedySearchEncoderDecoderOutput""",
          """TFSampleEncoderDecoderOutput""",
          """TFSampleDecoderOnlyOutput""",
          """TFBeamSearchEncoderDecoderOutput""",
          """TFBeamSearchDecoderOnlyOutput""",
          """TFBeamSampleEncoderDecoderOutput""",
          """TFBeamSampleDecoderOnlyOutput""",
          """TFContrastiveSearchEncoderDecoderOutput""",
          """TFContrastiveSearchDecoderOnlyOutput""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase         =	[
          """FlaxForcedBOSTokenLogitsProcessor""",
          """FlaxForcedEOSTokenLogitsProcessor""",
          """FlaxLogitsProcessor""",
          """FlaxLogitsProcessorList""",
          """FlaxLogitsWarper""",
          """FlaxMinLengthLogitsProcessor""",
          """FlaxTemperatureLogitsWarper""",
          """FlaxTopKLogitsWarper""",
          """FlaxTopPLogitsWarper""",
      ]
      _lowerCamelCase         =	[
          """FlaxGenerationMixin""",
          """FlaxGreedySearchOutput""",
          """FlaxSampleOutput""",
          """FlaxBeamSearchOutput""",
      ]

if TYPE_CHECKING:
      from .configuration_utils import GenerationConfig
      from .streamers import TextIteratorStreamer, TextStreamer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
            from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
            from .logits_process import (
                EncoderNoRepeatNGramLogitsProcessor,
                EncoderRepetitionPenaltyLogitsProcessor,
                EpsilonLogitsWarper,
                EtaLogitsWarper,
                ExponentialDecayLengthPenalty,
                ForcedBOSTokenLogitsProcessor,
                ForcedEOSTokenLogitsProcessor,
                HammingDiversityLogitsProcessor,
                InfNanRemoveLogitsProcessor,
                LogitNormalization,
                LogitsProcessor,
                LogitsProcessorList,
                LogitsWarper,
                MinLengthLogitsProcessor,
                MinNewTokensLengthLogitsProcessor,
                NoBadWordsLogitsProcessor,
                NoRepeatNGramLogitsProcessor,
                PrefixConstrainedLogitsProcessor,
                RepetitionPenaltyLogitsProcessor,
                SequenceBiasLogitsProcessor,
                TemperatureLogitsWarper,
                TopKLogitsWarper,
                TopPLogitsWarper,
                TypicalLogitsWarper,
            )
            from .stopping_criteria import (
                MaxLengthCriteria,
                MaxNewTokensCriteria,
                MaxTimeCriteria,
                StoppingCriteria,
                StoppingCriteriaList,
                validate_stopping_criteria,
            )
            from .utils import (
                BeamSampleDecoderOnlyOutput,
                BeamSampleEncoderDecoderOutput,
                BeamSearchDecoderOnlyOutput,
                BeamSearchEncoderDecoderOutput,
                ContrastiveSearchDecoderOnlyOutput,
                ContrastiveSearchEncoderDecoderOutput,
                GenerationMixin,
                GreedySearchDecoderOnlyOutput,
                GreedySearchEncoderDecoderOutput,
                SampleDecoderOnlyOutput,
                SampleEncoderDecoderOutput,
                top_k_top_p_filtering,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tf_logits_process import (
                TFForcedBOSTokenLogitsProcessor,
                TFForcedEOSTokenLogitsProcessor,
                TFForceTokensLogitsProcessor,
                TFLogitsProcessor,
                TFLogitsProcessorList,
                TFLogitsWarper,
                TFMinLengthLogitsProcessor,
                TFNoBadWordsLogitsProcessor,
                TFNoRepeatNGramLogitsProcessor,
                TFRepetitionPenaltyLogitsProcessor,
                TFSuppressTokensAtBeginLogitsProcessor,
                TFSuppressTokensLogitsProcessor,
                TFTemperatureLogitsWarper,
                TFTopKLogitsWarper,
                TFTopPLogitsWarper,
            )
            from .tf_utils import (
                TFBeamSampleDecoderOnlyOutput,
                TFBeamSampleEncoderDecoderOutput,
                TFBeamSearchDecoderOnlyOutput,
                TFBeamSearchEncoderDecoderOutput,
                TFContrastiveSearchDecoderOnlyOutput,
                TFContrastiveSearchEncoderDecoderOutput,
                TFGenerationMixin,
                TFGreedySearchDecoderOnlyOutput,
                TFGreedySearchEncoderDecoderOutput,
                TFSampleDecoderOnlyOutput,
                TFSampleEncoderDecoderOutput,
                tf_top_k_top_p_filtering,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .flax_logits_process import (
                FlaxForcedBOSTokenLogitsProcessor,
                FlaxForcedEOSTokenLogitsProcessor,
                FlaxLogitsProcessor,
                FlaxLogitsProcessorList,
                FlaxLogitsWarper,
                FlaxMinLengthLogitsProcessor,
                FlaxTemperatureLogitsWarper,
                FlaxTopKLogitsWarper,
                FlaxTopPLogitsWarper,
            )
            from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
      import sys

      _lowerCamelCase         =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










'''simple docstring'''




import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor





def 				a_							(    )      ->List[Any]:






				'''simple docstring'''

				__lowerCamelCase		:   Optional[Any]           =       set()
				# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
				for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
								__lowerCamelCase		:   Dict           =       getattr(transformers.models.auto				,    lowercase				,    lowercase     )
								if module is None:
												continue
								# all mappings in a single auto modeling file
								__lowerCamelCase		:   Any           =       [
								    x
								    for x in dir(lowercase     )
								    if x.endswith("_MAPPING_NAMES"     )
								    and (x.startswith("MODEL_"     ) or x.startswith("TF_MODEL_"     ) or x.startswith("FLAX_MODEL_"     ))
								]
								for name in mapping_names:
												__lowerCamelCase		:   Union[str, Any]           =       getattr(lowercase				,    lowercase     )
												if mapping is not None:
																for v in mapping.values():
																				if isinstance(lowercase				,    (list, tuple)     ):
																								model_names.update(lowercase     )
																				elif isinstance(lowercase				,    lowercase     ):
																								model_names.add(lowercase     )

				return sorted(lowercase     )





def 				a_							(    )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   Tuple           =       set(get_all_model_names()     )

				with open("tests/utils/tiny_model_summary.json"     ) as fp:
								__lowerCamelCase		:   Optional[int]           =       json.load(lowercase     )
				__lowerCamelCase		:   Union[str, Any]           =       set()
				for model_base_name in tiny_model_info:
								tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"]     )

				# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
				__lowerCamelCase		:   List[str]           =       model_names.difference(lowercase     )
				for model_name in copy.copy(lowercase     ):
								if not model_name.startswith("TF"     ) and F'TF{model_name}' in not_on_hub:
												tiny_models_names.remove(lowercase     )
								elif model_name.startswith("TF"     ) and model_name[2:] in not_on_hub:
												tiny_models_names.remove(lowercase     )

				return sorted(lowercase     )





def 				a_							(    lowercase     )      ->str:






				'''simple docstring'''

				__lowerCamelCase		:   Optional[int]           =       COMPOSITE_MODELS.values()

				# All tiny model base names on Hub
				__lowerCamelCase		:   List[Any]           =       get_all_model_names()
				__lowerCamelCase		:   Any           =       hf_api.list_models(
				    filter=ModelFilter(
				        author="hf-internal-testing"				,    )     )
				__lowerCamelCase		:   str           =       set()
				for x in models:
								__lowerCamelCase		:   Any           =       x.modelId
								__lowerCamelCase    ,       __lowerCamelCase		:   List[Any]           =       model.split("/"     )
								if not model.startswith("tiny-random-"     ):
												continue
								__lowerCamelCase		:   int           =       model.replace("tiny-random-"				,    ""     )
								if not model[0].isupper():
												continue
								if model not in model_names and model not in special_models:
												continue
								_models.add(lowercase     )

				__lowerCamelCase		:   Tuple           =       sorted(_models     )
				# All tiny model names on Hub
				__lowerCamelCase		:   Union[str, Any]           =       {}
				for model in models:
								__lowerCamelCase		:   List[str]           =       F'hf-internal-testing/tiny-random-{model}'
								__lowerCamelCase		:   Any           =       model.split("-"     )[0]
								try:
												__lowerCamelCase		:   str           =       hf_api.repo_info(lowercase     )
												__lowerCamelCase		:   str           =       {
												    "tokenizer_classes": set(),
												    "processor_classes": set(),
												    "model_classes": set(),
												    "sha": repo_info.sha,
												}
								except Exception:
												continue
								try:
												time.sleep(1     )
												__lowerCamelCase		:   Optional[int]           =       AutoTokenizer.from_pretrained(lowercase     )
												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												__lowerCamelCase		:   List[Any]           =       AutoTokenizer.from_pretrained(lowercase				,    use_fast=lowercase     )
												content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												__lowerCamelCase		:   List[Any]           =       AutoImageProcessor.from_pretrained(lowercase     )
												content["processor_classes"].add(img_p.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												__lowerCamelCase		:   Tuple           =       AutoFeatureExtractor.from_pretrained(lowercase     )
												if not isinstance(lowercase				,    lowercase     ):
																content["processor_classes"].add(feat_p.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												__lowerCamelCase		:   Optional[Any]           =       getattr(lowercase				,    lowercase     )
												__lowerCamelCase		:   Optional[Any]           =       model_class.from_pretrained(lowercase     )
												content["model_classes"].add(m.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												__lowerCamelCase		:   List[str]           =       getattr(lowercase				,    F'TF{model}'     )
												__lowerCamelCase		:   Dict           =       model_class.from_pretrained(lowercase     )
												content["model_classes"].add(m.__class__.__name__     )
								except Exception:
												pass

								__lowerCamelCase		:   List[Any]           =       sorted(content["tokenizer_classes"]     )
								__lowerCamelCase		:   str           =       sorted(content["processor_classes"]     )
								__lowerCamelCase		:   Union[str, Any]           =       sorted(content["model_classes"]     )

								__lowerCamelCase		:   Tuple           =       content
								with open(os.path.join(lowercase				,    "hub_tiny_model_summary.json"     )				,    "w"     ) as fp:
												json.dump(lowercase				,    lowercase				,    ensure_ascii=lowercase				,    indent=4     )


if __name__ == "__main__":
				__lowercase	:   Any   		=					argparse.ArgumentParser()
				parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
				__lowercase	:   Optional[int]   		=					parser.parse_args()

				# This has to be `spawn` to avoid hanging forever!
				multiprocessing.set_start_method("""spawn""")

				__lowercase	:   str   		=					"""tiny_models"""
				__lowercase	:   List[Any]   		=					True
				__lowercase	:   Optional[int]   		=					None
				__lowercase	:   List[Any]   		=					get_tiny_model_names_from_repo()
				__lowercase	:   List[Any]   		=					True
				__lowercase	:   Dict   		=					True
				__lowercase	:   Tuple   		=					"""hf-internal-testing"""

				create_tiny_models(
				    output_path,
				    all,
				    model_types,
				    models_to_skip,
				    no_check,
				    upload,
				    organization,
				    token=os.environ.get("""TOKEN""", None),
				    num_workers=args.num_workers,
				)





