
'''simple docstring'''
import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Dict   	=  FNetConfig.from_json_file(__UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)
    snake_case_       :			Optional[int]   	=  FNetForPreTraining(__UpperCAmelCase				)

    snake_case_       :			Any   	=  restore_checkpoint(__UpperCAmelCase,						__UpperCAmelCase				)
    snake_case_       :			List[str]   	=  checkpoint_dict["""target"""]

    # Embeddings
    # Position IDs
    snake_case_       :			List[str]   	=  fnet_pretraining_model.state_dict()

    snake_case_       :			int   	=  state_dict["""fnet.embeddings.position_ids"""]
    snake_case_       :			Union[str, Any]   	=  {"""fnet.embeddings.position_ids""": position_ids}
    # Embedding Layers
    snake_case_       :			Optional[int]   	=  torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]				)
    snake_case_       :			Optional[int]   	=  torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]				)
    snake_case_       :			str   	=  torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]				)
    snake_case_       :			Tuple   	=  torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]				).T
    snake_case_       :			List[Any]   	=  torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]				)
    snake_case_       :			List[str]   	=  torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]				)
    snake_case_       :			Any   	=  torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]				)

    # Encoder Layers
    for layer in range(config.num_hidden_layers				):
        snake_case_       :			List[str]   	=  torch.tensor(
            pretrained_model_params["encoder"][f'encoder_{layer}']["mixing_layer_norm"]["scale"]				)
        snake_case_       :			str   	=  torch.tensor(
            pretrained_model_params["encoder"][f'encoder_{layer}']["mixing_layer_norm"]["bias"]				)

        snake_case_       :			List[str]   	=  torch.tensor(
            pretrained_model_params["encoder"][f'feed_forward_{layer}']["intermediate"]["kernel"]				).T
        snake_case_       :			Tuple   	=  torch.tensor(
            pretrained_model_params["encoder"][f'feed_forward_{layer}']["intermediate"]["bias"]				)

        snake_case_       :			Dict   	=  torch.tensor(
            pretrained_model_params["encoder"][f'feed_forward_{layer}']["output"]["kernel"]				).T
        snake_case_       :			Union[str, Any]   	=  torch.tensor(
            pretrained_model_params["encoder"][f'feed_forward_{layer}']["output"]["bias"]				)

        snake_case_       :			List[Any]   	=  torch.tensor(
            pretrained_model_params["encoder"][f'encoder_{layer}']["output_layer_norm"]["scale"]				)
        snake_case_       :			List[Any]   	=  torch.tensor(
            pretrained_model_params["encoder"][f'encoder_{layer}']["output_layer_norm"]["bias"]				)

    # Pooler Layers
    snake_case_       :			Optional[int]   	=  torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]				).T
    snake_case_       :			str   	=  torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]				)

    # Masked LM Layers
    snake_case_       :			Tuple   	=  torch.tensor(
        pretrained_model_params["predictions_dense"]["kernel"]				).T
    snake_case_       :			Union[str, Any]   	=  torch.tensor(
        pretrained_model_params["predictions_dense"]["bias"]				)
    snake_case_       :			int   	=  torch.tensor(
        pretrained_model_params["predictions_layer_norm"]["scale"]				)
    snake_case_       :			Any   	=  torch.tensor(
        pretrained_model_params["predictions_layer_norm"]["bias"]				)
    snake_case_       :			Union[str, Any]   	=  torch.tensor(
        pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]				)
    snake_case_       :			int   	=  torch.tensor(
        pretrained_model_params["predictions_output"]["output_bias"]				)
    snake_case_       :			Optional[int]   	=  torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]				)

    # Seq Relationship Layers
    snake_case_       :			Optional[int]   	=  torch.tensor(
        pretrained_model_params["classification"]["output_kernel"]				)
    snake_case_       :			Tuple   	=  torch.tensor(
        pretrained_model_params["classification"]["output_bias"]				)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(__UpperCAmelCase				)

    # Save PreTrained
    print(f'Saving pretrained model to {save_path}'				)
    fnet_pretraining_model.save_pretrained(__UpperCAmelCase				)


if __name__ == "__main__":
  a_      =  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--flax_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--fnet_config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained FNet model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument("--save_path", default=None, type=str, required=True, help="Path to the output model.")
  a_      =  parser.parse_args()
  convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)









"""simple docstring"""







from typing import TYPE_CHECKING

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


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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase:   List[Any]       			=							[
		    """Constraint""",
		    """ConstraintListState""",
		    """DisjunctiveConstraint""",
		    """PhrasalConstraint""",
		]
		UpperCAmelCase:   Optional[Any]       			=							[
		    """BeamHypotheses""",
		    """BeamScorer""",
		    """BeamSearchScorer""",
		    """ConstrainedBeamSearchScorer""",
		]
		UpperCAmelCase:   Any       			=							[
		    """EpsilonLogitsWarper""",
		    """EtaLogitsWarper""",
		    """ForcedBOSTokenLogitsProcessor""",
		    """ForcedEOSTokenLogitsProcessor""",
		    """HammingDiversityLogitsProcessor""",
		    """InfNanRemoveLogitsProcessor""",
		    """LogitsProcessor""",
		    """LogitsProcessorList""",
		    """LogitsWarper""",
		    """MinLengthLogitsProcessor""",
		    """MinNewTokensLengthLogitsProcessor""",
		    """NoBadWordsLogitsProcessor""",
		    """NoRepeatNGramLogitsProcessor""",
		    """PrefixConstrainedLogitsProcessor""",
		    """RepetitionPenaltyLogitsProcessor""",
		    """SequenceBiasLogitsProcessor""",
		    """EncoderRepetitionPenaltyLogitsProcessor""",
		    """TemperatureLogitsWarper""",
		    """TopKLogitsWarper""",
		    """TopPLogitsWarper""",
		    """TypicalLogitsWarper""",
		    """EncoderNoRepeatNGramLogitsProcessor""",
		    """ExponentialDecayLengthPenalty""",
		    """LogitNormalization""",
		]
		UpperCAmelCase:   str       			=							[
		    """MaxNewTokensCriteria""",
		    """MaxLengthCriteria""",
		    """MaxTimeCriteria""",
		    """StoppingCriteria""",
		    """StoppingCriteriaList""",
		    """validate_stopping_criteria""",
		]
		UpperCAmelCase:   Optional[Any]       			=							[
		    """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:
		UpperCAmelCase:   Any       			=							[
		    """TFForcedBOSTokenLogitsProcessor""",
		    """TFForcedEOSTokenLogitsProcessor""",
		    """TFLogitsProcessor""",
		    """TFLogitsProcessorList""",
		    """TFLogitsWarper""",
		    """TFMinLengthLogitsProcessor""",
		    """TFNoBadWordsLogitsProcessor""",
		    """TFNoRepeatNGramLogitsProcessor""",
		    """TFRepetitionPenaltyLogitsProcessor""",
		    """TFTemperatureLogitsWarper""",
		    """TFTopKLogitsWarper""",
		    """TFTopPLogitsWarper""",
		    """TFForceTokensLogitsProcessor""",
		    """TFSuppressTokensAtBeginLogitsProcessor""",
		    """TFSuppressTokensLogitsProcessor""",
		]
		UpperCAmelCase:   Tuple       			=							[
		    """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:
		UpperCAmelCase:   Tuple       			=							[
		    """FlaxForcedBOSTokenLogitsProcessor""",
		    """FlaxForcedEOSTokenLogitsProcessor""",
		    """FlaxLogitsProcessor""",
		    """FlaxLogitsProcessorList""",
		    """FlaxLogitsWarper""",
		    """FlaxMinLengthLogitsProcessor""",
		    """FlaxTemperatureLogitsWarper""",
		    """FlaxTopKLogitsWarper""",
		    """FlaxTopPLogitsWarper""",
		]
		UpperCAmelCase:   int       			=							[
		    """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

		UpperCAmelCase:   Optional[Any]       			=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



