

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_						=					{
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

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

					SCREAMING_SNAKE_CASE_						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_weight.chunk(3       ,		dim=0       )
    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_bias.chunk(3       ,		dim=0       )

    UpperCamelCase_          =		pt_attn_layer.out_proj.weight
    UpperCamelCase_          =		pt_attn_layer.out_proj.bias

    UpperCamelCase_          =		q_proj
    UpperCamelCase_          =		q_proj_bias

    UpperCamelCase_          =		k_proj
    UpperCamelCase_          =		k_proj_bias

    UpperCamelCase_          =		v_proj
    UpperCamelCase_          =		v_proj_bias

    UpperCamelCase_          =		out_proj_weights
    UpperCamelCase_          =		out_proj_bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Union[str, Any]:



    """simple docstring"""






    copy_linear(hf_mlp.fca       ,		pt_mlp.c_fc       )
    copy_linear(hf_mlp.fca       ,		pt_mlp.c_proj       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_linear.weight
    UpperCamelCase_          =		pt_linear.bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    copy_linear(hf_layer.layer_norma       ,		pt_layer.ln_a       )
    copy_linear(hf_layer.layer_norma       ,		pt_layer.ln_a       )

    # copy MLP
    copy_mlp(hf_layer.mlp       ,		pt_layer.mlp       )

    # copy attn
    copy_attn_layer(hf_layer.self_attn       ,		pt_layer.attn       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        copy_layer(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.token_embedding.weight
    UpperCamelCase_          =		pt_model.positional_embedding

    # copy layer norm
    copy_linear(hf_encoder.final_layer_norm       ,		pt_model.ln_final       )

    # copy hidden layers
    copy_layers(hf_encoder.encoder.layers       ,		pt_model.transformer.resblocks       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.text_projection.data.T

    # copy text encoder
    copy_encoder(hf_model.text_model       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.visual.proj.data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layrnorm       ,		pt_model.visual.ln_pre       )
    copy_linear(hf_model.vision_model.post_layernorm       ,		pt_model.visual.ln_post       )

    # copy embeds
    UpperCamelCase_          =		pt_model.visual.conva.weight.data
    UpperCamelCase_          =		pt_model.visual.class_embedding
    UpperCamelCase_          =		pt_model.visual.positional_embedding.data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers       ,		pt_model.visual.transformer.resblocks       )




@torch.no_grad()
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None       )->		List[Any]:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_          =		CLIPConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
    else:
        UpperCamelCase_          =		CLIPConfig(projection_dim=5_1_2       ,		text_config={}       ,		vision_config={}       )

    UpperCamelCase_          =		CLIPModel(SCREAMING_SNAKE_CASE_       ).eval()

    UpperCamelCase_   ,    UpperCamelCase_          =		load(SCREAMING_SNAKE_CASE_       ,		device="cpu"       ,		jit=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.eval()

    copy_text_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    copy_vison_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.logit_scale

    UpperCamelCase_          =		torch.arange(0       ,		7_7       ).unsqueeze(0       )
    UpperCamelCase_          =		torch.randn(1       ,		3       ,		2_2_4       ,		2_2_4       )

    UpperCamelCase_          =		hf_model(input_ids=SCREAMING_SNAKE_CASE_       ,		pixel_values=SCREAMING_SNAKE_CASE_       ,		return_dict=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		hf_outputs.logits_per_image
    UpperCamelCase_          =		hf_outputs.logits_per_text
    UpperCamelCase_   ,    UpperCamelCase_          =		pt_model(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

    assert torch.allclose(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		atol=1E-3       )
    assert torch.allclose(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		atol=1E-3       )

    hf_model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:Any      =			argparse.ArgumentParser()
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 SCREAMING_SNAKE_CASE						:Dict      =			parser.parse_args()

 convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
