






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						Dict:





						"""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 __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						List[str]:





						"""simple docstring"""



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


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						int:





						"""simple docstring"""



						_UpperCAmelCase             =    pt_linear.weight
						_UpperCAmelCase             =    pt_linear.bias


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						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 __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						str:





						"""simple docstring"""



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


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						Optional[int]:





						"""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 __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						List[str]:





						"""simple docstring"""



						_UpperCAmelCase             =    pt_model.text_projection.data.T

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


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						Optional[int]:





						"""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 __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase=None			)->						Optional[Any]:





						"""simple docstring"""



						if config_path is not None:
												_UpperCAmelCase             =    CLIPConfig.from_pretrained(__lowerCAmelCase			)
						else:
												_UpperCAmelCase             =    CLIPConfig(projection_dim=512				,	text_config={}				,	vision_config={}			)

						_UpperCAmelCase             =    CLIPModel(__lowerCAmelCase			).eval()

						_UpperCAmelCase      ,				_UpperCAmelCase             =    load(__lowerCAmelCase				,	device='cpu'				,	jit=__lowerCAmelCase			)
						_UpperCAmelCase             =    pt_model.eval()

						copy_text_model_and_projection(__lowerCAmelCase				,	__lowerCAmelCase			)
						copy_vison_model_and_projection(__lowerCAmelCase				,	__lowerCAmelCase			)
						_UpperCAmelCase             =    pt_model.logit_scale

						_UpperCAmelCase             =    torch.arange(0				,	77			).unsqueeze(0			)
						_UpperCAmelCase             =    torch.randn(1				,	3				,	224				,	224			)

						_UpperCAmelCase             =    hf_model(input_ids=__lowerCAmelCase				,	pixel_values=__lowerCAmelCase				,	return_dict=__lowerCAmelCase			)
						_UpperCAmelCase             =    hf_outputs.logits_per_image
						_UpperCAmelCase             =    hf_outputs.logits_per_text
						_UpperCAmelCase      ,				_UpperCAmelCase             =    pt_model(__lowerCAmelCase				,	__lowerCAmelCase			)

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

						hf_model.save_pretrained(__lowerCAmelCase			)


if __name__ == "__main__":
		_a   			=						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''')
		_a   			=						parser.parse_args()

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








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_a   			=						logging.get_logger(__name__)

_a   			=						{
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	UpperCamelCase__			=   "m2m_100"
	UpperCamelCase__			=   ["past_key_values"]
	UpperCamelCase__			=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

	def __init__(							self ,					UpperCAmelCase=12_8112 ,					UpperCAmelCase=1024 ,					UpperCAmelCase=12 ,					UpperCAmelCase=4096 ,					UpperCAmelCase=16 ,					UpperCAmelCase=12 ,					UpperCAmelCase=4096 ,					UpperCAmelCase=16 ,					UpperCAmelCase=0.05 ,					UpperCAmelCase=0.05 ,					UpperCAmelCase=True ,					UpperCAmelCase=True ,					UpperCAmelCase="relu" ,					UpperCAmelCase=1024 ,					UpperCAmelCase=0.1 ,					UpperCAmelCase=0.1 ,					UpperCAmelCase=0.0 ,					UpperCAmelCase=0.02 ,					UpperCAmelCase=2 ,					UpperCAmelCase=True ,					UpperCAmelCase=1 ,					UpperCAmelCase=0 ,					UpperCAmelCase=2 ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    vocab_size
							_UpperCAmelCase             =    max_position_embeddings
							_UpperCAmelCase             =    d_model
							_UpperCAmelCase             =    encoder_ffn_dim
							_UpperCAmelCase             =    encoder_layers
							_UpperCAmelCase             =    encoder_attention_heads
							_UpperCAmelCase             =    decoder_ffn_dim
							_UpperCAmelCase             =    decoder_layers
							_UpperCAmelCase             =    decoder_attention_heads
							_UpperCAmelCase             =    dropout
							_UpperCAmelCase             =    attention_dropout
							_UpperCAmelCase             =    activation_dropout
							_UpperCAmelCase             =    activation_function
							_UpperCAmelCase             =    init_std
							_UpperCAmelCase             =    encoder_layerdrop
							_UpperCAmelCase             =    decoder_layerdrop
							_UpperCAmelCase             =    use_cache
							_UpperCAmelCase             =    encoder_layers
							_UpperCAmelCase             =    scale_embedding  # scale factor will be sqrt(d_model) if True

							super().__init__(
							    pad_token_id=UpperCAmelCase ,					bos_token_id=UpperCAmelCase ,					eos_token_id=UpperCAmelCase ,					is_encoder_decoder=UpperCAmelCase ,					decoder_start_token_id=UpperCAmelCase ,					**UpperCAmelCase ,					)





class __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	@property
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    OrderedDict(
							    [
							        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
							        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
							    ]					)

							if self.use_past:
													_UpperCAmelCase             =    {0: 'batch'}
													_UpperCAmelCase             =    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
													_UpperCAmelCase             =    {0: 'batch', 1: 'decoder_sequence'}
													_UpperCAmelCase             =    {0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
													self.fill_with_past_key_values_(UpperCAmelCase ,					direction='inputs'					)
							return common_inputs

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase = -1 ,					UpperCAmelCase = -1 ,					UpperCAmelCase = False ,					UpperCAmelCase = None ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    compute_effective_axis_dimension(
							    UpperCAmelCase ,					fixed_dimension=OnnxConfig.default_fixed_batch ,					num_token_to_add=0					)

							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							_UpperCAmelCase             =    tokenizer.num_special_tokens_to_add(UpperCAmelCase					)
							_UpperCAmelCase             =    compute_effective_axis_dimension(
							    UpperCAmelCase ,					fixed_dimension=OnnxConfig.default_fixed_sequence ,					num_token_to_add=UpperCAmelCase					)

							# Generate dummy inputs according to compute batch and sequence
							_UpperCAmelCase             =    [' '.join([tokenizer.unk_token]					) * seq_length] * batch_size
							_UpperCAmelCase             =    dict(tokenizer(UpperCAmelCase ,					return_tensors=UpperCAmelCase					)					)
							return common_inputs

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase = -1 ,					UpperCAmelCase = -1 ,					UpperCAmelCase = False ,					UpperCAmelCase = None ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					)

							# Generate decoder inputs
							_UpperCAmelCase             =    seq_length if not self.use_past else 1
							_UpperCAmelCase             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					)
							_UpperCAmelCase             =    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
							_UpperCAmelCase             =    dict(**UpperCAmelCase ,					**UpperCAmelCase					)

							if self.use_past:
													if not is_torch_available():
																			raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
													else:
																			import torch
													_UpperCAmelCase      ,				_UpperCAmelCase             =    common_inputs['input_ids'].shape
													_UpperCAmelCase             =    common_inputs['decoder_input_ids'].shape[1]
													_UpperCAmelCase      ,				_UpperCAmelCase             =    self.num_attention_heads
													_UpperCAmelCase             =    (
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													_UpperCAmelCase             =    decoder_seq_length + 3
													_UpperCAmelCase             =    (
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													_UpperCAmelCase             =    torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase ,					UpperCAmelCase					)] ,					dim=1					)

													_UpperCAmelCase             =    []
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													_UpperCAmelCase      ,				_UpperCAmelCase             =    self.num_layers
													_UpperCAmelCase             =    min(UpperCAmelCase ,					UpperCAmelCase					)
													_UpperCAmelCase             =    max(UpperCAmelCase ,					UpperCAmelCase					) - min_num_layers
													_UpperCAmelCase             =    'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

													for _ in range(UpperCAmelCase					):
																			common_inputs["past_key_values"].append(
																			    (
																			        torch.zeros(UpperCAmelCase					),
																			        torch.zeros(UpperCAmelCase					),
																			        torch.zeros(UpperCAmelCase					),
																			        torch.zeros(UpperCAmelCase					),
																			    )					)
													# TODO: test this.
													_UpperCAmelCase             =    encoder_shape if remaining_side_name == 'encoder' else decoder_shape
													for _ in range(UpperCAmelCase ,					UpperCAmelCase					):
																			common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase					), torch.zeros(UpperCAmelCase					))					)
							return common_inputs

	UpperCamelCase__			=   _generate_dummy_inputs_for_default_and_seqaseq_lm
