



import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp

def       A_   (						__a  :					str			,		__a  :					int			,		__a  :					List[Any]			,		__a  :					Dict			,		__a  :					int = 4_096		):

				"""simple docstring"""
				a__   ,      a__   ,      a__					   =		key.shape[-3:]
				a__					   =		value.shape[-1]
				a__					   =		min(__a			,		__a		)
				a__					   =		query / jnp.sqrt(__a		)

				@functools.partial(jax.checkpoint			,		prevent_cse=__a		)
				def summarize_chunk(__a  :					Optional[Any]			,		__a  :					Tuple			,		__a  :					Dict		):
								a__					   =		jnp.einsum("""...qhd,...khd->...qhk"""			,		__a			,		__a			,		precision=__a		)

								a__					   =		jnp.max(__a			,		axis=-1			,		keepdims=__a		)
								a__					   =		jax.lax.stop_gradient(__a		)
								a__					   =		jnp.exp(attn_weights - max_score		)

								a__					   =		jnp.einsum("""...vhf,...qhv->...qhf"""			,		__a			,		__a			,		precision=__a		)
								a__					   =		jnp.einsum("""...qhk->...qh"""			,		__a		)

								return (exp_values, exp_weights.sum(axis=-1		), max_score)

				def chunk_scanner(__a  :					Tuple		):
								# julienne key array
								a__					   =		jax.lax.dynamic_slice(
								    operand=__a			,		start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]			,		slice_sizes=list(key.shape[:-3]		) + [key_chunk_size, num_heads, k_features]			,		)

								# julienne value array
								a__					   =		jax.lax.dynamic_slice(
								    operand=__a			,		start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]			,		slice_sizes=list(value.shape[:-3]		) + [key_chunk_size, num_heads, v_features]			,		)

								return summarize_chunk(__a			,		__a			,		__a		)

				a__   ,      a__   ,      a__					   =		jax.lax.map(f=__a			,		xs=jnp.arange(0			,		__a			,		__a		)		)

				a__					   =		jnp.max(__a			,		axis=0			,		keepdims=__a		)
				a__					   =		jnp.exp(chunk_max - global_max		)

				chunk_values *= jnp.expand_dims(__a			,		axis=-1		)
				chunk_weights *= max_diffs

				a__					   =		chunk_values.sum(axis=0		)
				a__					   =		jnp.expand_dims(__a			,		-1		).sum(axis=0		)

				return all_values / all_weights

def       A_   (						__a  :					Tuple			,		__a  :					List[Any]			,		__a  :					int			,		__a  :					List[str]=jax.lax.Precision.HIGHEST			,		__a  :					int = 1_024			,		__a  :					int = 4_096		):

				"""simple docstring"""
				a__   ,      a__   ,      a__					   =		query.shape[-3:]

				def chunk_scanner(__a  :					List[str]			,		__a  :					List[str]		):
								# julienne query array
								a__					   =		jax.lax.dynamic_slice(
								    operand=__a			,		start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]			,		slice_sizes=list(query.shape[:-3]		) + [min(__a			,		__a		), num_heads, q_features]			,		)

								return (
								    chunk_idx + query_chunk_size,  # unused ignore it
								    _query_chunk_attention(
								        query=__a			,		key=__a			,		value=__a			,		precision=__a			,		key_chunk_size=__a		),
								)

				a__   ,      a__					   =		jax.lax.scan(
				    f=__a			,		init=0			,		xs=__a			,		length=math.ceil(num_q / query_chunk_size		)		)

				return jnp.concatenate(__a			,		axis=-3		)  # fuse the chunked result back

class 			__snake_case   ( nn.Module):







							'''simple docstring'''






							UpperCamelCase__					:   int
							UpperCamelCase__					:   int       =       8
							UpperCamelCase__					:   int       =       6_4
							UpperCamelCase__					:   float       =       0.0
							UpperCamelCase__					:   bool       =       False
							UpperCamelCase__					:   jnp.dtype       =       jnp.floataa


							def _a   (				self		):
											a__					   =		self.dim_head * self.heads
											a__					   =		self.dim_head**-0.5

											# Weights were exported with old names {to_q, to_k, to_v, to_out}
											a__					   =		nn.Dense(a_	,	use_bias=a_	,	dtype=self.dtype	,	name="""to_q"""		)
											a__					   =		nn.Dense(a_	,	use_bias=a_	,	dtype=self.dtype	,	name="""to_k"""		)
											a__					   =		nn.Dense(a_	,	use_bias=a_	,	dtype=self.dtype	,	name="""to_v"""		)

											a__					   =		nn.Dense(self.query_dim	,	dtype=self.dtype	,	name="""to_out_0"""		)
											a__					   =		nn.Dropout(rate=self.dropout		)


							def _a   (				self	,	a_		):
											a__   ,      a__   ,      a__					   =		tensor.shape
											a__					   =		self.heads
											a__					   =		tensor.reshape(a_	,	a_	,	a_	,	dim // head_size		)
											a__					   =		jnp.transpose(a_	,	(0, 2, 1, 3)		)
											a__					   =		tensor.reshape(batch_size * head_size	,	a_	,	dim // head_size		)
											return tensor


							def _a   (				self	,	a_		):
											a__   ,      a__   ,      a__					   =		tensor.shape
											a__					   =		self.heads
											a__					   =		tensor.reshape(batch_size // head_size	,	a_	,	a_	,	a_		)
											a__					   =		jnp.transpose(a_	,	(0, 2, 1, 3)		)
											a__					   =		tensor.reshape(batch_size // head_size	,	a_	,	dim * head_size		)
											return tensor







							def __call__(				self	,	a_	,	a_=None	,	a_=True		):
											a__					   =		hidden_states if context is None else context

											a__					   =		self.query(a_		)
											a__					   =		self.key(a_		)
											a__					   =		self.value(a_		)

											a__					   =		self.reshape_heads_to_batch_dim(a_		)
											a__					   =		self.reshape_heads_to_batch_dim(a_		)
											a__					   =		self.reshape_heads_to_batch_dim(a_		)

											if self.use_memory_efficient_attention:
															a__					   =		query_states.transpose(1	,	0	,	2		)
															a__					   =		key_states.transpose(1	,	0	,	2		)
															a__					   =		value_states.transpose(1	,	0	,	2		)

															# this if statement create a chunk size for each layer of the unet
															# the chunk size is equal to the query_length dimension of the deepest layer of the unet

															a__					   =		query_states.shape[-3]
															if flatten_latent_dim % 64 == 0:
																			a__					   =		int(flatten_latent_dim / 64		)
															elif flatten_latent_dim % 16 == 0:
																			a__					   =		int(flatten_latent_dim / 16		)
															elif flatten_latent_dim % 4 == 0:
																			a__					   =		int(flatten_latent_dim / 4		)
															else:
																			a__					   =		int(a_		)

															a__					   =		jax_memory_efficient_attention(
															    a_	,	a_	,	a_	,	query_chunk_size=a_	,	key_chunk_size=4_096 * 4		)

															a__					   =		hidden_states.transpose(1	,	0	,	2		)
											else:
															# compute attentions
															a__					   =		jnp.einsum("""b i d, b j d->b i j"""	,	a_	,	a_		)
															a__					   =		attention_scores * self.scale
															a__					   =		nn.softmax(a_	,	axis=2		)

															# attend to values
															a__					   =		jnp.einsum("""b i j, b j d -> b i d"""	,	a_	,	a_		)

											a__					   =		self.reshape_batch_dim_to_heads(a_		)
											a__					   =		self.proj_attn(a_		)
											return self.dropout_layer(a_	,	deterministic=a_		)




class 			__snake_case   ( nn.Module):







							'''simple docstring'''






							UpperCamelCase__					:   int
							UpperCamelCase__					:   int
							UpperCamelCase__					:   int
							UpperCamelCase__					:   float       =       0.0
							UpperCamelCase__					:   bool       =       False
							UpperCamelCase__					:   jnp.dtype       =       jnp.floataa
							UpperCamelCase__					:   bool       =       False


							def _a   (				self		):
											# self attention (or cross_attention if only_cross_attention is True)
											a__					   =		FlaxAttention(
											    self.dim	,	self.n_heads	,	self.d_head	,	self.dropout	,	self.use_memory_efficient_attention	,	dtype=self.dtype		)
											# cross attention
											a__					   =		FlaxAttention(
											    self.dim	,	self.n_heads	,	self.d_head	,	self.dropout	,	self.use_memory_efficient_attention	,	dtype=self.dtype		)
											a__					   =		FlaxFeedForward(dim=self.dim	,	dropout=self.dropout	,	dtype=self.dtype		)
											a__					   =		nn.LayerNorm(epsilon=1E-5	,	dtype=self.dtype		)
											a__					   =		nn.LayerNorm(epsilon=1E-5	,	dtype=self.dtype		)
											a__					   =		nn.LayerNorm(epsilon=1E-5	,	dtype=self.dtype		)
											a__					   =		nn.Dropout(rate=self.dropout		)







							def __call__(				self	,	a_	,	a_	,	a_=True		):
											# self attention
											a__					   =		hidden_states
											if self.only_cross_attention:
															a__					   =		self.attna(self.norma(a_		)	,	a_	,	deterministic=a_		)
											else:
															a__					   =		self.attna(self.norma(a_		)	,	deterministic=a_		)
											a__					   =		hidden_states + residual

											# cross attention
											a__					   =		hidden_states
											a__					   =		self.attna(self.norma(a_		)	,	a_	,	deterministic=a_		)
											a__					   =		hidden_states + residual

											# feed forward
											a__					   =		hidden_states
											a__					   =		self.ff(self.norma(a_		)	,	deterministic=a_		)
											a__					   =		hidden_states + residual

											return self.dropout_layer(a_	,	deterministic=a_		)




class 			__snake_case   ( nn.Module):







							'''simple docstring'''






							UpperCamelCase__					:   int
							UpperCamelCase__					:   int
							UpperCamelCase__					:   int
							UpperCamelCase__					:   int       =       1
							UpperCamelCase__					:   float       =       0.0
							UpperCamelCase__					:   bool       =       False
							UpperCamelCase__					:   bool       =       False
							UpperCamelCase__					:   jnp.dtype       =       jnp.floataa
							UpperCamelCase__					:   bool       =       False


							def _a   (				self		):
											a__					   =		nn.GroupNorm(num_groups=32	,	epsilon=1E-5		)

											a__					   =		self.n_heads * self.d_head
											if self.use_linear_projection:
															a__					   =		nn.Dense(a_	,	dtype=self.dtype		)
											else:
															a__					   =		nn.Conv(
															    a_	,	kernel_size=(1, 1)	,	strides=(1, 1)	,	padding="""VALID"""	,	dtype=self.dtype	,	)

											a__					   =		[
											    FlaxBasicTransformerBlock(
											        a_	,	self.n_heads	,	self.d_head	,	dropout=self.dropout	,	only_cross_attention=self.only_cross_attention	,	dtype=self.dtype	,	use_memory_efficient_attention=self.use_memory_efficient_attention	,	)
											    for _ in range(self.depth		)
											]

											if self.use_linear_projection:
															a__					   =		nn.Dense(a_	,	dtype=self.dtype		)
											else:
															a__					   =		nn.Conv(
															    a_	,	kernel_size=(1, 1)	,	strides=(1, 1)	,	padding="""VALID"""	,	dtype=self.dtype	,	)

											a__					   =		nn.Dropout(rate=self.dropout		)







							def __call__(				self	,	a_	,	a_	,	a_=True		):
											a__   ,      a__   ,      a__   ,      a__					   =		hidden_states.shape
											a__					   =		hidden_states
											a__					   =		self.norm(a_		)
											if self.use_linear_projection:
															a__					   =		hidden_states.reshape(a_	,	height * width	,	a_		)
															a__					   =		self.proj_in(a_		)
											else:
															a__					   =		self.proj_in(a_		)
															a__					   =		hidden_states.reshape(a_	,	height * width	,	a_		)

											for transformer_block in self.transformer_blocks:
															a__					   =		transformer_block(a_	,	a_	,	deterministic=a_		)

											if self.use_linear_projection:
															a__					   =		self.proj_out(a_		)
															a__					   =		hidden_states.reshape(a_	,	a_	,	a_	,	a_		)
											else:
															a__					   =		hidden_states.reshape(a_	,	a_	,	a_	,	a_		)
															a__					   =		self.proj_out(a_		)

											a__					   =		hidden_states + residual
											return self.dropout_layer(a_	,	deterministic=a_		)




class 			__snake_case   ( nn.Module):







							'''simple docstring'''






							UpperCamelCase__					:   int
							UpperCamelCase__					:   float       =       0.0
							UpperCamelCase__					:   jnp.dtype       =       jnp.floataa


							def _a   (				self		):
											# The second linear layer needs to be called
											# net_2 for now to match the index of the Sequential layer
											a__					   =		FlaxGEGLU(self.dim	,	self.dropout	,	self.dtype		)
											a__					   =		nn.Dense(self.dim	,	dtype=self.dtype		)







							def __call__(				self	,	a_	,	a_=True		):
											a__					   =		self.net_a(a_	,	deterministic=a_		)
											a__					   =		self.net_a(a_		)
											return hidden_states




class 			__snake_case   ( nn.Module):







							'''simple docstring'''






							UpperCamelCase__					:   int
							UpperCamelCase__					:   float       =       0.0
							UpperCamelCase__					:   jnp.dtype       =       jnp.floataa


							def _a   (				self		):
											a__					   =		self.dim * 4
											a__					   =		nn.Dense(inner_dim * 2	,	dtype=self.dtype		)
											a__					   =		nn.Dropout(rate=self.dropout		)







							def __call__(				self	,	a_	,	a_=True		):
											a__					   =		self.proj(a_		)
											a__   ,      a__					   =		jnp.split(a_	,	2	,	axis=2		)
											return self.dropout_layer(hidden_linear * nn.gelu(a_		)	,	deterministic=a_		)






import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
	raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       """Hello world! cécé herlolip"""

def       A_   (						__a  :					str			,		__a  :					str			,		__a  :					bool		):

				"""simple docstring"""
				a__   ,      a__					   =		os.path.split(__a		)
				a__					   =		DataaVecTextModel.from_pretrained(
				    __a			,		checkpoint_file=__a		)
				dataavec.eval()  # disable dropout
				a__					   =		dataavec.models[0]
				a__					   =		dataavec_model.encoder.sentence_encoder
				a__					   =		DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings			,		hidden_size=dataavec_model.args.encoder_embed_dim			,		num_hidden_layers=dataavec_model.args.encoder_layers			,		num_attention_heads=dataavec_model.args.encoder_attention_heads			,		intermediate_size=dataavec_model.args.encoder_ffn_embed_dim			,		max_position_embeddings=514			,		type_vocab_size=1			,		layer_norm_eps=1e-5			,		)
				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
				print("""Our BERT config:"""			,		__a		)

				a__					   =		DataaVecTextForSequenceClassification(__a		) if classification_head else DataaVecTextForMaskedLM(__a		)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				a__					   =		dataavec_sent_encoder.embed_tokens.weight
				a__					   =		dataavec_sent_encoder.embed_positions.weight
				a__					   =		torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
				a__					   =		dataavec_sent_encoder.layernorm_embedding.weight
				a__					   =		dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers		):
								# Encoder: start of layer
								a__					   =		model.dataavec_text.encoder.layer[i]
								a__					   =		dataavec_sent_encoder.layers[i]

								# self attention
								a__					   =		layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)

								a__					   =		dataavec_layer.self_attn.q_proj.weight
								a__					   =		dataavec_layer.self_attn.q_proj.bias
								a__					   =		dataavec_layer.self_attn.k_proj.weight
								a__					   =		dataavec_layer.self_attn.k_proj.bias
								a__					   =		dataavec_layer.self_attn.v_proj.weight
								a__					   =		dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								a__					   =		layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
								a__					   =		dataavec_layer.self_attn.out_proj.weight
								a__					   =		dataavec_layer.self_attn.out_proj.bias
								a__					   =		dataavec_layer.self_attn_layer_norm.weight
								a__					   =		dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								a__					   =		layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								a__					   =		dataavec_layer.fca.weight
								a__					   =		dataavec_layer.fca.bias

								# output
								a__					   =		layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								a__					   =		dataavec_layer.fca.weight
								a__					   =		dataavec_layer.fca.bias
								a__					   =		dataavec_layer.final_layer_norm.weight
								a__					   =		dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""].dense.weight
								a__					   =		dataavec.model.classification_heads["""mnli"""].dense.bias
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.weight
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.bias
				else:
								# LM Head
								a__					   =		dataavec_model.encoder.lm_head.dense.weight
								a__					   =		dataavec_model.encoder.lm_head.dense.bias
								a__					   =		dataavec_model.encoder.lm_head.layer_norm.weight
								a__					   =		dataavec_model.encoder.lm_head.layer_norm.bias
								a__					   =		dataavec_model.encoder.lm_head.weight
								a__					   =		dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				a__					   =		dataavec.encode(__a		).unsqueeze(0		)  # batch of size 1

				a__					   =		model(__a		)[0]
				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__a		)		)
				else:
								a__					   =		dataavec_model(__a		)[0]
				print(our_output.shape			,		their_output.shape		)
				a__					   =		torch.max(torch.abs(our_output - their_output		)		).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
				a__					   =		torch.allclose(__a			,		__a			,		atol=1e-3		)
				print("""Do both models output the same tensors?"""			,		"""🔥""" if success else """💩"""		)
				if not success:
								raise Exception("""Something went wRoNg"""		)

				pathlib.Path(__a		).mkdir(parents=__a			,		exist_ok=__a		)
				print(F'''Saving model to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(__a		)


if __name__ == "__main__":
	UpperCAmelCase							=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_dataavec_checkpoint_to_pytorch(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)

